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In this week’s In-Ear Insights, Katie and Chris tackle an unusual situation. What happens when data doesn’t have answers? What happens when data simply doesn’t provide any useful insights beyond confirmation of what you already knew? We review what your options are, and what might have gone wrong that led to the situation. If you’ve ever been stuck in this situation, listen to this episode for ways to think about solving this problem. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-nodatafordecisions.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights, the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses. To start you on your AI journey, including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes, visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people, our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more, again, that’s Trust insights.ai slash contact. In this week’s in ear insights we are talking about what to do when your data doesn’t tell you what to do. Many times we talked about how you have to be using data to make decisions. And if you’re not making data decisions with your data, it really is kind of a distraction. We want to focus on what actions can we take but sometimes, especially with some clients that have very, very steady even, you know, businesses that have no major disruptions, which is astonishing, you know, in the current pandemic times, you have data says, Yeah, there’s nothing to do except stay the course maybe. So Katie, what do we do when the data doesn’t tell us what to do? Katie Robbert 1:46 We panic and cry under our desks. That’s it? Unknown Speaker 1:50 No, no. Katie Robbert 1:51 Okay. All right, what do we do know. So this is actually a very common issue. So, you know, if you’re looking at your data every day, like in an automated report, like Data Studio, or if you’re looking at it every few weeks, and you’re not seeing a lot of change, that could be the indication of a good thing that things are working. But the risk that you run is that if things completely stay static, or they’re just very slowly growing, or just you know, plateauing, then it’s sort of that like bare minimum of like, okay, so we don’t touch anything, things will just keep rolling along. But couldn’t we be doing better? We ran into this recently, when we were pulling an analysis and a notch in attribution analysis for one of our clients. And, Chris, your first reaction was, myth doesn’t really tell us a whole lot. And what we ended up doing in that instance, was we really, you know, for lack of a better term, we stepped outside of the box of the way that we usually look at these analyses. And we thought, what are some of the questions that we can continue that we can start to ask? So instead of saying, This is what the data says, and this is the action, we took a different tack and said, let’s just start asking questions of, is this the best thing to be doing? Is this the way you want your data to be looking? Are these the results you’re hoping to get? And so instead of just like throwing our hands up and saying, well, this isn’t useful, we really tried to think about it from a different perspective of just questioning everything. What do you think, Chris? I mean, you’ve been doing data longer than I’ve been doing data. What do you what do you do when you run into this? Christopher Penn 3:33 For it depends on whether it’s a strategic or mathematical problem. In the math side of things, one of the things that happens, like when you run, say, a simple correlation, and you get something that doesn’t really provide any illumination, one of the first questions you have to ask is, is there some piece of data that’s missing? Right? Is there some metric or variable that isn’t accounted for that you then have to say, Okay, if we had this, then we might get some kind of relations? Because we know, some we know, we’re trying to find something we know there’s a there there. The question is, is, do we have the appropriate type of data to to make that analysis and there actually are statistical tests you can run on data? is specifically time series data to say, Okay, these are the things that could hint at a problem. There’s techniques like principal component analysis, factor analysis, certain types of regression analysis can hint that Yeah, you might not have all the ingredients that you’re trying to find. But I think for most marketers, it is the strategic problem more than anything, the one that you alluded to, which is if things just kind of plugging along, what’s missing, why aren’t things you know, the typical growth hacker created, why and things just going up into the right, you know, are there channels you’re not considering? Are there tactics you haven’t used? Are there audiences you haven’t reached? And so it’s a question of what is omitted from the analysis, when we do an attribution analysis, for example, there are things that in digital marketing are omitted, right, you cannot see things you don’t have information for. If you’re looking at Google Analytics, for example, one of the channels that shows up all the time is called direct, which is a misnomer, it should say unknown, because Google has literally no idea what to do with that data. It just calls it direct. And sometimes, depending on your website, and your Google Analytics configuration, that can be 20 3050 60% of your data. When 60% of your data is unknown, you kind of have a problem. And so the question then is, what do we do? How do we unpack that box? Because there may be things in there that could drastically change our analysis, maybe email isn’t holding steady, right? Maybe email is actually going up. But because you didn’t use UTM tags and your emails, for example, the you are missing a whole bunch of traffic, we had that problem with a client where email is showing up for like, 2% of their attribution analysis. And they were like, Oh, you know, we, we don’t actually put UTM tags in our email. It’s like, great, but we tried fixing that for a month. And then the next month, we ran the same report, oh, look, 70% of conversions are from email. It turns out, the data was missing. So when we’re talking about is the data, you know, math is the data that tells us what to do. We’ve got to make sure we’ve got all the data. Katie Robbert 6:25 Well, you know, and it’s interesting, because you’re focusing a lot on the quantitative side of the data, which is often what we look at. So we looked at our KPIs, our goals, you know, did we get the traffic did people convert? The other side of it is it’s you can’t know the full story without the qualitative side of it. Why did that happen? Why did you make that decision? Why are you doing things this way? And so really digging into that behavioral side of the data, asking your customers talking with the rest of your team? Why are we doing it this way? asking your, you know, C suite, why are these the goals, just really digging in. So if your data is just flat, if your data is just like, rolling along, there’s no peaks, there’s no valleys, you know, you may say, Oh, this is a great thing, you run the risk of if something changes, all of that disappeared. So you really need to understand why things are the way they are, you know, if your data is flat and plateaued, it’s a great opportunity to run a customer survey or a small focus group, or do some A B testing and say, Is this what we want it to be doing? You know, are we growing fast enough? Are we doing enough? And Chris, you had mentioned, you know, the different digital channels? Like is email doing what we want it to do? Yes, it’s bringing in traffic, but is it bringing in the right traffic? And those are some of the questions that we really decided to dig into when we ran into this recently of like, Yeah, it looks like SEO is doing well. But are you ranking for the right terms? And so it, we needed to do that additional analysis? And, you know, are you too reliant on any one channel? So there’s really a lot of opportunity, when, even if you think your data isn’t telling you anything useful? It’s actually an opportunity to dig in even deeper and see why, what’s going on? Christopher Penn 8:25 Exactly. I was doing some homeschool science with my kids this weekend, what things we were looking at was calculating momentum. momentum in physics is a simple form. That’s mass times velocity. And when we think about momentum in the bigger picture context of, you know, is our marketing growing as our results growing, are they staying flat? Are they declining? That in, in a sense, is momentum? Are we growing? Are we staying flat? Well, you look at your data and the data is kind of flat, that means your momentum is pretty stable. Well, momentum is a formula mass times velocity. In the context of marketing, that means things like your audience, your conversions, your traffic and stuff, and then the speed at which you’re able to get people to do stuff. So even if your conversions or your audience is growing, if you’re slowing down, in your ability to convert people, you will have consistent momentum. So it may be a question of which of those two things is the problem that you’re not growing? And once you can diagnose that, you can say, Okay, what do I have control over? That could increase either mass or velocity to make momentum increase? For a lot of marketers? velocity is easier to fix than mass, right? You may not have a ton of money to throw at ads. You may not have a ton of people on your mailing list to mail appealing, but you absolutely can do things like looking at the most valuable pages on your website and say, How can we tune these pages up? How can we accelerate the process to conversion? How can we remove obstacles From, but what’s getting in people’s way to convert? How could we have a website that doesn’t suck. And that’s how those the easier things for marketers to tinker with to increase velocity. And if you increase velocity while your mass remains the same, your momentum still goes up. Now the best case scenario is you can get both to go up the same time, and then you have acceleration, then you’re talking about force, which is a totally different, totally different, slightly different formula. But from this perspective, if the data is flat, there are these two fundamental reasons why that data could be flat. And you need to diagnose that first. Katie Robbert 10:40 One could argue that, you know, given everything that’s going on this year, the goal was to keep things status quo and keep things as flat as possible. So there’s definitely a good argument for having your data look that way. However, we are now nine months past the start of the pandemic, and you know, 2020 is coming to a close. So then I would also say, Yep, great, you kept everything status quo for the year. Now it’s time to turn those knobs and increase that mass and increase that velocity, and figure out because now everyone, you know, not to undermine it. But everyone has kind of settled into this new normal routine. And so there’s really no excuse at this point, to really sort of say, Okay, I’m just going to throw my hands up and keep everything status quo. Obviously, you know, that doesn’t apply to every industry. But when we’re talking about marketing, especially digital marketing, the internet is more important now than ever, because it’s how people are staying connected. It’s where they spend their time. You know, online, shopping, e commerce, all of those things are peaked at this point, especially now with the holiday season. So to say, Oh, no, we just want to make sure that everything stays flat is actually a really poor excuse. So this is the time to start doing that testing to do that. Additional customer research, you know, to figure out, are we doing the right things? Are we doing things that the right times? And you know, Chris, to your point, you know, tuning up the website? What pages are leading to conversions, what channels? and in what order are assisting to conversions? Are we really meeting our goals? Or are we just complacent? Christopher Penn 12:20 Exactly. And when we think about velocity, it’s emotion, right? It’s, it’s the distance you go over a period of time. To your point, if you can have speed and movement in a direction, until you hit a wall, if, for example, like your Guitar Center, which just declared bankruptcy this morning, they were unable to change directions. So there’s they weren’t going in a certain direction, they had the speed and they hit a wall called the pandemic. The way around that is, agility be able to change directions very, very quickly, depending on what’s happening in the data. And to your point, if you’re not watching the data, all the time, if here’s here’s a good test of your agility. Da, do you have a dashboard of KPIs? That’s number one. Number two, is it the start page in your browser, right? If it’s not the start page in your browser, when you open a new tab, whatever that means that you’re probably not keeping as close an eye on those KPIs as you could be, that doesn’t mean you have to, like live in it all the time. But it should be one of those things that you see every single day like, Oh, that’s what’s going on. You know, for example, I look at the COVID statistics every single day just to see what’s going on. Because I don’t want to be taken by surprise. I look at basic things like Google Analytics traffic, just make sure it’s not zero, because that would be a really nasty surprise. Every time I send an email newsletter, I look at the data to see Okay, did my newsletter perform, as well as has previously could have done a little bit better? Or did it like go down like by half like, Oh, that’s a big unpleasant surprise. And so we have to be very cognizant of keeping an eye on our speed and knowing when to change directions. And to point we’re nine months into this craziness. And there are still new changes every single day, you know, a bunch of restaurants or a bunch of locations have just gone to no more indoor dining because they’ve just issues with contagion. So we have to be really, really agile. And that comes from looking at the data. So if you have not got a dashboard, put together of your KPIs, and yet, making it something that’s in your face every single day. You have some room to to add some paying attention to your data. Katie Robbert 14:35 I would argue so you know, you just mentioned I believe you said the phrase like things are changing every day. I would argue that, yes, things are changing every day. But the outcomes themselves from a marketing perspective, are predictable in the sense of like, if you run you know, three or four scenarios like okay, let’s say you do the marketing for a restaurant chain, and the restaurant chain, you know, no longer offers indoor dining? Well, this is not new, like, this is a scenario that you’ve been through before. So you can already think through how do I, you know, market for now, you know, only outdoor under heater or you know, whatever the scenario is, or just take out or this that the other. And so, you know, with all of this time that we’ve had to see all of the different changes in the industries, you know, obviously, we can’t plan for every single scenario. But you know, the basics, you know, you know, people are stuck at home, people are going to start hoarding weird random items, people are going to be looking for outlets from, you know, the insanity of being of looking at the same four walls with the same four people all the time. And so these are scenarios that you can plan for. And so it’s a great time to test out some of those things within your own marketing so that your data doesn’t stay flat. Because we have a good sense of what, you know, the situation is around us now. There’s obviously there’s those black swans, those unpredictable things, the natural disasters, the, you know, I would argue even the behaviors by people who were in charge, at this point, are pretty predictable, in the sense that you can plan for things not working, or things not getting done or whatever, you know, without turning it into a political thing, whatever the situation might be. There is some predictability to it because it just it’s the same situation over and over and over again, with just a slightly different shade. Christopher Penn 16:41 Yeah, I agree. And given the familiarity of some of these situations, now, we’ve talked about some past episodes. This is an opportunity just to look at what you did say in March or April go, Well, what could we have done better? Right? We panicked, we scrambled? We we got through it? Maybe. But what didn’t we do? Like, ah, if only we had done that, I wish we could have done that. And again, this is where it pays to go back and look at your data from that period. And say, okay, you know, we had a 40% decline in traffic or whatever, what did we do to pull out of that? And what could we have done faster? What, what’s on the list of Oh, if only I had known at that time? Well, now, to your point, we do know. So it’s an opportunity to bring that stuff back that playbook back and say, Okay, what was on our wish list that now we know pretty much what’s going to happen? Katie Robbert 17:33 That’s a really good point. Um, you know, I, I don’t know that enough companies, or enough marketers are going back to that specific point in time to say, what decisions did we make? And what could we have done differently? I know that a lot are looking at what goals did we set for the year? And did we reach them? Did we not? But you really have to factor in that specific point in time? And how much should we panic? How much? How unprepared? Where are we? What decisions did we make? Why did we make those decisions versus different things? Because clearly, we’re not out of the thick of it yet. And therefore, it’s an opportunity to continue to do better and get out of that plateau that you might be in with your data. Christopher Penn 18:17 Yep. So to summarize, if your data doesn’t help you make decisions, it’s probably because something is missing, either mathematically or strategically. to figure that out, use the basic formula for momentum, mass times velocity, what stuff do you have budget resources, people, etc? And how quickly can you get your audience to move to change directions to to move with you, if you are stuck, it means one of those two things is probably not working and figure out which one’s not working. And start making changes as quickly as you can to get your momentum back on track and start getting your data moving in the right direction. If you got questions about this, or anything else that we’ve talked about, on today’s show, hop on over to our free slack group analytics from Marcus Trust insights.ai slash analytics for marketers where you can chat with up to 1400, other digital marketers every day about all your analytics and data questions. And as always, if you haven’t done this already, please go over to Trust insights.ai slash t AI podcast and subscribe to the show. Wherever it is, you’re listening to it. And we’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this week’s In-Ear Insights, Katie and Chris discuss upcoming 2021 marketing trends. It’s that time of year when everyone’s pontificating on what’s next in marketing. Listen in as we discuss: – Analytics infrastructure – Agile marketing – Private social media communities – Customer data privacy and sales – Value exchange Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-2021trends.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses to start you on your AI journey including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes. Visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people. Our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more. Again, that’s Trust insights.ai slash contact. In this week’s in In-Ear Insights it’s the most wonderful time of the year Katie? Actually, it’s not it’s the time of year when everybody in their cousin is sending us all pitches to podcasts or blogs to YouTube’s saying, hey, person, what are your 2021 predictions, advice, forecast all this stuff about you know, marketing and analytics and AI and machine learning and all this stuff. So I figured why don’t we talk about our 2021 prognostications with our best understanding of the landscape as it stands right now. And that way, when people ask us for our predictions, we have this wonderful episode of the podcast to direct them to being efficient. So Katie, what are your 2021 predictions? Katie Robbert 1:52 More dumpster fires and chaos? I think, you know, all joking aside, obviously, everybody knows that 2020 has been a very different kind of year. And something Chris, you and I have talked about quite a bit is that, you know, we use our data to make those predictions and those forecasts and right now the data is jacked up, because nothing has been normal, nothing has been repetitive or consistent. And that’s one of the challenges moving into next year. So I know that that’s why people are asking this question, even more than they were asking it at this time last year, because people just want to know, what can I expect? What can I rely on? And the answer is, nobody really knows anyone who’s telling you they know what’s going to happen in 2021, even if it’s like an industry trend is wrong, because there is no way right now, because the way the business is done at the moment has changed so rapidly, just in the past eight months, the business models that, you know, people started with have had to switch and change and adapt and evolve. So nothing is the same as it was. And you know, even for us, we had specific business goals going into 2020, those have all changed, we have had to also adapt our services, we’ve had to adapt the way that we do business now. You know, the basic nuts and bolts, we were always were a tool that never changed. But the things that our customers need have changed quite a bit. And sometimes just on the fly mid conversation, someone’s getting new information. And we have to be ready to pivot. So, you know, when I’m asked the question, What are my 2021 predictions, I think it’s less about those tangible it’s going to be this, it’s going to be that and more about actions of people are going to continue to need to be agile there are, should be spending their time now, you know, writing down all of their processes and their governance. So they have that ready, so that they can alter it and evolve it. What do you think, Chris? Christopher Penn 4:04 I completely agree with you. Um, in the past, marketers had the luxury I think of being able to ignore kind of the big picture. And you can’t do that in in these uncertain times. So we have to start with the big picture we do, here’s what we are reasonably sure of. We are reasonably sure of that. By mid 2021, one or more vaccine candidates will be available and that will start being rolled out. We can be reasonably certain by the end of 2021, you know, some stuff we’ll get back to quote normal. Until then not the case. The bigger problem, I think is we still do not have a grasp on the amount of business and economic damage that has occurred. It’s kind of like a hurricane comes through bad things happen. Everyone’s hunkered down appropriately. And then the hard part is always after the hurt. Can’t like you got to do all the cleanup, the flooding, the reconstruction stuff that takes a really long time. And we are still in the middle of this disaster. And we’re not out of it, and we won’t be able to come out of the bunker and, you know, look around. So Oh, look, this is all flooded for quite some time. And so for the next year, I think you’re absolutely right, there is no telling what’s going to happen, except that we know it’s not going to necessarily be great. It will still be, you know, businesses will all still be operating under constrained conditions. And the biggest thing that a company can do, I think, is implement your advice very specifically, and that is have your analytics infrastructure in really good order, and be looking at the data very, very frequently. because things change all the time, you know, it is November 16, as we’re recording this, and for a large part of the United States, you know, we’re kind of having to revert back to like March, April, business conditions and lock downs, because the pandemic has gotten so bad, so fast. And if you were trying to forecast out, like, oh, what’s the holiday season gonna be like? Well, yet, you don’t know. Because there’s now this massive wrench like you might not leave your house for the holidays. Katie Robbert 6:14 And so even large brands have sort of adapted have started adapting. So, you know, Black Friday, the Friday after Thanksgiving is usually a very large shopping. But in order to keep people from lining up at the door, a lot of these larger brands like Target, WalMart, so on and so forth, have said it’s Black Friday, all month, don’t please don’t all show up on Friday. And so like things that we kind of take for granted that have been routine for years, have all changed. Christopher Penn 6:44 Exactly. So are our advice to the extent that we can give advice for an unknown year is have your finger on the pulse of your data so that when something changes and change will happen very quickly. You’re not caught unaware, in terms of other forecasts and predictions, and the other, the only things that you can count on to the things that don’t change. And we said this, back in March and April, when when things really started first changing, which was what’s not going to change, people still need to eat food, people still need a place to live, people still need to find work. Businesses still need to sell stuff. And so figuring out how are people continuing to adapt and implement and stuff? Yes, we’ve all had, you know, those wonderful zoom calls with various holidays, and things. And you know that that has definitely gotten old, but the alternative is worse. But we have started to see some interesting innovations. There’s interesting innovations in the conference space, you know, different companies have tried different virtual events to varying degrees of success. We have seen substantial growth in media creation. Everybody in their cousin, the last two years has started a podcast, right? Everybody, their cousin has a YouTube channel of some kind people have been, you know, doing all sorts of stuff on Tiktok and things and it’s for businesses, it really is figuring out where the audience is. The one thing I see as a total Miss for businesses the last year that they desperately need is you need a curated private community, you know, if, for example, ours, if you go over to Trust insights.ai slash analytics for marketers, that’s our private slack community. And, okay, do you want to talk just briefly about just how valuable that community is not only for the people in it, but also for us as a business. Katie Robbert 8:32 So for us as a business, you know, it’s an opportunity to have our audience, a captive audience, you know, with the right people who are interested in the things that we’re interested in. And so it’s an opportunity for us to, you know, do some market research, what are you interested in run a run a short poll, but also just to sort of sit back and listen, to see the types of conversations that people are having with each other, and helping them you know, helping one another solve their problems and seeing this is the type of issue that is most common, this is how people usually approach because what we want to do is make sure that we are meeting people where they are, versus asking them to come up to where we think they should be. And so the other benefit, obviously, is a dedicated space for, you know, professionals who are interested in these things so that they can network, they can share what they’re working on. They can see new job opportunities, all of the things that go along with being part of that type of community. It strikes me the reason. One of the reasons why I really try to focus on like your sort of what you can control is when I was working in pharmaceuticals, more regulatory field, we would go through disaster planning exercises, and I don’t think that that’s an extremely thing to be thinking about at this time. Now, disaster recovery disaster planning usually starts with things like it. But if you’re in the mahr tech space, then you have a foot in the door. And it reminds me of a more extreme example that we were going through of what if there’s a natural disaster? And what if we don’t have power for the next three weeks? What do we do with the company and we really started to think about the priorities of the company. And so, you know, the first thing we need to do is make sure that we can communicate with the employees what’s happening. So how do we do that? Do we, you know, find landlines? Do we get walkie talkies? And obviously, these are more extreme examples. But that it was like, okay, people probably want to get paid. So we need to make sure that accounting has a piece of paper and a pencil and a stack of checks that they can literally write out to people. And so while I don’t think that marketers need to go through that extreme planning, they should definitely think about, like, what happens if my analytics, you know, shut off for three days, or, you know, I can’t reach my audience, or Facebook changes their algorithm again, or becomes a completely, you know, paid platform or those kinds of things. And I think, instead of focusing on what are the trends and hot topics going to be, personally, I would focus my time more inward more internally in the company to see what do I currently have going on? What makes the most sense for my customers and my company, and my goals, versus what everybody else says is going to be important next year, because, again, it’s sort of a, like, I’m gonna stick my finger in the air and see which way the wind is blowing and make a guess. Christopher Penn 11:51 Yeah. And the focus on the things that you do know that haven’t happened yet. So real simple example of this, the California population voted a new law into effect cpra, that will take effect January 120 23. It supersedes ccpa. And one of the big changes for marketers is that instead of the previous law said companies above a certain size and in certain parameters can’t sell data over a certain amount to other companies, it’s not allowed. The new law says you can’t share. Right, so for everyone who’s used to buying and swapping lists and doing all these different kinds of things, it’s pretty big deal. Now you have three, two and a half years to to get ready for it. And as we know, with ccpa, and GDPR, people won’t they’ll wait till the last minute. And and it will have to go be messy. So if you have things in your calendar that you know are going to happen, year out two years out three years out, now is the time to plan for while things are in transition and chaos, it’s actually a really good opportunity to put new things into the mix. Because people as a try, just adapt to how things are working, we’ll just take these new changes in stride, because everything is changing. One of our big, big, big pieces of advice and again, this is one of those things that is a trend and you should be doing is you’ve got to own the right to contact your audience. And that means things like newsletters, because you don’t own your Facebook page, you don’t own your LinkedIn channel, you own none of these things you do own your email list. As long as you keep paying your bills, he will be able to send email to people make a priority for the next year to extract as many people from all these platforms you don’t own into a platform that you do own into, into your blog, into your website, into your your podcast, into your email newsletter, because you will at a certain point for sure, stop getting third party data, right? Cookies are already out of Safari and Firefox. Google has said as of 2022, that are going to support third party cookies and Chrome. So if you are relying on ad networks, and DSPs and dmps, to try and give you additional insights and your customers that you don’t have to ask them for, that’s getting harder and harder. The solution is create content that your customers actually want and that they value and exchange it. Think of think of customer data, like something that’s for sale. You can’t just take it from somebody, you have to trade with them if exchange with them. I want your data in exchange for this valuable thing. And right now, I don’t think hardly any markers have anything that I would say yeah, I want to buy that. Like, even if was just $1 could we could you convince a customer to buy your newsletter for $1? Oh, by a blog post for dollar, I would guarantee that 99% of marketers. Yeah. Like customers like, No, no, you pay us to read your craft. Katie Robbert 14:50 Well, and I think that there goes that goes back to your point about you know, building those communities. And so that’s one of the benefits on both sides is you can literally say to your community What do you want me to write about? What do you want to know more about? And they will tell you, because people in these communities are not shy, they love having a voice, because they get to be heard. And then you can say, okay, cool, I now have my punch list for the next two or three blog posts. And then you share that back with them. And it’s this reciprocal cycle of, I’m telling you what I want, and that I’m getting what I want. And then I continue to tell you what I want, and I continue to get what I want. And it’s that sort of feeds that machine of giving your audience content they want to read. You know, you were talking about these third party cookies. But Chris, also, can you talk a little bit about ad blockers, being able to even show the ads? Christopher Penn 15:44 Yep, yep. So ad blockers for if you’re not looking in your your data, you probably don’t know just how much data you’re missing a number of ad blocking services block even basic tracking mechanisms. For example, there’s one called ghostery, that can impair some installations of Google Analytics, right. So you have a certain percentage of visitors who you can’t track because it’s the data simply is not there. And so ad blockers, and, and AD, anti ad tech technology, are a growing concern, I forget what the stat was, but something like 30% of visitors on the web use some form of ad blocker. And now, in the new version of Mac OS ad blocking is built into the Safari browser, it’s it’s no longer you know, you don’t have to have an add on. There are, there are things I believe in Microsoft Edge that do similar. So you’re seeing the browser technologies themselves become more hostile to advertisers, which again, underscores the need for you to get the first party data from your customers by asking them for it and giving them something valuable in exchange for it. Because if a customer tells you, Hey, I’m the VP of Marketing at this company, like that’s, to me is a lot more valuable than trying to get it from some third party database based on you know, their, their what’s on their LinkedIn profile. know, somebody says, This is who I am, this is what I do. And this is how I like you to communicate with me. That seems like a win. Katie Robbert 17:06 Mm hmm. Absolutely. Because you have asked them, you’ve asked their permission, they’ve willingly given you this information, versus you finding out this information from a third party. And they’re like, Hey, I don’t remember signing up for this thing, I don’t want this. And so you are not only giving them what they want, but you’re giving them information in the way that they have asked for and given permission for. And that’s a big deal in building that customer relationship as well. And those are the things that you have control over is your relationship, one on one or with a community. And so, you know, when we’re talking about trends for next year, it’s that personal connection, it’s that community, it’s that personalized content. And it’s not to say every single individual person gets their own individual piece of content. But you can start to make sort of those segments within your own communities that you’re building to say, majority of my community is, you know, VP of Marketing. What does the VP of Marketing want? Well, ask the VP of Marketing, hey, if you’re a VP of Marketing, what kind of content are you interested in? Or what kinds of problems are you facing? So then you can deliver that content in the community, you can post it on social to try to bring more people in. And then you also have that email newsletter, and the newsletter is something that is completely within your control. Maybe you send one out every day, maybe you send one out once a week, maybe once a month, it’s something that you have control over and again, ask the community, if I was to send you a newsletter, how often would you want to receive it? You know, what kinds of information should be in there? Do you want curated content, you want original content, you want a mix of both? You really have that flexibility? Because these are all things within your control. And so, you know, if someone asked me, What are your 2021 trends, I’d say it’s things you can control, things that you have ownership of. Christopher Penn 19:06 I completely agree. I would say to add on to that. 2021 is the year that you probably should implement the best practices that you’ve been told the last 10 years. It’s always funny to listen to all the people talking about trends, like oh, this is you know, this is the trend Yeah, but you’re not going to do anything about it. So it’s like reading a Fitness magazine instead of working out like no, no, you actually need to go work out in order to see any benefit whatsoever. But so recap is better than nothing works back as Moses except water. So for those who have asked, hey, Trust Insights, Hey, folks, what are your 2021 treads number one, your analytics infrastructure has to be an order because you will need it to make decisions based on data in a rapidly changing environment. Number two, change is going to be rapid and extremely unexpected. You don’t know what’s going to happen for the most part beyond the things that won’t change number Three, make sure you have a private community, a curated community that is essentially an unpaid focus group that you provide value for. Number four, make sure that you are providing value in exchange for customer data, cuz you’ve got to own your first party data, you’ve got to bring all that data in and get customers to give it to you. Because you cannot rely on third parties to give it to you, it will, that is just going away so rapidly. And number five, do the things do all the best practices that you’ve been told to do for the last 10 years right in business and everything else people have been saying for ever pay. If you want to get more fit, eat less and exercise more, eat less bad stuff anyway, if you want to get wealthy buy low and sell high, right? Because these are not arcane concepts that are difficult to grasp. These are just the hard work that we all have to put in. Do it. Any other trends, I forgot Katie. Katie Robbert 20:54 I mean, I think that’s it, you got to do the hard work, you got to take the time and do your own planning and look inward. And all of those things will start to fall into place. Christopher Penn 21:05 And the golden trend of all provide value to people. I provide value to people so that they have a reason to want to do business with you. There’s there’s just so much marketing out there that is that is taking trying to take before they give and it never works out for anyone. So if you’ve got a trend that you think we need to talk about that that or you want our opinion on, by all means please join our slack community over TrustInsights.ai dot AI slash analytics, remarketing, artisan, we’ll speak up say, Hey, what do you think of this trend, we’re happy to have this conversations anytime of the day that we’re awake. And if you have not already subscribed to this episode of the show, or the show, wherever it is, you’re watching to go to Trust insights.ai slash ti podcast where you can subscribe to this and catch all the back episodes. And finally, if you miss chip, be sure to stop by our YouTube channel over TrustInsights.ai to AI slash YouTube and every Thursday at 1pm eastern time we do a live shows and if you’ve got a question about you know trends and things like that stop on by any Thursday and ask those questions during the q&a section as well as enjoying the rest of the show. Thanks for watching and we’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this episode of In-Ear Insights, Katie and Chris dig into this key question: is there such a thing as being too data-driven? “I’m not sure if you and I have discussed this in the past but it’s been on my mind a lot lately, and I’m not sure if there’s formal thought around this: Being TOO data-driven. I’ve seen areas where data point to weird or undesirable possibilities. Examples: A female YouTuber is clear that videos do better when the thumbnail has her in a bikini. Sometimes it’s appropriate because it’s beach vlog, other times, no. But the data say: wear a bikini By far, my most popular blogpost is one I regret having written because it was tongue-in-cheek and deliberately used a clickbait headline. It resulted in 6 years of mean comments from people who didn’t read the article. But, the analytics suggest I do more of that. The difficulty is in having clear evidence that something works, but is it content (or a product/service) that we want to keep creating? It’s popular to say, “it’s not about you, it’s about what the audience wants.” And that’s been the approach of coaches I’ve paid and people whom I’ve asked advice from. They go straight to the analytics and suggest doing more of the top performing stuff. For the female YouTuber, it might be an easy decision to always use a bikini-clad photo of herself on thumbnails and make that part of her brand. But this person was torn. Once in a while was fine, when it was relevant to the video. But, the data was pushing her toward, “no. Do it all the time.” Said another way: when do you tell the data “shut up! I’m not listening to you”? And how do you manage the mental strain of resisting something that clearly works?” Dig in as they tackle how data-driven marketing and avoiding these kinds of traps. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-toodatadriven.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses to start you on your AI journey including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes. Visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people. Our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more. Again, that’s Trust insights.ai slash contact in this week’s in ear insights friend of the show oz Drusilla asks, Is there such a thing as being too data driven? And the example he gave in the comments he made on LinkedIn? Was that when you look at data, can it lead you down pathways that you might not want to go one example. He said, a female youtuber is clear that videos do better when a thumbnail has this person in a bikini. Sometimes it’s appropriate because it’s a beach flog other times No. But the data says always wear a bikini. And there are a variety of other things. But in these examples, is there such a thing that where you can be so focused on trying to maximize the results? That you end up doing things that might be not great? I feel like there’s there’s two branches, we could go with us? I think we should talk about them both. One is on testing. But I think the bigger one comes down to values and ethics. So Katie, when you listen to these different examples of you know, people being ready clickbait headlines, or using thumbnail images and stuff, yes, the data says, do more of this. What’s your take? Katie Robbert 2:12 My take is that you Yes, you can be too data driven. And it’s, you know, as you start to walk backwards, you know, first and foremost, is your data, right is are you getting the right information. And so if you’re so heavily focused on what the data is telling you to do, have you been focused enough on is the data set up correctly. So I guess there’s that part of it. So there’s like the, the infrastructure and the data integrity piece of it, because, you know, false information, misinformation, all of those things include data in it. But that doesn’t mean the data is correct. And so that in and of itself is a fairly large topic to tackle. But if you’re talking about, you know, like the ethics of it. I think that listening to the data is usually a good plan. However, what we know about, you know, being data driven and artificial intelligence in general is that there is a still a very large role for humans to play, you need that human judgment. And so, you know, we’ve talked on past episodes about how, you know, training data sets have been included incorrectly. And so it gives you the wrong information, the most, one of the more famous examples being the Amazon hiring only men, because that was what, historically, their training data set said. And you still need that human to say, you know what, that doesn’t sound? Right. And so that’s to me really sort of where we’re back with, you know, can you be too data driven? Absolutely. Because if you take the data at face value, and just assume that it’s correct, then, you know, that’s also problematic, you still need that gut instinct, or that just that, that doesn’t seem right, or that doesn’t align with, you know, what it is that we’re trying to do? Christopher Penn 4:01 Yeah, it’s, to me, data driven is a tactic. And a lot of these questions or access to Dziedzic ones in the sense that imagine, like, you know, data driven literally is when you open up Google Maps, and you have it help you plot your destination. In this case, to me, it seems like the YouTuber example, isn’t thinking about the destination of where they want to go. Right. So they’re just having the essentially the their GPS is saying go this way is faster, this was faster, this was faster, but there’s no real destination, or it’s a destination, they didn’t mean to go to like, do you want to be, you know, the bikini clad YouTuber? Is that going to be your brand? Is that your strategy? If so, then yes, following that, you’re the current data you’re on is the way to go. But if that’s not where you want to go, then you’re right. Your data is actually misleading you in the same way that if you wanted to go to Boston, but you put, you know, Burlington, Vermont and your GPS, it doesn’t matter how optimized the data is, you’re still going to the wrong place. I feel like in this in these examples that we’re talking about, it feels like the person or the question has not built that strategy, has not built a plan and has not set a goal. That’s clear that would help them with that data. Katie Robbert 5:19 You know, it’s interesting, you say that data driven is a tactic. And I agree with you, I think that, you know, when you say that your mission or your values are to be data driven? Well, yeah, you and everyone else, whether or not you realize that you are looking at data or listening to data, or, you know, including data in your decisions you are, every single decision you’re making throughout the day includes some data point, you know, and it’s not always a numeric data point, you’re not always saying, I’m going to look at a spreadsheet and make a decision. So, you know, I would argue that everyone in everything is already data driven. So saying it is redundant, and it’s not, you know, a core value of your company, like, you know, if you say, Hey, I looked at our revenue numbers for last year, and I want more great, your data driven, you looked at the data, you’re driving your decision based on the data. So I would argue, using the term data driven, is irrelevant in the conversation, like, so it’s just like, take it out of there. Let’s just say everyone’s data driven, everything is data driven. Everything is a data point. So just get it out of the conversation. So then really, what we’re coming down to is, are you making the right decisions? Based on the information presented in front of you? Christopher Penn 6:38 Are you seeing data driven is the new synergy? Hey, Katie Robbert 6:46 let’s circle back on that later. put a pin in it. Christopher Penn 6:51 All right. Um, secondarily, I think there’s the optimization trap at play here, too. And this is something that a lot of folks who do AV testing should be aware of. And what it means is that if you are constantly optimizing, which we recommend, you should always be optimizing and trying to improve your results, what can happen is you end up optimizing for an ever smaller part of your audience. So if you have two versions of a creative A and B, and a wins by 60%, great, now you optimize for a and the next time you do a test, you have C and D, and C wins by five, you know, 55%, now you’ve, you’ve not taken 55% of your audience, you’ve essentially taken 55% of that 60% from a and so you keep getting all this pin down into this, this niche of this is what the data says. But again, you’ve lost sight of the goal, and you actually create an increasingly larger pool of people you’ve pissed off along the way. Because you’re like, Oh, yeah, you’re even though you’re 40% was the loser in this, it’s still 40% your audience. So in these examples of, you know, should you wear a bikini all the time, because it’s, it’s, you know, what the data says, You’re not introducing anything new into the testing so that you can see, maybe this isn’t the best thing. Or maybe there’s there’s some additional context, one of the things that in machine learning that is really important for coders to understand is, you need to do what’s called perturbation testing, where you add in five or 10%. net new things that were not that you either explicitly excluded from the optimized pool, so that you can test to see, hey, maybe this new thing we’re trying on the side here actually performs better than than the, you know, the 80% of the pre optimized data that we started with. That way, you can constantly find out, oh, there’s a new thing here. Real simple example. If you’re on Amazon, and you’re shopping, and you know, this happens after a while you’re shopping for, I don’t know, new slippers. And suddenly, you’re, you know, your whole feet of you might also enjoy becoming variations on slippers, right. But you’ll notice as you do that, Amazon will toss in like a lawn mower, right? or other things that are related to other interests you have, but they’re they’re testing to see like, are you still on the slippers kick? Or have you changed your mind like now you you’re looking at books, or now you’re looking at, you know, various versions of hot sauce. That testing allows you to figure out, oh, this isn’t just a one trick pony. Whereas it sounds like in a lot of these examples. There’s not enough testing going on. And so these marketers are ending up as one trick ponies as opposed to constantly testing their audiences. And that, to me also feels like not only a lack of strategy, but it also feels like a lack of awareness about how testing an optimization supposed to work. Katie Robbert 9:45 I think it’s testing and I think you’re right, I think the other piece of it because as you know, digital marketing like you need all of the things working together is are you even getting your stuff to the right audience. And so you know, I’m looking at these examples and it says, you know, a female youtuber does better with her thumbnail when she has a bikini on? Well, I don’t even know what the thing is that she does. She could just be, you know, reaching people who like to see a woman in a bikini. And so it doesn’t matter what it is that she does, she’s already reaching the wrong audience. And so I think that there’s a lot of, you know, audience analysis that also has to go in with it. You know, this person who asked the question, oz said that my most popular blog post was a tongue in cheek and deliberately used as clickbait. Well, I don’t know what the thing was about. But obviously, it attracted a lot of people who may not even care about the thing that he was trying to write about. And so testing is a big part of it. So obviously, don’t just do one test. You continue to iterate you’re testing Chris’s you’re describing but also, you need to understand what is the audience that you’re reaching? And so, you know, let’s say, for as a terrible example, every single one of our blog posts starts to be extremely No. No, has a picture of one of our dogs. And so there are millions of people in the world who love pictures of dogs. And so every one of the mobile or desktop previews includes a picture of a dog, therefore, people who, like dogs start to click into our content, and very quickly realize this isn’t the content for me, but it had a picture of a dog. So I was attracted to it. And, you know, we can continue to test, you know, my dog, your dog, John’s dog genies like, all the different dogs that we have, it’s still the wrong thing to be testing. And we’re still attracting the wrong audience. We are attracting people who love dogs, when we really want to be appealing to people who want help with their data, or their Google Analytics or their marketing strategies. And so I think that there’s, you know, another layer to this in terms of being too data driven, like, yeah, if we keep putting up dogs, the data is going to tell us that picture, the pictures of dogs are really popular. But that’s the wrong thing to be putting up a picture of, because then we are attracting the wrong audience. Christopher Penn 12:13 And I think that would come out in the data if you were doing a better job with the data if you had better attribution modeling and things because you would find out you attract a lot of traffic, but none of it converts. Unknown Speaker 12:23 Right. So Christopher Penn 12:23 if you’ve optimized for eyeballs, then yes, dogs in bikinis and dogs in bikinis would be the thing to get eyeballs. Unknown Speaker 12:32 Can we just let the bikini thing go Christopher Penn 12:35 with cigars? Katie Robbert 12:37 Just let it go. Christopher Penn 12:40 We would get no conversions on that. Because you get there. As one of these examples, you get there like, Oh, this is not at all what I came for. And so that’s a failure of having the wrong goal. Right? If you are selling something like AWS is a Microsoft MVP sells, you know, amazing Excel trainings. If you’re putting up a piece of clickbait, that gets an audience that doesn’t care about Excel, then you’re not going to convert, you know, we put up a piece of content. We’ve had this discussion internally, like, should we be putting up as much content as we do about things like Instagram analytics, when we’re not a social media agency, we can help you with your social media data. But we’re not a social media agency, we’re certainly not an ad agency. And are we getting those conversions from the right people? So it again, no surprise, it comes back to what’s the goal that you’re working for, towards if the goal is eyeballs to sell ad inventory? Great. But if the goal is to get people to buy something, a core product, and you’re out of alignment with your core product, it’s not going to be great. But I suspect for a lot of these examples, that attribution analysis probably does not exist. Katie Robbert 13:50 It doesn’t, I would say that it definitely doesn’t. And we know from the work that we’ve done, that there’s not a great out of the box system that does the kind of attribution analysis, Chris, that you’re talking about. So, you know, out of the box, Google Analytics does offer attribution analysis, but it has limitations to what it’s able to do, and what it’s able to show you in terms of what’s happening within your data. So it does a great job of first click, and last clip, click attribution. And depending on the type of company that you are, that might be fine. But if you are a company that has multiple channels and multiple efforts and social and email and organic and paid and you know this and that and other things, then there are definitely limitations to what you can find out from the out of the box, Google Analytics, attribution modeling. And so you’re definitely at a disadvantage in terms of finding out what it is that’s working for you and bringing your audience and but if you’re just looking for basic, like, does this page help people Convert, then yes, you can find that out from Google Analytics. So you know, in this example of, you know, I wrote a blog with, you know, clickbait, and it resulted in six years of comments. Well, I can probably make this strong assumption that it drove traffic, but nobody converted because of it, because it was clickbait. And you were learning people into read the thing. And they were like, hey, that’s not what I wanted. And so those are easy ways to determine is the content that you’re putting out there doing what you want it to do. So Chris, in your example, we could drive more traffic to our website by posting more about Instagram, TV and other social metrics. And people would convert but then we would have to sit there and say, but we don’t do social media. So you converted? And we’re so sorry, we can’t help you. Yep. Unknown Speaker 15:50 I think Christopher Penn 15:52 when we look at how to look at this data, even a basic attribution model, you know, time decay, for example, would be good enough to know oh, this ain’t working. Right? You might not, you need the more sophisticated attribution modeling to understand the nuances of how different channels interplay. But if you put enough stuff, it’s just not converting like, Oh, this, this flat out ain’t working. You don’t need a sophisticated mods, no, things just aren’t working good. You do need a sophisticated module, understand how things work. But this is pretty clearly case of a I put up a picture of a dog in a hat, but I sell analytics. That’s, that’s not going to convert. And so again, unsurprisingly, come back to Did you have a goal on a plan? And if the data is telling you something that is opposite? The goal, you have to decide is the gold one you still want to pursue? And if it isn’t, great, change it? And if it is, then yeah, you have to ignore the data, you have to be willing to say, Yep, we’re going to willfully ignore this data, even though it looks good. I have. There’s a post I wrote in 22,007. The difference between two different types of decongestants, sudo, f adrene, and phenol, Efrain. And that’s the one the highest traffic blog post I get on, on any given wintertime month, because people still don’t understand the difference. But I ignore it completely. I don’t ever optimize it, I’ve never gone back and updated a refreshing thing, because it’s not something I want to be known for. It’s there, because I wrote it for fun, but it’s not relevant. And so I put no resources and effort towards it, because that’s not what I want my people to come into my blog Come on, same stuff for my pizza sauce recipe. It’s good. It’s not what I want to be known for. So to kind of circle back and wrap up on all this, if you don’t have a goal and a plan, yes, your data can mislead you can take you in places you may or may not want to go. And you will always end up if you don’t have a direction, you’ll always end up you know, sort of the lowest common denominator, whatever your audience is, you need that goal on that plan, Katie Robbert 18:04 stretching anything else, we need to tie up on the skatey you need to go on the plan, and you need that human interaction with the data, the data stand alone, doesn’t make decisions, you know, your audience, you know, your company, you know, your goals. Therefore, you need to have a heavy hand in, you know, determining what you’re going to do with the data. So if you just like hand someone spreadsheet and say do whatever the data says that is the absolute wrong way to be data driven, data driven, really, you know, the spirit of it. What it is meant to encompass for companies is that we use data in a smart way to make good decisions for our company. What that really implies is that we are collecting data the right way, we are looking at the data on a regular basis. And then we are making decisions based on what we inherently know about our audience and what they want. Because the data can’t always tell you all of that nuance. And so that I think sort of, you know, you need the people, you need to collect it the right way. You need to trust your gut and your instincts and you need to collect the data correctly. Christopher Penn 19:14 data driven means you do the driving the the map just helps you get there. And if you don’t know where you’re going, you will find out very quickly just how entertaining google maps can get you lost. If you have follow up questions about this or anything else, please hop on over to our free slack group. Go to Trust insights.ai slash analytics for marketers you can join over 1400 marketers talking about all the analytics issues of the day. If you haven’t subscribed to the show, wherever it is you’re consuming right now go to Trust insights.ai slash ti podcast where you can subscribe to the show on the platform of your choice and never miss an episode. Thanks for listening and we’ll talk to you soon take care. One help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help Unknown Speaker 19:56 you

In this episode of In-Ear Insights, Katie and Chris discuss the latest findings on Instagram engagement rates, especially for Instagram TV. You’ll learn: How brands fared in 2020 with a global pandemic and multiple, conflicting crises How influencers fared and whether influencers still as powerful What you should do with your social media monitoring strategy in 2021 Watch and listen as we dig into Instagram! Read the original post and data here. Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-igtv2020.mp3 Download the MP3 audio here. Got a question for You Ask, I’ll Answer? Submit it here! Subscribe to my weekly newsletter for more useful marketing tips. Find older episodes of You Ask, I Answer on my YouTube channel. Need help with your company’s data and analytics? Let me know! Join my free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for watching the video.

In this week’s In-Ear Insights, Katie and Chris discuss a recent consumer data experience with one vendor sharing data to another vendor without the consumer’s active consent. In this show, you’ll learn what active consent is (and why it matters), how to track down the likely vectors of data sharing, and how marketers should think about consumer data collection and sharing. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-consumer-data-detectives.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses to start you on your AI journey including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes. Visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people. Our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more. Again, that’s Trust insights.ai slash contact. In this week’s in your insights. Let’s talk about data forensics, both from the consumer perspective and from the marketers perspective. So Katie, you’ve got a bit of a mystery for us data detectives to solve. Katie Robbert 1:15 I do. And it was one of those things as a consumer that took me aback a little bit. So even though you know we run a data science company, there’s still a lot about how data is shared, that we don’t necessarily have the answer for so something that we want to start to pick apart is so over the weekend, on Saturday, I went to Walgreens a local pharmacy chain to pick up a haircare product, not a product I’d ever used not a product that I ever talked about. Not a product I’d ever even known existed until I got to the store and saw Oh, this looks like the kind of thing I’m looking for. But I didn’t go into the store thinking I want this exact product. I’ve done some research on it. So there’s a little bit of that happening. So I bought the product. I came home and then on Sunday, I start seeing ads for it on Instagram. Now my first thought is, how the heck does Instagram know that just 24 hours prior I for the first time discovered and purchase this product. Now I don’t have a smart system set up on my phone. So I haven’t been talking about it, there is a good chance that my phone is still listening regardless. But then I would have had to have named that exact thing in the course of conversation with myself because my husband was working all weekend. So it to me, it’s just sort of a mystery as to how Instagram, within 24 hours knew that I was in possession of this product. So Chris, what are you thinking? Christopher Penn 2:51 Well, let’s ask some questions. So the ad on Instagram was by Walgreens was it by the hair company? Katie Robbert 2:57 It was by the hair company. Christopher Penn 2:59 Okay. And when you were at the shelf, did you notice it was a standard Walgreens shelf? Was there an electronic display? Was it an endcap? What was it? Katie Robbert 3:10 It was your standard? Metal cold shelf? Christopher Penn 3:15 There was no cool display. Okay. And what apps were you running on your phone at the time, you might not know that. But if there are apps in the background that were running, Katie Robbert 3:28 um, I don’t know what was running in the background. At the time, I was listening to an audio book, I was listening to overdrive through my library. But I highly doubt that my library is transmitting haircare product data. Most likely things like you know, GPS and that kind of thing. But otherwise, none that no apps that I’m aware of. And that may create a problem. Christopher Penn 3:53 And did you use your Walgreens customer card? Katie Robbert 3:57 I did. And that was my thought. Yep. Christopher Penn 4:00 Okay, so there’s three, maybe four different possibilities here. One, apps, particularly Spotify can transmit location based data within stores back to merchants and vendors. They are very, very good at that their location data from a source that we have talked to can target you down to the aisle so it knows he can know what aisle you’re in. And obviously, based on that what kinds of stuff is in that aisle? That’s one possibility. second possibility would be beacons, which are the NFC devices that a merchant would typically place in and around a store that track your movement either throughout the store or very specific aisles. So if there was a special cool display is a nonzero chance there’s a beacon embedded within that display. You’ll see this in electronic pricing shelves like if you go on to like Alibaba for example. You can actually as a merchant by shells have cameras and displays built right Along with facial recognition, and you can tie that to, you know, different merchants. And so the shelf can recognize you, and display and change the pricing for you, like give you a discount, or a promotion. But based on what you’re saying, it sounds like the most logical thing is the Walgreens customer file. Obviously, you know, every merchant with these loyalty cards, shares that data, well sells that data to other to other brands. And if you’re seeing ads for it, what work, what I’m wondering is, if it’s just the, you know, in the file, it’s like, Hey, you bought this thing, or you just bought something from this company. Because normally, when you get an ad for something you already bought, it’s usually because a marketer didn’t set up their advertising system correctly, and is trying to sell you something you just bought, which is kind of a waste of ad dollars. Katie Robbert 5:54 Well, and so that was the other thing I was thinking is I’ve already purchased the thing, why am I now seeing ads for it? Which so that’s a whole other conversation about how improperly ads can be set up in the wrong messaging, depending on where they are on the customer journey. You know, so to your first point about the location data, through apps, like Spotify, you know, that actually makes a lot of sense, because I have previously seen ads for things that I was looking at or considering at other kinds of stores. While when I was at Walgreens, I happened to be in the haircare aisle, they had no fit no like fancy displays, and it’s actually a much older Walgreens. And so I would have, I would have been less surprised if I just seen general haircare ads, because it’s a rather long aisle. And there’s hundreds of different kinds of products, but they were able to show me an ad specifically for the product that I purchased, which is what sort of, for me raised my hackles of like, okay, someone sold my data. And within 24 hours, I started seeing ads for the thing. So I’m guessing, Chris, that your analysis but the Walgreens Customer Care number is the thing that tied me to this specific ad, what I was even more caught off guard by was the quickness in which it happened. It wasn’t a week later, it was literally within 24 hours for this exact product. Christopher Penn 7:26 The thing about the 24 hour delay tells me that it’s likely the Walgreens customer number because Spotify and other GPS based apps, that date is near real time. So you could have been seeing, you know, Instagram as fat as soon as that evening. If if they was because what they’re going to try and do, ideally is hard you before you leave the store, like if you’re listening to something on on the on the radio, you’re hearing that in the in the non paid version would be a good way to try and save that shopping cart. If it’s a day later, that suggests batch processing, like you get a daily file from the vendor. And then you know, they load that into their ad systems and they spin up, it goes into the next day as batch. And to your point, if it had been a week later, that’s something different. That’s either the vendor gets a much less frequent data files, or they are just really bad at running their own Mar tech stack. But that 24 hour delay tells me it’s probably a daily batch process. And then that gets loaded. And so from a forensics perspective, the the thing that you would do next to test it is no, I’m not saying go out and buy a whole bunch of stuff at Walgreens. But go and you know, browse Walgreens without buying something one visit and see if you see any new ads in the Instagram feed, if so that tells you that it’s not the customer data file itself, and then maybe do go buy a sample of sub products that you know, could likely be running e commerce ads, and see if again, those start to show up in your Instagram feed as well. And that that will give you that sort of scientific test like yeah, there’s, there’s some causality here for this. There’s not for these other things. Katie Robbert 9:05 So you know, it’s interesting. If we go back to Spotify for a second, I knew that they had the you know, you can break it down into very small geographic regions. And they know when their customers are either driving or traveling through certain areas. So I’ve experienced that before. And I’ve actually set up ads to that effect before. And that makes sense because it’s using your GPS information to say you are in the town of Framingham, Massachusetts, therefore the you know, vendor who is purchased ad space in the town of Framingham is now going to have you listened to these very specific ads. That to me makes a lot of sense. It was the for it. A bit of a disconnect between having no awareness of the product and then seeing starting to see ads on the product. You know, I wonder if this is something that consumers are even aware about? Done with their information. As they’re signing up for these, you know, discount cards, I think that there’s this lack of understanding that these discount cards are actually a bigger deal to the companies that are issuing them. So you might get like, a $1 off or a $2 off coupon. But the company is getting much, much more in terms of rich customer behavioral data, in order to develop new products and show ads and do e commerce and you know, basically follow you everywhere. Christopher Penn 10:36 Yep, so one of the other things to think about is who the parent company of the haircare product is because there’s a nonzero chance that it could be like Unilever, for example, in which case, that data file could be sent to the parent company, and it’s it’s shared down to the individual units and things. So there’s, there’s definitely some more forensics to be done there. But yeah, to your point about what consumers expect, the average consumer, I don’t think just has this the slightest understanding of how ad tech works. And ad tech is, obviously it’s a very lucrative industry. Obviously, it’s a very competitive industry. And consumers give an awful lot permission to things that they don’t understand. There’s the old Latin phrase, Nolan gratuites. And pronto, right, there’s no such thing as a free lunch. So as our friend Tom Webster says, if you’re not paying, you are the product that is being resold. And I don’t think that’s a mindset that consumers understand what their data which means that for marketers, we have to tread really carefully, and how we use consumer data, how we acquire it, how we store it safely, and then how we use it. Obviously, there are any number of landmines, you know, going all the way back to the famous target example of target sending your maternity ads to a team that, you know, that didn’t realize their father didn’t realize they were pregnant. Where we can cause more harm than good, right? We can, we can cause substantial reputational damage and part of good governance around consumer data is disclosed to consumers and up in an easy way. Like, hey, here’s what we’re doing with your data. If you don’t consent, you know, click here. It’s like every website, you go to saying, Hey, we use cookies, you know, click here to accept and then you know, there’s like, some tiny little button says, you could manage this and you open up like, here’s the 44 things we do with your day, you’re like, well, I only want this one here and these other 43 No, but yeah, most people don’t look at that. If you want actually, a fun thing to test would be to set up some event tracking on your on your websites pop up. Nobody know that manage cookies button to see how many people actually click on the, you know, manage my data, like I would bet you like maybe 1% of people that go to the website. Good. Everyone the other night never said just click the OK button to make the thing go away. Even though you could lose said your firstborn child is now our property. Katie Robbert 13:00 So let me ask you this question, Chris. So let’s say you are like me, and you signed up for, for example, a Walgreens card maybe, you know, 567 10 years ago. And let’s say you never like so you just you sign up for the card, because you knew you get some additional discounts. Great. No big deal. 10 years ago, you’ve never logged into their website to manage your account, you just on occasion, like maybe once or twice a month when you go to the store. They’re like, do you have a Walgreens account? Oh, yeah, I think I do. Let me punch in my phone number. And so you do that. And then you go about your day. And so I’ve never checked in to see, in the 10 years that I’ve had this account, has the privacy policy policy changed? Has my end user agreement change? You know, is there even a way for me to manage this information? So why am I so caught off guard that all of a sudden, they’re selling my data? And I never said it was okay, because it’s on me? I never checked to see if that was even something I was signing up for? Or if that has changed in their policy in 10 years. Christopher Penn 14:11 So you’re getting it why GDPR and ccpa exist, these these legislative requirements for marketers? Because in a lot of cases, yeah. People don’t know, they might have given a phone over there, they might have given an address and the company has likely done the minimum due diligence required means that they probably sent a postal notice, or an email that ended up in your spam bin saying, here’s how our privacy policies have changed. You almost certainly got notices about this, at the beginning of this year when ccpa took over, because every company did the whole you know, we all got these notices, even in 2018 when GDPR went into effect saying our privacy policy has changed got 500 of them and all at the same time for every company you’ve ever done business with. And what did you do? You probably put them all in the trash, right? Cuz these Yeah. But implicit in that is you were given notice, and you didn’t read it. And so the, you’re correct, the burden of responsibility for managing your privacy is on you. However, as marketers, we have to be cognizant of the fact that, even if we’ve made it, we’ve done the regulatorily required things, that is different than doing something with a consumer that has active consent. And that’s one of the topics that keeps coming up in privacy legislation is active consent means Yeah, yeah, actually read the thing. Like you went through the the two minute tutorial or whatever. And, and you click OK, knowingly, or you took a quiz? And it says, yes, you actually read the thing. In those cases, companies are a lot safer for reputational damage, because like, yeah, you watch the YouTube video to the end, we can watch that. And, and, you know, you’ve agreed, knowingly, what you’re getting into. And I think from a best practices perspective, we want that we want active consent for our stuff, we want customers to know what they’re getting into. And if we’re selling their data, we tell them, you know, what’s in it for them, like, Hey, I’m gonna sell you, I’m gonna sell your data to this haircare company. And in exchange, you might get some extra discounts on the hair guy think Do you agree? If the customer says no, I don’t want discounts cool. If the customers share, I’ll take an extra, you know, 20% off, then then they get to make the exchange. This was something that one of the presidential candidates Andrew Yang was was big on was saying that customers should have the right to sell their data instead, like the company is not permitted to do it, the customer is what it says, Yeah, you pay me, you know, 30% off your product, and I’ll give you my data kind of thing. Or you give me $1. For every time I click OK, I’m a consent button. It’s an interesting idea, because it would shift the economic burden to the the technology providers to the marketers, I don’t think it will ever happen because companies are not going to be willing to pay that. But certainly, with things like GDPR, it’s going to make a patchwork quilt of privacy. And so our guidance always to marketers is make sure your privacy policies and things adhere to the strictest standard. And then you never have to worry about whether you’re compliant or not. Katie Robbert 17:17 You know, you bring up this notion of the active consent. And I feel like we’ve talked about this before, probably on a different podcast. Um, you know, but I can absolutely see why companies would want to do the bare minimum and sort of like skirt around this notion of active consent, because it’s not in their best interest, to not have complete access to consumer data. So if they are putting privacy policies and consent forms and tutorials in the face of their consumer, the likelihood of them getting the data starts to go down considerably. Because a lot of people are saying, Well, no, I don’t want to share my data with you at all, ever. I want nothing to do with discounts, I want nothing to do with you, knowing who I am, my purchasing patterns, my likes my dislikes, I want nothing to do with that. So you get nothing. And then that then puts the companies at a severe handicap. And as I’m saying this, I realized, we have had this conversation before on a different podcast. That said, it’s still such an important topic that it definitely bears repeating. Because as it gets increasingly more difficult for companies to get consumer data, they have to then think of other ways to reach their key demographic and their market. And you’re gonna start to see the companies that are less creative, less agile, less innovative, just start to fall by the wayside when it comes to digital marketing. Christopher Penn 18:47 Yep, and the other thing is, as awareness properly, properly, becomes greater about the misuse of data, things like AI and you know, creating unintended biases, there’s an additional disincentive for you to tell a company who you are, what your preferences are, because you can obviously infer things like race and religion from those those data points. So yeah, there’s a strong disincentive to want to share that data, which means that as marketers, we have to figure out okay, what is the strong enough incentive to get somebody to want to share that data? If we agree that data is valuable, which I think if you’re listening to this podcast, you probably agree with that. Then what are we willing to pay for? What are we willing to give for it? What’s in it for the customer, and in a lot of cases, there’s not enough, there’s not enough in it for the customer to go, Okay, I think this is worth forking over my data, and potentially even being exposed to a hostile algorithm down the road, depending on the company. So the takeaway here for the marketer is you’ve got to decide what customer data is worth, and then be willing to pay it and be willing to pay a premium for it because as legislation continues as privacy groups get better as consumers get better at understanding how their data isn’t, is not being misused, you will be in competition with your competitors, not only for the sale, but also for the right to the data. And you would better be able to outsell your competitors on the value of what you’re going to give them in exchange for this data. Because it’s, you know, to cater to your point, it’s a sale, it’s just like, buying the product itself, that giving that data over is a sale. And we as marketers have to convince people to sell us their data. Katie Robbert 20:31 Mm hmm. I agree. And, you know, as you know, being on both sides of it is really interesting as a consumer, in some ways, I almost felt completely violated of how dare you know, the thing that I just bought, and then show me ads for it. But I was the marketer, I’m like, Oh, this is great. I have access to consumer data. And so it is definitely that like, push pull of, I don’t know which side is the right side. So there has to be that middle ground of the consumer actively knowing and the market are actively telling. Christopher Penn 21:04 Yep. As with so many things in life, consensus, everything gets somebody say yes, and you’re all good to go. If you got follow up questions about this or any other topic, pop on over to our slack group over at Trust insights.ai slash analytics for marketers, where you can talk with over 1300 other marketers about topics like this or anything else under the sun that’s related to marketing analytics. And if you haven’t already subscribed to this show, please do so go to Trust insights.ai slash ti podcast or just subscribe on whatever platform it is listening on. Thanks for listening and we’ll talk to you soon. want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this episode of In-Ear Insights, Katie and Chris discuss the newly-released Google Analytics 4. At a high level, what should marketers be thinking about? How soon should they migrate? Are some companies better suited for it than others? All these questions and more answered in this episode. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-ga4migration.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses to start you on your AI journey including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes. Visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people. Our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more. Again, that’s Trust insights.ai slash contact. In this week’s in your insights we are talking about the brand spanking new but not really Google Analytics for and I will say not really because they’ve had it in a as a differently named product for last year or so in beta for people to try. And it is now available it you’ve likely seen the notification in your Google Analytics account, if you’ve been in your Google Analytics account recently, that it is now the new shiny object in marketing analytics. So Katie, here’s the thing, people are going to log into the GA account, they’re going to see that banner, they’re gonna go, Oh, crap, what do we do? is this? is this important? How do we handle this? And certainly from a technical aspect, there are some pretty big changes in how the application works. But how should people be thinking about how to manage the change itself? How do they know whether it’s important to them or not? How do they, how they communicate to their stakeholders? Yeah, we should get on this soon or no, let’s wait and see. Katie Robbert 2:04 Well, you know, I think you categorized it correctly, when you call it a new shiny object. And shiny objects tend to be distractions. And that doesn’t mean that shiny objects shouldn’t be paid attention to, but they tend to distract away from what you’re actually doing on a day to day basis. And so in this instance, what we want to discourage people from doing is abandoning their current Google Analytics, in favor of Google Analytics for what we actually recommend. And what Google recommends is that you run both views in parallel and have both views collecting data because they are set up a little bit differently. But ultimately, the outcomes should be the same, like you should be able to get the same data out of both systems, how you go about getting that data is going to look a little different. That’s the difference between classic and for, you know, I think one of the things that we know, just from the work that we do is that a lot of companies are still struggling with the basics of Google Analytics and having Google Analytics set up in the first place. So to have this new shiny object to distract them from getting the basic setup is, to me, it’s problematic, because then that means that the basics won’t be set up correctly. And then they’ll just sort of try to do this new thing over here. But we also know that in technology, while the lifecycle to get things out the door is very rapid. The lifecycle to adopt, the new technology actually takes a few years. And so I’m not so fussed about the fact that Google Analytics four came out, I think people have time, and they should take their time to learn the system. You know, so you asked me a bunch of questions in there, Chris, about, you know, change management, and letting the executives know. But I feel like we need to start with the PSA of don’t abandon Google Analytics Classic for Google Analytics for Christopher Penn 4:02 now, I think that’s right, because version four is a very different creature. There are clearly applications where it makes sense to move sooner rather than later. Like if you have a web property and a mobile app, you’re on the list of sooner rather than later because the measuring capabilities are much better for people who have both. And so that’s, you know, an easy prioritization method for prioritizing whether it’s right for you or not. But the system overall hat is, has is thinking about analytics in a different way. We typically think of it in three or four different ways, right? We think of the hit or the page view, we think of the session the things that people do, in one sitting, we think of the user. And we think of the products those are the four measurement measurements that we look at units of measurement in traditional Google Analytics, as reflected in the old product in the new version. All that has gone is gone to the point where that’s its events only. So something happening is what is considered the the sole unit of tracking. And that’s a very different way of thinking, because instead, the closest analogy I can come up with is instead of like courses have a meal, you know, appetizer, entree, you have dessert, you have drinks and things, and we would measure those things differently and value them differently. It’s like moving to, we’ve moved entirely to ingredients, like, here’s all the ingredients doesn’t matter where the ingredient occurs in the menu, it’s, it’s in there. And then we have to make sense of it. And one of the things I think is going to be very challenging for people in the new version, is that a lot of the canned reports, you know, the old manual long left hand side has a ton of different canned reports, things you could look at, like acquisitions, much of that is gone, it’s been replaced by what’s called the analysis hub, where you can build your own, and mix and match all these different measures together. So it’s a lot more flexible. But it’s I think, in some ways is a lot more daunting, if you’re not already an analytics pro to log, if you’re a, you know, a standard business user who logs in, like once a month, or you’re a small business owner who logs in once a month, just to see what’s going on the website, you’re gonna log in to the new version go. Ah, now what. And that’s why I was asking about the change management because it’s, it’s gonna be a major mind shift for mindset change for people. But it’s also going to be a substantial process change for them to have a totally different way of doing things now. Katie Robbert 6:33 So you’d mentioned that, you know, so let’s say in the example, you have a business owner who goes in once a month. So if this fictional person only goes in once a month to look at their data, why is Google Analytics for such a big deal in terms of change? I guess my question to play devil’s advocate is if people aren’t using classic, why would they bother to use? You know, for it? For a lot of people, it’s just sort of a check the box, yes, it’s hooked up to my website, but then I don’t do anything with it. Christopher Penn 7:04 For people with existing websites and existing accounts, you’re right, that’s not going to be as nearly as big of a deal. But if you are, say, a new business owner, and you’ve just started your own startup, right, and you go into Google Analytics and sign up for a new account, you get four by default. And then when you go to ask for help, people would be like, I don’t know what you’re talking about, what is this events, you know, business, and, you know, just set up goals? And you go in and you look for goals? You’re like, I can’t find goals, what are you talking about, there’s this this thing called conversions, you know, conversion event link, or like what? So there’s a lot of that disparity between the two, what I think we’re seeing is, Google has taken what used to be a monolithic application, and split it into three pieces. So there is Tag Manager, which is now sort of the configuration engine, because the things like goals don’t even exist in Google Analytics anymore. There’s Google Analytics, which is the app analysis engine that does all the processing. And then there’s Data Studio, that is, you know, rapidly becoming the sort of the visualization engine. And I think that in splitting up a lot of those roles and pushing the power of one application into three, Google is changing how its architecture works. And for a lot of again, that, you know, the average user, that’s not something that they have been told, that’s not something that they know, to, to think about the system that way. And so it’s on, you know, people like us companies like ours, just to advise people like that small business owner logs in once a month, hey, Google Analytics probably isn’t the right place for you to be hanging out, right? It is, you should be probably hanging out Google Data Studio instead. Because you can get the data there. But then make the reports that have canned and tuned for your business for what you need them to be. And that’s where I, for any of our clients, I’d say that’s where we would like them to go eventually say, like, Yeah, you do your stuff, and Data Studio will manage the analysis engine will manage the configuration so that you don’t have to, because, frankly, for most people is not the best use of the time. Katie Robbert 9:03 So while we don’t recommend it, you can technically run the correct class at Google Analytics without Tag Manager. Again, we do not recommend that you take that route. It sounds like with Google Analytics for you really can’t run without Tag Manager because Tag Manager in Google Analytics four is where you set up your goals. It’s where you set up all of your events, and all of your actions and things that you want to collect data on. Whereas in classic Google Analytics, you set up all your goals in the admin. And you can run it without using Tag Manager at all. And I think when I think about, you know, training and education, like I think that’s going to be one of the more difficult things because Google Analytics is technical, but you can kind of wrap your head around it when you start to get into Tag Manager. It’s a whole different training course through the Google Analytics through Google Academy, and, you know, it’s containers and variables and you know, regex, and all of those different technical things, or at least they feel more technical to Google Analytics, even though they’re roughly the same level of technicality. And so I think that’ll be one of the bigger shifts and the things in terms of change management that marketers are going to have to wrap their heads around is I not only have to learn Google Analytics, I also now have to learn and wrap my head around Tag Manager. Whereas before I could kind of skate by without having to do too much with Tag Manager, knowing that my data was good enough. But you know, it wasn’t the best practices. I think that that’s part of it in terms of communicating it to the executives. You know, it’s, I don’t think it needs to be an overwhelming or over complicated conversation. It’s, hey, Google Analytics, just rolled out this new updated version, I’m going to set up another view that runs in parallel with what we’re currently collecting. And that at some point, when they shut off Google Analytics Classic, we won’t have lost any data because we’re running this other one in parallel. Cool. Okay, thanks, bye, going to go back to do my job now. Like, it doesn’t need to be a huge thing. And I think that that’s sort of the other PSA is, it’s not a big deal, don’t make it into a big deal. Just set up a new view and like, turn it on, and just let it run in the background and keep doing what you’re doing with classic because, again, that’s going to be around for another few years. We still have, we still know that there’s a lot of companies that aren’t even using any sort of proper tracking system at all. So to suddenly start switching them over to this more advanced thing, it’s just not going to happen overnight. Christopher Penn 11:47 Yeah, exactly. And definitely Google will be keeping classic around, likely for at least a couple of years, possibly longer than that. I mean, they had the old legacy ga around for like five years, the old legacy tracking tags and stuff. So it’s not something people need to worry about being an immediate thing. But what I do foresee happening is obviously, classical, essentially go into maintenance mode. It’ll kept running, but you won’t get the new stuff there. So as new features and innovations come up, you know, that they will likely be put into for. And if there, there are things that would be of clear benefit to your company, you may have to make that move when those features are announced, like you may, for example, want better cross device tracking, if you suddenly find out that 60% of your audience’s on mobile devices, well, now you got to go go that route, or your company comes up with a mobile app like yeah, we’re releasing our mobile app, like great. Now, you were sort of having that change pushed on you a little bit faster. So when we think about the change itself, you know, how does the individual marketer think about? What do they should be doing, like just getting skilled up in terms of working with their teams, or if you work agency side, working at your agency, you know, with your clients, I can say, I’m thinking back to our, our days in the agency world, when we had to be responsible for the training of our own team and helping them communicate changes. And this is a fairly big change, communicate? how did how do people do that gracefully? Because obviously, when I ran the team, I did a poorly. Katie Robbert 13:25 Well, and well, I was gonna say you’re calling it a fairly big change in this big change. And you’re going against what I just said, which we don’t don’t overcomplicate it, don’t overthink it. So that’s number one is, you know, stop overwhelming people with this notion that it’s this big, huge change that they have to do right now. Oh, my God. It think that you know, it was funny. I think that we were saying last week that I felt like it’s kind of like y2k, like everyone’s getting all hyped up y2k. Oh, my God, I have to get prepared. And then you wake up on January 1, and literally, nothing’s changed. And I think this is very much the same. It’s not that you don’t need to, you know, not pay attention to it, you do. But you don’t need to drop everything to suddenly get skilled up on it. So, you know, the Google Analytics certification, for example, I just took, I just retook mine on Friday, so three days ago. And this is post ga for being announced. And the Google Analytics certification is still all based around classic Google Analytics, they have separate trainings for ga four. But if you’re thinking about education, and if you’re thinking about training certifications, classic Google Analytics is still very valid and still very important to get skilled up on and actually having that foundational knowledge of how the current system works is going to help you then transition into ga for eventually, not overnight, but eventually. And I think that that as you think about like if you’re working on education with your team, then I think maybe over the next few months, introducing ga four, there’s training courses available through Google already getting the view setup, but again, it’s a, set it up, let it run in the background. And then, you know, maybe once a month, you pull up both views, and you look at them side by side to say, are they collecting the same type of data? Did I set it up correctly, and then eventually, you start to do more closer comparisons like week over week, day by day to see, am I getting the right thing. And so you don’t just forget the old one and switch to the new one, you have to do that side by side and keep them running side by side. For a while, like when I say a while, I mean, you know, at least like a good year, so to say, I’m confident that this other system is set up correctly to collect the data the same way that the first one was. Christopher Penn 15:51 The one thing I would say might be an alternative is that if you feel like your analytics, infrastructure, and your processes and stuff are not in good condition, this might be a good forcing function like to maybe, you know, speak about as though it could be a bigger deal so that it gets buy in from your stakeholders to say, yeah, we need to reevaluate our KPIs. We need to reevaluate what we measure, we need to reevaluate the quality of our data, and our our marketing technology, infrastructure, we need to re evaluate the training of our team and things. So there’s a potential there, not to cause panic. But to say, we know we have deficiencies, we know we are behind, we know things not working as well, hey, this is a good excuse to make it a thing within the company in order to create those those people and process changes that will lead to growth. And you use g4 as the excuse to say like, Oh, yeah, we everyone saw the notification, we have to do this thing. We know it’s not true. You don’t have to do it right away. But it might be the good idea to get people to say like, yeah, you know, we haven’t looked at our goals in three years. We don’t even know if they work anymore. We just we just kind of look at the website and hope the numbers keep going up. I feel like there’s certainly a good chunk of of businesses out there that are in that condition. Katie Robbert 17:16 I agree with you. I think that, you know, as you’re describing it, yes, I agree with you that it’s a good excuse to get the train back on the tracks. But the other side of me is saying if you have to use this as an excuse to get the train back on the tracks, there’s probably bigger issues going on. You know, you should be evaluating your goals. At least once a year, you should be evaluating your KPIs at least once a year, you should be evaluating your people and your processes at least once a year, probably more often. So if you have to wait for a new version of software to come out to us as as an excuse to get these things working correctly, then perhaps there’s you know, it’s symptomatic of a bigger issue. But for the sake of this podcast, Chris, you’re right. That is a good reason for people to sort of rally around the cause and say, You know what, this is a great opportunity for us to rethink what it is that we’re doing. So let’s use this as the catalyst for that. Christopher Penn 18:18 Yep. So to wrap up, is Google Analytics for the next big thing. It is from Google Analytics perspective. Is it an immediate thing? Absolutely not. Is it something that you should have a strategy around and a migration plan? Yes. Should you be taking Google’s free course to get knowledgeable about absolutely free you have nothing to lose? And should you be looking at your marketing analytics infrastructure on a regular basis and tuning it up? 100% all the time. If you have questions that we didn’t ask answer in this episode about this or anything else, go over to our free slack group at Trust insights.ai slash analytics for marketers. You can chat with up to 1300 other folks were interested in marketing analytics and of course, if you have not subscribed to the podcast, go to Trust insights.ai slash ti podcast. Thanks for listening. We’ll talk to you soon. want help solving your company’s data analytics and digital marketing problems? This is Trust insights.ai today and let us know how we can help you

In this episode of In-Ear Insights, Katie and Chris discuss the balance companies must find between cost and time. When should you outsource products, services, data sources, or analytics? Tune in to find out. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-conveniencemarketing.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights, the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses. To start you on your AI journey, including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes, visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people, our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more, again, that’s Trust insights.ai slash contact in this week’s in ear insights, we’re talking about convenience, and the way that some companies are able to provide value in ways that at first seem like Well, that seems like why wouldn’t you do that yourself? You know, it’s the the version of why would you go to a restaurant, when you can cook the same thing at home for substantially less money. The example we’re talking about is we saw the software company that sells public data and an API to companies that want to bring in public data. And when I first saw that, I said, Well, why would you need to do that? It’s it’s trivial to, you know, bring in data from like the US Federal Reserve Bank or data.gov into these credible sources. And it’s already free. Why would I pay $400 a month or more? to do exactly the same thing? So Katie, why would we? Katie Robbert 1:55 Well, um, you know, some of it comes down to time, some of it comes down to skill set. And some of it comes down to I just want someone to do it for me. So in the example of the restaurant, you know, if you’re, you know, pressed for time, or you can’t cook, then you know, getting takeout and going to restaurants is probably part of your routine, as opposed to buying food and making it yourself. So you replace those costs of buying groceries with buying pre made food already. You know, when we were talking about this last week, I was giving the example of you know, the fruit cut up at the grocery store. So, you know, you can buy a watermelon, a whole watermelon for a couple of dollars, or you can buy a very small portion of the watermelon, but it’s already been seated and cut up and portioned for you. But it’s more expensive, because someone else has already done the work. And for a lot of people that pre portioned already, we’re already having the work done is more attractive, because then they don’t have to think about it. You know, the same is true for like, you know, think about the things around your house, I could get up on a ladder and try to clean my gutters myself. Or I can pay someone, you know, $100 to clean out my gutters better than I can do it. And so there’s this give and take with this, you know, market of convenience. Now, some of this stuff really does come down to I don’t have the skills or I don’t have the time. And then some of it comes down to I just don’t want to, I don’t want to clean my gutters. I’m happy to pay someone else to clean my gutters so that I don’t have to deal with it. And I think when we’re talking about, you know, services within marketing and data, analytics, prepackaged data is an attractive thing to people because they don’t have to deal with trying to figure out how do I connect an API? What is an API? Where do I bring it? How do I clean it? Where do I get it? Do I know how to use it, like, just give me the data, tell me what it says. And then I can go about my merry way. But I feel like Chris, because you are such a do it yourselfer, you probably have a difference of opinion on this. Christopher Penn 4:04 I do particularly since this company offers an API, which is what you’re buying, like so you could not spend $400 a month and you still do the same amount of coding that you’re going to have to do to connect to this company’s API. I think the value of what they’re offering theoretically is that the data itself is is supposedly vetted, but it is all publicly available data from, you know, different governments, the same way that a lot of people pay subscriptions to statistics, instead of, you know, googling for a minute and a half to find the actual underlying data source. And so that the challenge I run into is not so much the the existence of it, it’s the value of property, what is the value of spending, you know, $400 a month to get data that’s already free in a format that is not necessarily prepackaged any better than the original data. But why? You know why? But how much time does that save you? Like does it doesn’t really save you that much, I guess if you’re really bad at googling, then that it would save you some time. But to me, it’s like, I don’t see the time savings there, there’s something set for sure. I see the time savings for and I like you said, it always comes down to time, money and skill, right? Which of the those Do you have the least of tells you what you’re gonna have to do. If you’ve got skill, and you got time and you don’t have money, then yeah, you’re gonna be making it, it’s gonna be all DIY all day, right. And on the other hand, if you have no time, and you have no skill, but you got a ton of money, everything in your life is prepackaged, you know, you somebody probably comes and makes your bed for you. And and that’s okay, there’s a there’s a place for that. There’s definitely a market for that. But where I question is, especially in today’s environment, what is the return on investment of something that’s that expensive, like those, if that was nine bucks a month, like, okay, you know, nine bucks a month. So I can code to one API and Step five, I can see that time savings pretty quickly. But 400 bucks a month, I don’t see the savings there. Katie Robbert 6:04 Do you think that perhaps it’s a misunderstanding of how much work you have to do yourself, versus that return on investment. And so the example that you’re giving Chris is this company who is quote, unquote, pre packaging, data sets, but really, there’s still a lot of setup and developer time needed. In order to access these data sources, which is relatively the same as you just doing it, you know, yourself and getting these free. You know, it sounds to me like there’s perhaps a misunderstanding with this particular company, what they’re, what they’re selling, what they’re giving access to, and how much work you have to do yourself. Do you see the value in? You know, if this company were to say, you know what we will for that $400 a month, we will also set up the API, do the coding for you. And basically, all you have to do is show up, and the data is presented to you in a usable format? Christopher Penn 7:03 I could definitely see the value in that. Yeah. If it was literally, you know, that what’s the the the B2B cliche, is it literally a turnkey solution? And it really was, then yes, because that saves time. The other thing that they could do, that we didn’t see in their marketing, but would be a significant value add would be these data sets have been cleaned. Because we know government data sets have all sorts of anomalies in them. You know, the Medicare data sets really good example, the hospital quality data set 30%. of its missing because Unknown Speaker 7:35 various health report Christopher Penn 7:36 itself, yeah, good self reporting, especially in today’s environment, you know, this data set has been validated for bias, you know, or has the, the provenance and the lineage of the data set has been verified like this is we know, this is good data, we know the state is free of obvious biases, both statistical and human. Those are the things where suddenly $400 a month starts to make a lot more sense. Because if you can say, Hey, I’m going to build a machine learning model on this thing, and I need to be able to show an auditor Yep, that I checked the data set, if you don’t have training in detecting statistical bias, but you can buy from a verified vendor, then yeah, you’ve basically handed the blame off. So so. Katie Robbert 8:16 So let’s walk through some of the things that are a value. In this, you know, I’m calling it this convenience market segment. And so these are services done for you that if you had the time, you could do yourself, or if you had the skill you could do yourself. But the reason people seek these things out is because they don’t have the time or they don’t have the skill, or they just don’t want to do it. You know, one of the things that we do, Chris at TrustInsights.ai, is we will set up somebody’s Google Analytics account for them using best practices, and then make sure all their tags are connected so that they can collect the proper data. And this is something that we always do always kind of go back and forth of where Why can’t somebody do this themselves? versus we’re happy to do this for them? because it keeps us in business? You know, what are some of those things that are are good return on investment, and fit into that convenience market? Christopher Penn 9:13 You know, it’s funny, you bring that up? Because that’s something that I wrestle with all the time, which is, what’s the value of it? Right? I was thinking about that last night while I was shoveling mulch, like what is the value of Google Analytics, by itself, it doesn’t really have any value. You know, it’s just a pile of data. And so setting up correctly is theoretically important, but only if you’re doing something with the data. You know, one of the things that we say all the time is data without decisions is a distraction. If you just collect data, and and you’re obsessing about data, and you’re not actually making any decisions with it, it’s you’re wasting time and money. where the value is for all this stuff, including this, this convenient stuff is does it get you to better decisions faster, right. So if you have a good Google Analytics setup, and a good Google Tag Manager setup and you’re bringing in the data You’re running an attribution model and you figure out sooner rather than later that you’re wasting your budget on Facebook. Right? That’s a decision you can make that has benefit, like, you could say, a week into a campaign. Hmm. That sure looks like Facebook’s giving us no real return on investment because we’re pouring $10,000 a day into it, we’re getting five leads. Right? That’s, that’s an easy decision to make, like, okay, let’s let’s put that spend somewhere else. So when we offer those services, in some ways that those are like, prerequisite services to actually getting the value out of the data itself? And could you get the value of that data value is somewhere else? Maybe. But Google Analytics is pretty thorough, which is one of the reasons why most of the planet uses it. Where I see a role for all kinds of companies, including ours, is making that data valuable by turning it into decisions, like okay, we’re gonna look at this with you and say, This is what you need to do more of this, what you need to do less of this looks like an opportunity for you, this looks like it could be a problem for you. One of the reasons why we see things like SEO and social media analytics and stuff be continued to be prominent in marketing is because there’s a lot of data, people can’t make good value out of it. And they don’t know what to do when you when you hand somebody your standard SEO report out of an SEO tool, their eyes just get really big, like, what do I do, as opposed to when you hand somebody, you know, a boil down report, like fix these five pages that got it? Katie Robbert 11:32 So it sounds like you’re saying and you know, correct me if I’m wrong, that a lot of these off the shelf software tools that are meant to make your life easier, collect the data, automate things, they’re not actually making anything easier for you, they’re just adding more work for you. So therefore, the return on investment and the convenience piece of it is working with some someone hiring someone to help you make sense of the information. So it’s not the data collection, that’s the convenience, it’s the making sense of the data that you are collecting. That’s the convenience. Christopher Penn 12:12 Let’s go back to your grocery store analogy, right? There’s there’s like what four different kinds of shrimp you can buy, there’s like whole shrimp, there is pre cut easy pill shrimp, there is cooked shrimp and then there’s obviously you know, dishes that with shrimp in them. The difference between whole shrimp and Easy Peel shrimp is basically a large knife right that somebody’s gone through and and cut these things in a way that you can peel them a little more easily. That’s some value. But if you can’t cook, you’ve just you’ve still bought $14 of something that that is no good. So when the equivalent there is if we go and we set up Google Analytics and you still can’t make you something useful out of the data. You’ve we’ve saved you a step. But you’re still getting a value as opposed to here’s some shrimp gumbo, or you know, whatever else that Baba less listed in, in Forrest Gump, you know, all the different dishes you could possibly cook. That’s where we all want to go is what do I do? What what what should I be doing? Because to your point earlier, we are so overloaded and so overwhelmed every single day with all this stuff happening in marketing in our jobs in the world at large. What do you do? What? What should you do one of the things that, like I do with the I publish a weekday newsletter on the pandemic, and the thing I try to focus on is okay, out of all this news, what should I be doing differently than I am right now, like there’s a piece last week on on flying on airplanes, like make sure that the vent is pointing at your face full blast, because the air coming through the vents is already been filtered. So you want as much clean air on you as possible. Like that’s something I can do. And I feel like when we talk to our clients, when we hand them the punch list, this is what you need to do. They’re a lot more grateful for that than saying, Here’s your new dashboards. Good luck. Katie Robbert 14:07 Yeah, no. And I think that that’s one of the biggest mistakes that you know, these agencies that are trying to be part of that convenience market are doing is they’re forgetting about the So what, you know, okay, so I’m giving you all of this information. Great. So what and that’s actually the question, Chris, I probably asked you, I asked you every single time you send me a report to review is so what, why, why am I looking at this thing? Give me something actionable to do so that we can pass it off to our clients, you know, and that’s something that we’ll be actually doing for ourselves today. Yeah, and it’s it’s the reason why we called our live stream which is every Thursday at 1pm. Eastern. So what because the goal of making all of these tools work together and convenient for marketers is So what you have to be able to do something with the information that you have, and there is data overload and, you know, with, you know, so Chris and your example of the pandemic, email that you send out every day and the data that you collect, I personally, I don’t read it, because it’s overwhelming amount of information. And unless I have those very tangible things that I can do, I don’t need to take in more information. And I don’t plan on getting on a plane anytime soon. So for me, I’m, you know, I’m the wrong audience, because I’m not someone who can benefit from this additional amount of information. And I think that that’s probably one of the things that happens when a company hires on a marketing analytics agency is they think, well, if I just have more data, then I can do more with it. And then it gets overwhelming. And there’s too much information. And then there’s this disconnect of, well, you agency have brought us more information, but nothing has changed. If anything, we’re just more confused. And the focus should be on that. So what the what can I do, what is the action, and you know, when we go back to the original point of the conversation with this convenience market, when you have these companies that are working so hard to convince you, that your life will be easy, if you hire them, you’d better be true to what you’re saying, My life had better be easier, if I hire you don’t make me work harder, because I will let go of you immediately. Christopher Penn 16:34 And it you bring up a really good point there. Because in the example of the strip, if you’re a good chef, the pre cut shrimp is probably the best middle ground, right? Because you can still see the quality of you can still see, you know, was healthy or not and stuff like that way, if it’s pre cooked, it’s like, okay, you know, there’s who knows how long was cooked, or how well was cooked. But also you don’t want to be sitting with a knife cutting over 500 shrimp every night before service. So that’s a case where you have someone who is skilled, who can take the output of, you know, a time saving step and and get real benefit. There are not that many people who are in that bucket. So for this convenience market, you have to decide what level of convenience matches up to the market that’s out there. If you have somebody who’s a trained data scientist, that convenience API could save them some some time, right? Because they don’t have to get the developers if they’re a developer themselves, they don’t have to write the code themselves. But is it enough of a convenience? That it’s worth it, as opposed to what’s the next step of the process? And can you provide that, even though it will cost more, because every step you take, the cost goes higher, it’s like you can buy green whole coffee beans for like $4 a pound really good ones, then you got to roast them yourself, grinding yourself, turn them into coffee. And when you could just buy a cup of coffee from Starbucks, which is like be 15 beans total, right? And get the same get the same outcome. Obviously, there are way more customers of Starbucks than there are customers of sweet Maria’s on roast coffee beans, right? So for all of us in this industry, we have to think about where is the market? And who’s going to pay for what level of convenience? If I make you a Google Data Studio dashboard, and you never look at it? It’s the same as not having it at all. Right? You may as well have just, you know, spent it on I don’t know, do cocaine and Netflix. Katie Robbert 18:29 Sure. I don’t even know how to respond to that one. You know, I was in I was thinking about this morning prior to us recording. And, you know, I feel like so there’s there’s two sides of it, there’s the companies providing the convenience. And then there’s the companies who are buying the convenience. And so if you are the company buying the convenience, you probably fall into one of a couple of buckets. One is, you know, I just want somebody else to get their hands dirty and do the thing myself and do the thing. So that I don’t have to. The other is I don’t have time to do the thing. But then there’s what I was thinking about was sort of that smart delegation, it’s, I probably have people on my team who could do the thing, but is that the best use of their time? Therefore, can I bring in someone to do the thing alongside my team? And then you know, they can guide them and but my team can then focus on other things. So I think there’s when you’re evaluating this, you know, these conveniences. You know, Chris, to your point about cooking shrimp, you know, what’s that middle ground, and that’s probably the sweet spot for both the service provider and the person buying the service is, you know, give them enough so that they can do the thing themselves, but make sure that you’re providing value for them. And Fun fact, one of my first jobs was actually in a seafood restaurant and so I could clean and peeled shrimp Very, very quickly. So for me, I’m always going to go for the completely unclean shrimp because I can do it myself very speedily. And I would even compete if I thought that that was the thing that would be worth my time. Christopher Penn 20:12 There are competitions for that. Katie Robbert 20:14 So that’s a wrap up. Christopher Penn 20:17 When you’re evaluating vendors of any kind, you’re looking at four factors time, money, skill and motivation. Do you have the time? Do you have the money? Do you have the skill? Do you want to do it? judge each vendor based on where you are, where they are, and find out where they can complement you rather than overlap or deliver mismatched services. And be careful do ask questions like what is the ROI of this particular service? Sometimes you’re going to find out the answer is not necessarily all that good. If you have follow up questions for this, drop us a line go over our slack group go to Trust insights.ai slash analytics for marketers is our free slack. There are 1300 marketers discussing all sorts of analytics and data questions and the occasional funny joke, very occasional. And if you haven’t already subscribed to our show, please go to Trust insights.ai slash ti podcast and subscribe today. Thanks for watching. want help solving your company’s data analytics and digital marketing problems? Visit Trust insights.ai today and let us know how we can help you

In this week’s In-Ear Insights, Katie and Chris discuss shiny object syndrome, blind spots in your marketing technology (especially around AI and machine learning) and how arrogance can lead to substantial technical problems in your tech stack and company culture. How can you avoid pitfalls and blind spots? How do you manage AI and machine learning initiatives, especially if you’re a non-technical manager? This and much more – tune in now! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-techarrogance.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 In this week’s in ear insights, let’s talk about machine learning. And AI. talk a bit about this, and the array of different tools and techniques available to us and why I feel like some folks are doing it wrong. I was talking with a technologist not too long ago, who was bragging about his company is so cool, they got this new model and state of the art and all this stuff, and put it through its paces. And it wasn’t the results were substandard, the results were no better than something you could pick up off the shelf, and do yourself. And in looking at the process, behind the scenes, there are a whole bunch of things, important prerequisites that were missing. Specifically, they hadn’t done any of the basic pre processing, some filtering, some cleaning of the data, some sampling of the data to make sure the data was okay. And as a result, the model that they created spit out garbage. And when went to talk to these folks, there was this almost arrogance to their to their tone, like oh, no, we don’t need to do those things. The software smart after we’ve got the state of the art software, it’s smart enough to handle that. And they said, but clearly it’s not. It’s still spitting out garbage. And they got me thinking about the corporate cultures. And that sort of tech arrogance, it’s not just a guy think it’s very much a tech thing around AI and machine learning and how we can avoid this and how we can prevent this because clearly, this company has spent millions of dollars and thousands of hours, put it together state of the art software that is not state of the art. And that seems like an A, I’m sure their investors are probably not thrilled about this outcome. So Katie, we know that there are a bunch of technical solutions. But how do we fix that people culture of saying Well, I’m clearly you’re the smartest person in the room, I know what I say goes, I must be right. And then seeing results, which are clearly not the best. Katie Robbert 2:11 you slap them. End of story and a podcast. Now you know it, what you bring up is a really challenging issue because you’re trying to change people changing tech is probably one of the easier things to do. Changing people is one of the more difficult things and more often than not the issues that we see within an organization really stemmed from that people problem. And so you know, you’re describing a very typical scenario, the I know best, you can’t change my mind. And there is no immediate solution to that. It is something that at least in my experience takes time to start to change people’s opinion of themselves and the work that they’re doing. And a lot of it comes down to, can you present back to them real life scenarios where this thing is not working? Can you present back to the the data that shows this is the way that you approached it, this was the result. And this is how it’s working. Versus had we done it this way, some alternatives, and this would have happened this way instead, or I did some mock up and projections had we done requirements gathering due diligence, ahead of time, this is the money that we would have saved, you know, so there’s different ways to approach it. And it’s really understanding your audience. So if you’re the person tasked with trying to change the opinion of the person who is high and mighty on themselves and think that they can never be wrong, first of all, I feel bad for you. Because that is a really difficult thing to do. But second, it’s it’s more than just saying No, you’re wrong. You need to get over yourself. It’s really using psychology and reading the person and understanding how they respond. And so it’s it’s not an easy issue to tackle. Chris, Christopher Penn 4:07 how do you handle that that tech bro personality, because in this particular instance, there was a entire area of domain expertise they’re completely lacking, that would have made their lives easier, it would have saved them a lot of money and gotten them better results out of the gate. But the person in question was was so blinded to that. So they could No, no, no, we don’t need to understand the Oh, this particular area of of machine learning. We are software’s better than what we created is better than that. And it is that outright dismissal of the Yeah, this there’s some older stuff. But guess what, that older stuff actually works as good or better. There are conclusive proofs in academic research that, you know, this machine learning model here offers maybe a half a percentage point better performance, then the The old reliable stuff that he has is a little clunkier to work with and is not sexy, you’re not going to win any awards, you’re not going to get any press for it. But it works. So how do you? How do you handle that tech was like, No, no, we don’t, we don’t look at the old stuff. We’re all we’re all in on the new stuff. Katie Robbert 5:18 The first mistake that people make is trying to prove someone wrong. So if you’re dealing with that sort of aggressive personality of I know everything, you can’t tell me otherwise, meeting them where they are, and saying No, you’re wrong, is just going to put them on the defensive, and likely, they’re just going to shut down and say, I don’t have time to listen to you. The stuff that I’m doing is fantastic. And so it’s really more about you know, and none of this is meant to sound, you know, devious or backhanded or malicious. But it’s really starting to, you know, work with them and collaborate with them, you know, okay, so help me understand why this is the best solution helped me understand why this was the one picked, and you didn’t explore other options, because I need to understand and so it’s really working with them to open up and explain and in some ways, justify the decisions that they’ve made. Because you need to also believe in the thing that they’re doing, because you need to help them, sell it or market it or, you know, do customer service on it, or whatever it is. And so showing them that you are part of the team and that you’re on their side. But you need more information to understand it is a really good, almost gentle way to get someone to open up and really start to explain why they made those choices. What you’re trying to do, in some ways, is to like, disarm them a little bit, because again, in my experience that like arrogant tech bro thing is very much a facade. And it’s almost like that armor of, you know, I’m not super confident what I’m doing. But if I present myself as super confident, then nobody’s going to question it. And they may not even be aware that that’s what’s happening. And so starting to poke holes and attack them and say, you’re wrong, and you’re doing it wrong is only going to make the situation worse. You have to approach it. As we’re a team, we’re in this together help me understand why we’re doing it this way. Okay. Chris is now like, Oh, so that’s what you’ve been doing all these years? Christopher Penn 7:26 Well, how does how does the whole shiny object syndrome thing to because that’s the other angle is not only there’s this belief in being correct, but there’s also this shiny object syndrome of saying, you know, this, the new thing must be better. And in this case, the new thing isn’t better. And it’s harder and more expensive than the old thing. Katie Robbert 7:53 So, you know, I think that’s one of the reasons why having an AI, my dogs have, you know, Happy Monday. I think one of the things that’s really important on a team is making sure that nobody is working in isolation. And so that’s why having like, you know, what, I think they call it like peer development, and you know, a review committee, and all of those things are really what help you get out of that, you know, bad habit of the shiny object syndrome, shiny objects are great, new and innovative, things are great. But you need somebody on the team who’s willing to question, Is this the right move for us? Is this the thing that we need to focus on? Right now? What problem? does it solve that we are currently experiencing? Or do we see a solution, and we’ll come up with the problem later. And I think that the shiny object syndrome, quite often leads to, here’s a really cool solution, we don’t have a problem for it. But it’s a really cool solution, if we can just use it, and then we can convince people that they need it. And it’s the absolute backwards way to innovate. And I think that, you know, again, it’s making sure that nobody is working in isolation. So if you, you know, if you’re the CEO, and you don’t have an advisor, or a mentor, or someone that you can bounce ideas off of, then you probably need that person. Because if you’re relying on the people, you know, who report up to you, they may not feel comfortable saying this is the wrong decision. You know, and then if you are on the engineering team, the development team, the IT team, there should be people within your organization who can say, I don’t know that that’s the right thing, or can you help me understand why that is the right thing? Christopher Penn 9:38 What do you do in a situation where you don’t have those size teams? Like for example, for us? There’s there really is and this has been the theme for a good chunk of my career is there’s no one to actually QA the code, right? Because there literally is nobody else who understands within our organization, what exactly is going on behind the scenes? was the same way that the previous place that we both worked where there was, I literally could have said anything and and people would have had to accept it because there was no one to look at and go, that that doesn’t look right. Yeah, that’s it. The outputs are have been good. The results have been good. So it’s not, there’s not obviously a flaming dumpster fire behind the scenes. But at the same time, especially if you’re somebody who is managing a person like me, and you don’t have the technical experience to look under the under the hood and go, huh, it’s all those roaches doing? How do you manage? How do you manage that? Because there may be cases where that the person who’s writing that code may have a blind spot may have that arrogance may have that shiny object syndrome? Katie Robbert 10:43 Well, and again, it comes down to feeling confident to ask questions, and they’re not antagonistic questions, they’re just questions of, you know, so that we can be on the same page and the same team around the thing that you’re creating, help me understand it and explain it to me in such a way that I can then, you know, talk about it on your behalf. And you don’t need to be in the room because I want to be your advocate. So I need to understand it wholly, I don’t have to be able to push the buttons. But I need to understand the methodology, I need to understand and answer questions of why was this the methodology chosen and not something else? And this is a conversation, Chris, you and I quite have a lot so that I can do that in your behalf. And so, you know, if in the scenario where you’re a smaller team, and you don’t have that, then that’s really where you need to rely on those communities that do exist. And so maybe you belong to an engineering slack community, or, you know, I can’t think of the name of the other ones, the, you know, there’s GitHub, and then there’s Christopher Penn 11:48 stackoverflow.com, overflow, Katie Robbert 11:49 exactly. Those kinds of communities are where you could actually do some of that peer review, if you don’t have that built into your organization. Now, that works only if you are willing to take the feedback, if you are, you know, a little bit hard headed and say no, my stuff is great, then you probably aren’t open to doing that. But then you also have to, you know, live with whatever the result is. And I think one of the things that strikes me, as you were sort of describing this, Chris is, you know, I think some of the arrogance is well, the results look great. So why would I do it a different way? Well, how do you know the results are the true results? Or are they just, you know, bias, because they’re the results that you were hoping to get. And I think that there’s a lot to unpack in that. Christopher Penn 12:37 There. There really is. And that’s a case, you know, the classical machine learning example, where somebody built, built this image recognition software to differentiate between a dog and a wolf, and it did great in the lab, failed miserably, the moment went into production. And then once the post mortem was done, and they looked inside the model, the model was looking to see if there was snow in the photo. And if there was snowing the photo is a wolf. And like, okay, so the results in the lab look great, but not so much we see this a ton in medicine, you know, we see it in clinical trials, it looks great in the lab. And then once it goes to trials, it goes that that didn’t perform as expected. The risk there of bias is extremely high. Because, again, we all everyone in this situation, whether you’re in a clinical lab, whether you’re doing machine learning whether you’re just doing your your marketing reporting, you have a built in incentive to have results that look good. Right? That’s that’s just the nature of everything that we do I have an incentive to create software that works. I can’t think of any organizations where there’s an incentive to to fail. And when you’re encouraged to to fail without eventually succeeding. And so the challenge then is, how do you tame that so that it’s still honest API like yes, this thing does fail more often than just like, we have this one client and the software that we built for this client. The first five additions didn’t have error checking built into it. The sixth edition actually has error checking built into it now and works much better. But it took a while to get there. And then I think that happened, because there was no process to build that in place. Katie Robbert 14:23 Well, and that’s a really good starting off point. And so this is something you know, that we talk about quite a bit is process isn’t meant to get in the way of innovation process is there as the foundation for innovation. So you know, Chris, in your example, you’re saying that, you know, five versions of the software didn’t have error checking. And then we finally said, Oh, that’s what needs to go into it. So we updated our documentation and any new software moving forward has error checking built into it. Therefore, when you are presented with a new data extraction challenge, you can say these are the steps that I I need to follow for the basic foundation to know that it will work. And then I can innovate on top of it. You know, and so I think that process is one thing. And then the other is, you know, failure is such a taboo world, taboo word in the business world that, you know, it’s, it’s okay to fail. And it’s actually better that you fail. A couple of times, like before it goes into production, I think that people are, so want their stuff to go to production so quickly, that they don’t do that QA. They don’t do that error checking. And they’re just like, No, I’m confident it’ll work it has to work, it just has to work. If I just like, put all my hopes and dreams in it, it’s definitely going to have to work. And then it fails and then right, and then you kind of look like a fool. But if you would spend a little bit of like r&d time and this is something that companies need to make the decision to set aside for, then you could get all those failures out of the way, and then build something really great that would go into production. Christopher Penn 15:59 So to wrap up, whether you’re dealing with shiny object syndrome, hardheadedness, or fragile tech bro egos, at the end of the day, it’s the people that present the biggest challenges to your outcomes. The technology is easy, and process is straightforward, even though there may not be easy, it’s managing the people. So if you feel like you’re struggling to manage the people, go ask for some help. If you haven’t already done so join our free slack group over here at TrustInsights.ai dot AI slash analytics for markers. You got questions not only about analytics, and a tech and the numbers, but hey, how would you manage this situation? Or, hey, I could use some advice on on this and you don’t have that peer group. come by and join us. We’re happy to have you there. If you have not already done so please subscribe to the show. Go to Trust insights.ai slash gi podcast, subscribe to on Apple podcast, Google podcasts, Amazon, music, Spotify, Stitcher, you name it. It’s over there. Thanks for watching, folks. We’ll talk to you soon. Take care

In this week’s In-Ear Insights, Katie and Chris examine rapid response marketing. How do you react when a major event happens in your market on a very short timeframe? We look at Amazon Prime Day 2020 and how marketers must react on a significant marketing activity in just two weeks. Will marketers make it work? Will they lose out to Amazon entirely? Tune in for a full discussion. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-surprisesincalendar.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 This is In-Ear Insights the Trust Insights podcast. Do you want to use AI in your marketing but you’re not sure where to start? Take a class with Trust Insights and the marketing AI Institute. The AI Academy offers more than 25 classes and certification courses to start you on your AI journey including our intelligent attribution modeling for marketers certification. One membership gets you access to all 25 classes. Visit Trust insights.ai slash AI Academy to learn more and enroll today. That’s Trust insights.ai slash AI Academy to enroll today. Are you struggling to reach the right audiences? Trust Insights offers sponsorships in our newsletters, podcasts and media properties to help your brand be seen and heard by the right people. Our media properties with almost 100,000 people every week from the In-Ear Insights podcast to the most timely and in the headlights newsletters. Reach out to us today at Trust insights.ai slash contact to learn more. Again, that’s Trust insights.ai slash contact. In this week’s in ear insights, what do you do when somebody drops a bomb on you. So for everyone in the e commerce and retail space, this year, obviously has been a little challenging. And one of the biggest days of the year, for a lot of e commerce retailers, particularly those within the Amazon system is Prime Day. Every year, July, August, Amazon drops Prime Day, billions of dollars get moved around. It’s a huge, huge retail dates, it’s practically its own holiday. And of course this year with it being 2020. It did not happen in July, August. So over the weekend, Amazon dropped the bomb, Prime Day, October 13 and 14th, which as we’re recording this exactly two weeks away. So the big question is for everybody in marketing, oh, that was a surprise. What do we do? How do we can we even take advantage of this? For some obviously retailers and things if you know what stock you have in stock is what you got? You got to roll with it. If you’re in the in that ecosystem for marketers, it’s like okay, well, how do we communicate this? Do you if you are in a competing space? Do you even want to attempt marketing on those two days to say, Baggett, we’re done? The holiday break? Katie, when we focus so much on planning, and scenarios, and thinking things through, what do you do when just the the 800 pound gorilla that just sits on you? Katie Robbert 2:31 Well, you know, so I would respectfully disagree with you a little bit about the surprise element. So, you know, Amazon Prime Day has been a thing for, you know, quite a few years. And you know, every year the date is roughly around the same time. And obviously, this year was a little bit different. But the expectation was that there would eventually be an Amazon Prime Day. So I would say if you hadn’t already figured out like what your strategy is what you were going to do for it, then you are behind the ball, and you’re probably screwed. But you as a company, you know, as your as your marketing team should have been anticipating priority is going to come at some point, you know, whether it’s in July, August, September, and obviously now this year in October, like you knew it was coming, you had enough time to say let’s at least sketch out an outline of a plan. So I sort of I call shenanigans on the whole surprise thing, because it was really more of a waiting game of When are they going to announce it not Oh my god, they announced this thing that we had no idea about. So I think that planning and scenario based planning, and all of those things still apply here, because you knew it was going to happen eventually, you just didn’t know the exact date. Now, I think you’re talking about two different things, you’re talking about marketing plans, and then you’re talking about companies who have to have things in stock. Those are, you know, two separate conversations. So if we focus primarily on marketing, and the services that you sell, or the physical things that you sell, you know, you can have a plan for Amazon Prime Day, you know, up to a year in advance and just continue to tweak it as you get more information. And, you know, gets closer. So that’s sort of my take, Chris, do you agree? Disagree? Christopher Penn 4:20 I mostly agree. Katie Robbert 4:22 That’s all I can ask for. Christopher Penn 4:25 So you know, for example, with our company, what’s our what is our take on it normally, when private is happening, we have a plan in place to monitor it to be able to produce some useful data on and things like that we configure our social media monitoring, listening software, generously provided by Talkwalker to, to track it, and things like that and be able to provide insights, particularly for any of our clients that are in that space, to be able to say, Okay, here’s the things that you want to be watching for. Those operations don’t change. It’s just a question. In our case of Do we have the resources and time to be able to spend something like that But the other thing that is always a challenge for anyone in the marketing space and you know, and for us in particular is, what can we do? What should we do that is newer and a little more different a little bit, raising the bar a little bit, even just for ourselves year over year. So we’re not just doing the same thing, hitting, you know, repeat and hoping that it works. From that perspective, when you get a major event with a very short timeline, like two weeks away, that changes the amount of r&d time that you have to work up to the event. Katie Robbert 5:32 So I guess the first question I would be asking myself and my team is, do we have a place in the conversation? You know, so obviously, Amazon Prime Day is going to take over those whole two days, it’s going to take over social media, ads, all of those things. And, you know, do we Trust Insights have a voice in that? Do we need to have a voice in that? Do we need to try to cut through the noise because, you know, what we, you know, quote, unquote, sell our consulting services, you know, things that are a little bit less tangible? Do we need to be a part of Prime Day? I don’t know that we do. But if it’s an opportunity to showcase the type of data collection, and analysis and interpretation that we do, then yes, we do want to have a part in a major event where there’s going to be a lot of data to be processed. You know, so I think that when an event comes up, that you’re like, Oh, my God, we have to be a part of this, I think you really have to ask yourself, do we need to be a part of it? Or do you just sort of have like FOMO, if you’re not doing something for it, you know, so I don’t think that necessarily Trust Insights would be running ads, during prime to have sort of like, here’s our Prime Day deal. Like, that’s just not the type of company that we are. But we would then switch our focus to, here’s what you can do with all of this data to enhance your marketing plans for the next major event next Prime Day, those kinds of things. And so it’s less about us selling something and more of a showcasing what you can do with the information. And I think that companies need to think about it in that respect of what is our place in the conversation during an event of this magnitude? Christopher Penn 7:22 I agree. To your point, it is about showcasing what we’re capable of. And in many cases, for any kind of x, we do this a lot with conferences and stuff. It’s about trying to improve visibility for the company, just to for the folks who are in that space, particularly if it’s a sector where we don’t have a ton of customers, we would want to try and at least you know, wave and attention does. So. Okay, so we’ve established, is there a goal? And this case, there is a goal? Is the goal achievable? You know, we know that something is achievable may not be the Rolls Royce edition, because of the timeline, but it certainly is going to be better than a broken skateboard. So the question then becomes, what do you do with like, how do you how do you take the playbook that you already have that you’ve used in the past that has some ramp time and adapt it? What do you do to be more agile in the in the truest literal sense of the word? Katie Robbert 8:18 Well, I think the you know, so we’ve talked about the goal, and for us, it’s more awareness. And so I guess the next question there is, do we need to try to get awareness during those 48 hours, or the 48 hours after, and that’s drastically going to change the plan, you know, anything beyond those 48 hours after the event has ended is probably a waste of your time people have moved on to the next, you know, thing or whatever people are talking about. And if your goal is awareness, then it might not be worth trying to cut through the noise during the actual event, pre or post event might be the best place to think about it. And that’s obviously a choice that you need to make with your team. And then in terms of being agile, it’s in a case like this. So let’s say we had no idea Prime Day was coming at all. But we had two weeks to prepare, the first thing you do is look around and see what sort of resources you already have. Now is not the time to start to reinvent the wheel, especially if you need to be nimble and agile. So you start to say, you know, what have we done for similar scenarios like this? So if it’s a large scale social event, okay, we’ve done network mapping that we’ve done, you know, and you start to tick off all of the things and think through like, how were those things responded to do people like those things? Do they bring leads in the door? Do people engage with them? Do they bring us more awareness? Do they give us that boost that we want? And so you have to start to go through your inventory and figure out like, how does this align with my goal? My goal is awareness. What are the things that we have done in a similar situation that have brought us that awareness that we can replicate fairly easily with just a different data set. Christopher Penn 10:03 So this is where those two different paths actually do converge the the marketer and the the operations person. It sounds like, it’s just like a restaurant, you’ve got a dinner rush coming in, you don’t have time to order more supplies, you don’t have time to do anything except work with what you got in the kitchen. So who’s on who’s on call that day? What do they what supplies you have an inventory? And what do you know how to make? Does that sound like what you’re describing? Katie Robbert 10:28 Basically? Yeah, so if you have a surplus of, you know, lettuce, but nobody’s ordering salad, how do you make it into soup? Yeah. It’s, but you know, you have to work with what you have. And so if everybody’s ordering soup, then how do you turn it into a vegetable stock to enhance the soup. So that way you are using all of the things you already currently have, not running out to the store and coming up with fresh new ideas with stuff that you’ve never worked with before. And that’s how in a situation like this, you can remain more agile and keep your costs down, especially if your goal is more awareness than it is, you know, people directly purchasing something. Christopher Penn 11:10 How do you balance the the tried and true, which, you know, you can work in a crunch? With the ever, ever increasing demand for innovation in the marketplace? There are people saying, oh, what do you got? That’s new? Katie Robbert 11:28 Well, I think that, you know, that’s a tricky question. Because it’s not that there’s nothing new anymore, a lot of stuff is very just referential. And it’s just a new flavor of something that already exists. And so in our case, the likely go to would be some sort of a network map, for us to see like, who the influencers are, which companies are the most talked about those kinds of things, and you know, it, it might just be, you know, painting in a different color or making it look a little bit more polished. And I think that when you’re in a time crunch, you know, you only have a couple of weeks to do something, trying to, you know, completely innovate, it start to finish, it’s just not likely going to happen. And quite honestly, some of the best innovations are those little tweaks here and there. And then you’re sort of testing it out, seeing how people respond to it, versus throwing all of your eggs into a basket and saying, This is the most innovative thing ever. And then everybody hates it. And so for us, I think that it might be, you know, maybe looking at the data slightly a different way, but the same presentation that people like to see, because for us, at the end of the day, people really like to see their names on a chart, because it’s that like 15 seconds of fame thing. And so why would we reinvent that, we would just make sure that it’s a little bit easier for them to see, hey, look, I was popular that day. Everybody loves that. Christopher Penn 12:54 That’s true, though. I love the philosophy, even if the the person behind isn’t necessarily the best human being in the world, the philosophy that Jeff Bezos offers, which is your focus on what doesn’t change, focus on the things that are going to be consistent no matter what, we know that people love to see the name of lights, right. So that’s clearly going to be part of something that’s in the toolkit. But we also know that retailers in particular, are always trying to figure out, okay, what’s going to be the thing, right, especially this year, everybody and their cousin, appropriately is like, we have absolutely no idea what to do, right? There’s all the data skewed everywhere, things are hot mess, what do we do? And in that light, to your point, instead of doing just the people, one of the other things that would be a relatively minor tweak, would be to see what are the most referenced and shared items on Amazon in the run up to Prime Day to be able to say, Okay, yeah, we know you’re over your data’s garbage. Six months ago, data is garbage. But looking at the last two weeks of data about what’s being shared, or the the, the shares, just relevant to Prime Day, could give us a suggestion like, yeah, this, this thing might be a thing, not because somebody’s talking about it a lot, which is traditional influencer marketing, but because a lot of other people are talking about this thing, and that ability to spot what might be hot for Prime Day. But even more important, what might be hot for the holiday retail season, could be something that would be a value to retailers to say, Oh, that’s actually helpful. You know, I would appreciate knowing what’s going to be hot. So I know it’s a stock. Katie Robbert 14:30 No, and I think that that’s a smart way to go about it. And, you know, obviously, we’re not trying to compete with Amazon’s resources. But if you are a smaller retailer, and you want to know what Amazon is selling, you can say, Well, I have something similar, that’s a higher quality, lower price kind of a thing that your customers may not be aware of. And so it’s an opportunity to get a little bit of a competitive advantage as well. If you’re looking to say well, what is Amazon promoting this whole Season, let me look at what the data says. So I can see if I have something comparable as well. So there’s a lot of different ways to think about it. And for us a lot of different ways to message it, depending on who we’re trying to reach with this information. Um, you know, something that I would be interested in is, you know, which brands which retailers are the most talked about, so that if you’re trying to purchase ad space, you know, where do you want the eyeballs to go? Like, if a lot of those retailers are offering ad space on their websites, that might be a way to think about it as well. Christopher Penn 15:34 Hmm. So it sounds like it’s the same general structure with some some fresh new twists, maybe a slightly different flavor on a familiar dish. You can tell I’ve been watching a lot of Kitchen Nightmares recently. And, and having the playbook having a playbook of some kind in in place so that even if you can’t cook your main dish, you’ve got something to offer when the dinner rush comes in. Katie Robbert 16:01 Well, and I think that that’s the thing that people misunderstand the most about process. There’s this idea that having a process and a structure gets in the way of innovation and creativity. And actually, it’s quite the opposite. It gives you a foundation to work from, so that every time you’re looking to do something new, you’re not having to restart over and over and over again, it actually saves you time and allows you to be more agile and nimble. So when things like this come up, you can say what recipes do we already have? What foundations can we already rely on, that we can just build upon? versus Oh, crap, we have a dinner rush, what are we going to do? You know, and so I think that that is one of probably the biggest mistakes that marketing teams and companies make is not having process at all. And thinking that we have to wing it every single time. And that’s where they kind of get caught in that trap of, we can’t keep up. But having processed and structure is not your enemy. It’s actually what’s going to help you continue to move forward at a very quick pace. Christopher Penn 17:01 Alright, so if you’ve been listening to this, and you’re you haven’t figured out what your prime day plan is, this would be the time to dust off the cookbook and figure out what you do have in stock? What are your recipes, who’s on staff? What do you have to work with? And to make the most of it if you have a place in the conversation at all, so figure out those goals first. For today’s episode, if you have comments about your questions, please go over to our slack group analytics for marketers where you can find up to 1300 different folks who are chatting about analytics all the time. In fact, we’re gonna ask the slacker what people’s plans are for Prime Day now that we know the date and see what other folks are doing. So if you want to see what other marketers just like you’re talking about with regard to Prime Day just hop on over to Trust insights.ai slash analytics for marketers. As always, if you have not, please subscribe to the show over Trust insights.ai slash ti podcast we are available on all the channels including the brand new amazon music channels for podcast as well as nope no Prime Day purchases there. Thanks for watching and we’ll talk to you soon. Take care. One help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help Unknown Speaker 18:09 you

In this episode of In-Ear Insights, Katie and Chris ask and answer the question, “What is the value of analytics?” For many marketers, analytics is seen as a cost center and an administrative burden. What will it take for it to be seen as a benefit, as something we want to do instead of a necessary evil? Tune in now to find out. Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/secure/inearinsights/tipodcast-valueofanalytics.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 In this week’s In-Ear Insights, we’re talking about the value of marketing analytics, a lot of the time when we talk to customers to clients prospective customers, we’re talking about things like you know, Google Analytics and attribution models, all the stuff that I personally love to geek out on. And people are like, Okay, this sounds cool, but I don’t get it. Or I know I need this. My boss says I need this. But it’s an expense. It’s a cost center. So Katie, how do we how do we pivot? people’s thinking from analytics is a cost center to analytics is a value centers a profit center, it’s a decision making tool? How do we get them over the hump to realize the value of analytics. Katie Robbert 1:49 It’s, it’s not a one and done. It’s something that has to be done consistently. And so when you are trying to help your team or your boss, or someone see that collecting data, analyzing data, using data to make a decision is a valuable thing. You can’t just do it once they say no, and then you give up, it has to be a consistent and persistent effort. And so, you know, starting with one metric, so let’s say your overall business goal is you know, more revenue. Great. How do you start to use one metric to demonstrate that your efforts are contributing to overall sales, maybe it’s getting people to come to your website. So you can show that, you know, every month, the number of people visiting our website is growing. And you start to consistently show that to your team, to your bosses, and say, the more people we get to our website, the more likely we are to convert them into sales. And so obviously, that’s a very basic way to look at analytics. But for for companies that are not comfortable using data to make decisions, or it’s brand new to them, you have to start small. And I think that that’s one of the mistakes that, you know, we tend to make ourselves and other marketers tend to make is we try to do too much, too soon. Because there’s so many data points that you can use, there are so many advanced techniques, but for a company that is brand new to or that you have a lot of change management that you have to navigate, start small start with one or two metrics to really get people comfortable with using data to have a conversation to make a decision. What do you think, Chris? What would your approach be? Christopher Penn 3:38 It sounds like working out, right? It sounds like yeah, you can’t just go to the gym once and you’re fit, right? This is like no, you actually I mean, Katie Robbert 3:43 if that were true, I would be I’d be all fat. Christopher Penn 3:48 But in that case, though, the problem is like how, you know, the principles of things like weight loss are pretty simple, right? Eat less exercise more, getting people to actually do that, and endure the discomfort as their body makes changes and adjust to a new reality is really hard, which is why this multi billion dollar industry of you know, kinds of crazy stuff, but also why people don’t do it is because they’re not willing to endure the upfront discomfort until they readjust. And they don’t see that future state of Oh, I’m going to live to see my grandkids right. I’m going to I can walk up a flight of stairs without like falling over. Mm hmm. Katie Robbert 4:29 They want that immediate instant gratification. Right. And I think, you know, Christopher Penn 4:34 again, I don’t have any data to mutely support this so we’d have to go look for clinical studies, but I think these little guys have an ime for those who are listening on audio I’m holding up my smartphone have made that instant gratification thing worse, where people like I expect to push a button and you know, a car arrives in front of my house, a pizza shows up. And when it’s a question of, do I go and do 50 pushups or do I lay on my couch, push a button pizza aircrafts in my door, the pizza tends to win an awful lot. And I think the same thing is true in analytics where, you know, we hear this all the time at conferences, what’s the weather your fast, easy tips for this? Like? Well, you know, what are your fascinating tips for, you know, losing 50 pounds or, or, you know, being able to benchpress 400, there are no fast and easy tips, it’s gotta show up at the gym three times a week. Katie Robbert 5:23 It’s true. And as someone who has been working out for a while, the milestones that I’m hitting are small, but I have to keep looking at the information, my own personal data in order to stay motivated and see, nope, every day I’m in it. So it’s hard to see what’s happening. But when I step out from it, I can see Oh, I have made, you know, x progress, or I’ve hit you know, these couple of milestones. So I think that that’s a really good analogy that, you know, using your data to make decisions is a lot like working out, you can’t just do it once and say, Okay, I’m done, it worked, or it didn’t work. It’s a slow, consistent process that you need to build up and continue to check in on. And I think that that’s exactly what companies don’t do. Because like, get me the ROI in that campaign. Did it work? No. Okay, great. Let’s do something else. There’s not enough information there to make a really good sound informed decision. You’re looking at one data point one time, and you’re moving on and it just like, that’s not how that works. And so Chris, how do we get people to be comfortable with the uncomfortable? Christopher Penn 6:37 Well, I’ll go turn that right back on. Yeah. So what convinced you that you needed to start doing strength training? What said, You know, I want to feel sore every other day? Wake up? What made you think that that’s a good idea? I want to do that. Katie Robbert 6:55 Um, you know, I don’t have a good answer for that. But what I can say is that I’ve had a lot of fits and starts, where I’ve done the Okay, let me do it one time, okay, nothing happened. I’m not suddenly on the cover of Sports Illustrated. That’s weird. Okay, I should just quit right now. I’ve done that a lot. I am definitely guilty of the How can I do this faster? How can I do this easier? How can I do this without actually doing the work and it’s never worked out? Well, definitely not in a healthy way, definitely not in a way where I’ve gotten sustainable results. And so I made the decision that I had to do the hard work in order to make this a habit, make this part of my lifestyle every single day, it’s not, okay, I’m working out, it’s, every morning, I get up, this is part of my everyday routine. And when you do it consistently, it just becomes part of your everyday and you start to miss it when it’s not there. And you can feel when you’re not doing it and looking at your data. And using data to make decisions is very much the same way, if you start to build it into your daily routine, it becomes it’s a habit that when you stop doing it, you’re like, oh, man, I don’t have any data to make a decision with, I don’t know what to do, let me go get some data. And so, you know, I know there’s like different, you know, studies that say it takes X number of days to form a habit, and everybody’s different. And when you have multiple employees, multiple people, like an entire team, or CEO, or whoever, also playing into that, like it’s gonna it’s gonna take a while. So you yourself have to be in the right mindset and make up your mind to say, regardless of the pushback, I’m going to do this every single day. Christopher Penn 8:48 So what do you think those analytics habits are? Because you’re right, when you step outside, and when you’re when you measure the measurement itself, you definitely see it, you know, in one of my discord groups, we just did the hundred pushup challenge. And we over the span of eight weeks, we all strove to follow this workout plan, it hurt a lot. But at the end, you know, being able to crank out 104 push ups in a row is what was really motivating. But even early on, we could see very rapid changes, even though we didn’t like you look in the mirror and see anything different. We could see on the data itself. Oh, last week, I could do 26 this week, I can do 34. That’s a pretty big change. What are the parallels? and analytics? Is it just that you stop feeling uncomfortable? Maybe when you show up at a staff meeting, like oh, I don’t have it. I don’t know what’s going on with the market is gonna hide, you know, as opposed to saying, Oh, actually, I’m ready to talk about our team’s work. Like, what are the other things that tell you? Yeah, I’m on the right track. Katie Robbert 9:48 Well, that’s exactly it is having an understanding of what is actually going on. It’s being able to answer questions, or it’s being able to know where to find the answers to questions and so it’s That comfort level. And so, you know, if you’ve never opened up Google Analytics before, it might, it could very well be overwhelming. And you could say, I don’t even know where to start, I don’t even know what I’m looking for. It’s like going to the gym for the first time you’re like, well, there’s a bunch of equipment. And I don’t know, I guess I’m just going to sit here on the floor and cry. You know, it’s opening your, you know, data software, data collection software, it could be very much the same mentality. And so, you know, pick one thing, pick one machine to start on and start slow. And start with the lightest weight possible just to get a feel for the movement. So you don’t injure yourself, the same is true of your data, pick one or two data points and start consistently collecting those to see, do they go up? Do they go down? Is there seasonality, you know, am I able to collect this consistently? Are there gaps in the data? What am I not understanding and get really comfortable without one or two data points and be able to explain everything around it, and you’ll start to see, well, I can explain this. But if I had this other data point over here, then I could really tell a story. And I could really answered questions. And so it’s slowly building on that progress that you’re making. Instead of trying to do it all at once, like, I can’t walk outside and run a marathon, it’s just not going to happen. But I can walk outside and run a quarter mile. And tomorrow, I can run a little bit further and then a little bit further and then a little bit further. And that’s the kind of approach you need to be able to take. And that’s actually how change management works, too. You can’t just overhaul everything all at once, like Chris, you and I have both been in organizations that have been, you know, bought out and turned over. And when the new company comes in and says we’re gonna make some changes, when they start to just sort of like, turn everything over how well does that work? Christopher Penn 11:58 Oh, it’s fantastic. It’s fabulous. Everyone quits. works great. No, but I think by that point that it’s, you know, you pick something you start with it, maybe that’s probably it is some of what’s missing, when it comes to the communicating of the value of analytics to people is communicating to them, like, here’s what you’re going to get in the first week, and the first month, right, and there’s small gains is something like you’ll be able to open up your analytics application and find an answer, you know, and six months down the road, it’ll look like this, you know, a year from now, you’ll be running a marathon kind of thing. But I don’t know that anybody and you know, our hands up, I don’t know that anybody in our industry communicate that? Well, the end state well of here’s the end state that you’re going for, when you start working out, you have an end state in mind, you’re maybe going to look a certain way, you’re going to wait a certain amount, or you’re going to do a certain thing that you couldn’t do before. And you’re like, Okay, that’s a goal I want to go after. I don’t know that we communicate that and analytics very well. Katie Robbert 13:02 We don’t and and you know, shame on us. Because that is one of the things that Trust Insights that we You and I are supposed to do very well. And I think that’s that continual learning and growing and iterating, you know, for us, you know, let’s say were the personal trainers. You know, for us, it’s second nature, like, Well, duh, if you just do this, if you just do 10 push ups a day, you’ll be great. But we need to step back and explain to the person that we are training, here’s why I want you to do 10 push ups a day, every day, for the next six weeks, here’s what will ultimately happen, you know, you will stop having lower back pain, or you will be able to pick up your child or whatever the outcome is. So it starts with the conversation of what do you the person being trained want to be able to do? What is your goal? Okay, here’s how we the personal trainers, plan to get you there. Here’s why we’re asking you to do certain things every single day. Because building on those, you know, skill sets will help you get to x goal. And that’s exactly right, Chris, we don’t do that. Well, but starting today, that’s exactly how we’re going to be communicating it to those customers and potential customers and our audience members listening who have questions about, you know, I want to be able to measure everything, and I want to use all the data in my 2021 planning. Okay, great. Let’s start with where you are today, we have to assess where you are today to see how long it’s going to be able to take you to run a marathon. Christopher Penn 14:36 And I love the personal trainer analogy because I think there’s other components of that too, that get missed when we talk about analytics. You know, one of the, you know, with the hundred pushup challenge thing. I and one other person in our Discord server, we’re kind of the the leaders, the informal leaders of that nobody appointed us that we just took it upon ourselves to do it. But a big part of that was the accountability part. You know, US setting an all channel message every other day. Hey, it’s Push up time. Here’s today’s workout, you know, and at the end of the day, you’ll roll call, hey, who didn’t check in with their numbers and stuff? Yeah, we always got people like Bill frowny angry faces. But people did it. And the idea of being your personal trainers for someone’s analytics workout, a big part of that probably also is the accountability like, Hey, did you check your web analytics this week? How are you making your progress towards your goals? Have you hit your revenue numbers? Is there something wrong? And I also love the analogy of that as the personal trainer? Because you’re right, when you so when you show up at a gym, they ask you like, what are your goals, like, some people will say, I need to lose 50 pounds. Other people say I want to benchpress 400 pounds, and the path to get to those goals is gonna be very different for each person, one person may be doing a lot more cardio, the person’s like, okay, go pick up heavy things and put them down repeatedly for the rest of you. To do that, you know, if you want to look a certain way, there’s some workouts that will be actually counterproductive to that, that look, because you need a ton of different muscles to pull down. So when we talk about analytics, you know, and those end states people are after, it’s almost like designing workouts for them. Katie Robbert 16:15 It absolutely is, and you know, the accountability piece. So you would ask me earlier, what is different this time around, I have a trainer this time around, because I, I needed that accountability for someone to say, did you do the thing today, and now I am an adult, I’m responsible. But there are some things that I need someone looking over my shoulder, making sure I’m doing the thing, because they’re not fun, I don’t enjoy working out. But it’s something that I need to do to stay healthy. And your data collection, your data analysis is something that you need to do to continually monitor the health of your company, it’s very similar. And, you know, if if we continue with this analogy, you know, working out doing cardio lifting weights is only part of the equation, there’s nutrition and mental health and all of those things that factor in. And so when you’re doing data collection, there’s the people the process and the platforms. And so you have to make sure that everybody is onboard. And in the right mindset, or, you know, there’s roles and responsibilities defined and that you have the right things happening in order to collect the data and the resource. And so it’s a full, comprehensive 360 plan for your data health, just like your physical health is a full comprehensive 360. And having someone who has experience in it, like a trainer is a really good place to start, because they can guide you, you may not need a trainer forever. But starting with someone who knows what they’re doing is probably a good idea so that you don’t, you know, get on the benchpress machine and you know, stack up the weights to 250 yourself in crush yourself Exactly. You know, people who can show you proper form to avoid injury, you know, the same thing to in data, people who can show you the right data to collect so that you’re not overwhelming yourself. And you can use it to make decisions, Christopher Penn 18:16 the right algorithms and the right models. Yeah, no, that makes total sense. Especially because, like with the thinking back to the pushup challenge, there was no equipment, right? It’s just like you is you and the floor. As long as you have a flat surface, you can do it. And what happens in marketing analytics an awful lot is we focus so much on the tools and the technology and stuff, which is like focusing on the machines, the weight room, when really the challenge for almost everyone was the motivation, okay, I’m going to motivate myself to do the push ups, right? I don’t want to I feel like I don’t feel good. I didn’t have my coffee today, whatever the thing is, now, we eventually got to the point where like, yep, like you were saying it became a habit. In analytics, the same thing holds true you don’t need your multimillion dollar machine learning capabilities out of the gate, what you need is the people and the motivation to to do the thing, right to get out of bed and do the push ups. The same thing is true in analytics, get out of bed and and start looking at a piece of data and start trying to use it. Katie Robbert 19:15 That’s exactly it, you know, and so if you can commit to five minutes a day, okay, within five minutes, I could probably do a handful of push ups. And then done, I can move on with my life. In five minutes, you can probably pull, you know, your website traffic data for the day and throw it into a spreadsheet. And so those non equipment workouts and those non equipment, data collection techniques, they exist. Like if you have a spreadsheet if you have a piece of paper, you can hand write it down like is that ideal? No, but it’s a great place to start as you work up to your comfort level of having more advanced equipment and knowing how to use those pieces. So it’s, you know, the hardest part is getting started. And showing up every single day. That’s half the battle. Christopher Penn 20:05 Exactly. So, to wrap up, proving the value of analytics is like proving the value of fitness. It’s a long haul. It’s something that you don’t stop doing and it’s certainly not something that is instant all at once. If you want to chat about having the equivalent of personal trainers drop us a line at TrustInsights.ai dot AI slash contact we promise we will not make you do push ups as your Katie Robbert 20:27 workout no promises are less you Christopher Penn 20:29 ask us to do that. If you’d like to check out the podcast and subscribe to it make sure you go over to Trust insights.ai slash gi podcast. And while you’re there, you can hit up the newsletter at TrustInsights.ai dot AI slash newsletter. Thanks for listening and we’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help Unknown Speaker 20:51 you

In this week’s In-Ear Insights, Katie and Chris discuss what to do with an analytics or data science project goes off the rails. How soon should you call a marketing analytics project a failure? What do you do once it’s clear a data research project isn’t going to meet its goals or produce anything useful? What’s the best strategy for averting such failures in the future? Listen in for what just happened to a bunch of social media data for a research project, and what the next steps would be in this case study. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-sunkcostsandprojects.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn 0:02 In this week’s in In-Ear Insights, sometimes things go sideways, sometimes things don’t work out the way we expected them to. I was working on a large data analysis of Reddit data last week. And there’s a certain point where like, you know, what, not only is not yielding good results, it’s not yielding much of anything except junk. And so the question I asked myself and the question I want to ask you, Katie is, at what point do we throw it all away? At what point should we be willing to say, you know what, we’re throwing good money after bad we’re throwing good time after bad. Let’s candidate versus let’s try and salvage it. How do you how do you balance it? How do you make that that judgment call when you’re running a project, particularly one that’s Uh, you know, a big investment where a lot of eyes are on what you’re doing. Katie Robbert 2:05 So, you know, easy questions on one day morning God. You know, it is it’s a tough call. And it’s usually, in my experience, it’s never been a decision that’s made lightly. And it’s never been a decision that’s made solely by one person. And so the project manager may be the person responsible for moving things forward. But the project manager is rarely solely responsible for making big business decisions, especially when they involve money. And so, you know, it, it. It really depends. It depends on how much we’re talking in terms of a sunk cost. It depends on whether or not you actually set that money aside for research and development. And when it’s not really a sunk cost. It depends on you know, how well planned out the project was and if there’s an opportunity to salvage it. So, you know, if we’re going down the road of, you know, it’s a small, you know, project. And you know, it’s only maybe a few thousand dollars, which is still a lot of money, especially to a small business. You know, you may just have to say this thing didn’t work out, we experimented. The thing that you salvage from there is you take your lessons learned of what didn’t work, what didn’t we do this time around, that we should do moving forward? Maybe it’s, you know, our hypothesis for the research wasn’t really that strong, or we just kind of started like digging around to see what we would find that’s almost always going to end up as a sunk cost. Because if you’re not thinking about what you’re going after, if you’re just sort of like, I don’t know, maybe this might work. Maybe this over here might work. You are likely always going to have some kind of a sunk cost and that’s what you want to avoid. And so unsurprisingly, it always comes back to how much of it have you planned out ahead of time now planning process itself can be a sunk cost because you could go down the road of, you know, planning requirements and starting to do the data gathering and then find out that there’s nothing there. But generally speaking, that planning costs a lot less than actually having a data scientist or an engineer or someone start to do the digging through the data itself. Christopher Penn 4:23 I feel like in the, in this particular example we’re using I think we did go about this backwards in the sense of we came up with a hypothesis of the plan first before doing the exploratory data analysis. And then as we got into the exploration realized, there’s not anything here is there’s nothing in this pile of stuff. It’s all chaff, no, no wheat which I guess is can be challenging because you also don’t want to have your you know, data data scientist at you know, how $500 an hour or whatever. Just digging around data for without a clear goal in mind, either So how do we balance the two? How do you bounce? Like, okay, we we should do the exploration, to see if there’s any idea before came for the hypothesis or we have a plan. And then we start to execute the plan and find out the plan. It goes off the rails very, you know, fairly soon on, how do you balance those two scenarios? Katie Robbert 5:18 So that’s where a lot of like, proof of concepts and beta tests and those sort of experiments come about. Very rarely is proof of concept or a beta test. Just sort of like, I don’t know, let’s just see what we want to get. There’s still a level of planning involved. And so, you know, we spent, you know, 30 minutes, maybe an hour over the past couple of weeks sort of sketching out the outline, putting together a plan for this research project that we wanted to do and then you spent a little bit of time digging around the data to find out is there something there and so we haven’t gone so far down the road, as we’ve fully committed to this is the data that we have to use. This is the structure of the paper, we’ve been promoting it like our sunk costs are very minimal because we were essentially working as if this your initial data exploration was a proof of concept. So I wouldn’t say we did it backwards. We actually did it the way that it’s supposed to happen, because you need to find out, am I going to get the kind of output that I’m after? And so when we talked earlier this morning about here’s what I’m finding, we were able to start to pivot the plan to say, What if we did this instead? So the data is salvageable is still usable, we’re just going to use it in a slightly different context, you know, and we’re in a fortunate position where we own the company and we can do that we can decide to change, you know, the research, we can change the hypothesis, we can change, you know, the topic of the paper, not all marketers are in that situation, and so they are scrambling to figure out well, I’ve already pitched out this concept To all of these other customers and, you know, articles and whatever, and now I have to stick with it. And that’s where you start to get into trouble is when you start to put the horse, the cart in front of the horse. Yes, I don’t know how horses and carts work. And I know that one of them does not belong in front of the other. Christopher Penn 7:19 Well, I mean, that brings up a really good point, because we’ve also had an experience with a client project where there was a very good plan, there was a, there was a survey plan that was rolled out, the survey got done differently, and then you ended up with something that, you know, the that that train had left the station, but it was filled with the newer instead of gold. And so at the end, and now, in this particular instance, it’s in the train is still full of maneuver, and is probably not going to be successful. So should every company should every organization have an r&d department that’s working on this sort of things so that you’re, you’re doing those rapid proofs of concepts all that Time and that you’re not ever left holding the bag of Well, what do we do with this thing, as opposed to being in a situation where we’ve invested $100,000 or $200,000, or whatever in this thing, and only when you’re 60% of the way through the project, you realize, wow, this thing is not going to go anywhere, we basically burned a quarter million dollars for no reason. Katie Robbert 8:18 They absolutely should. And that’s what falls into some of the Agile methodology is sort of those two week iterative sprints. You’re constantly building and iterating. And coming up, you know, at the end of two weeks, you should have some sort of a proof of concept that you can demonstrate to the rest of the team and stakeholders. They don’t, you know, it should be something tangible. It shouldn’t just be a theoretical, you know, for the past few weeks, we’ve been building a bunch of code and you can see it in six months. You need to have something that, you know, I as a non developer should be able to at least look at and go Okay, that kind of looks like the thing that we were talking about, you know, keep moving down this road. That’s not what we asked for, we need to change it. And that way, you’re not investing millions and millions of dollars, you’ve only sunk about two weeks worth of work to then pivot and you’re still at that early point where you can continue to pivot. And then two weeks later, you’re just building on top of the thing in a small, iterative way. And that’s exactly what agile methodology is meant to do. And so a lot of development teams, you know, ones that I’ve managed in the past have always fought for and pushed for that r&d time so that they can do that experimentation. It’s difficult to build into your budget, because it is going to be likely sunk costs, you know, not all of the, you know, research that you know, we do with data or we do with code is going to pan out to be anything to your point initially, Chris, but there needs to be some sort of a structure around it of like, what what am i researching? Am I researching if The new, shiny, you know, plug in on top of our studio does a better version of generating a chart than tableau. That’s a useful thing. And we might find out that no, it doesn’t or yes, it does like, then at least you can say, at the end of, you know, your two week sprint, I found out that I got pretty close. And I just need a little more time or this isn’t going to do what I ever want it to do. So I need to abandon it and move on. And I won’t keep wondering like, well, what if I just spent more time What if I just did this? You’ve already done the proof of concept. And so I do think that it makes sense for companies to build it in but there needs to be structure around it and there needs to be guidelines and guardrails. Even though r&d feels like it should sort of be like a playground. It should but it should have a fence around it. Christopher Penn 10:49 Yes, don’t let your your scientific cobblers run around unguarded. In that context, though, so there’s there’s two things that are really worth Pointing out one. When I look at the like amount of code and research and stuff that we do as an organization, I would say 95% of the research we do never sees the light of day, like it just kind of sits in a folder. And pieces of it definitely do get reused for other projects, like I was working on this one thing, we’re building a recommendation engine, and the prototype was a miserable failure. version two was moderate failure. version three is only slightly a failure. But all those will never see the light of day. So I think there it probably is worth pointing out to folks that Yeah, there is going to be a sizable amount of cost in research. And the second thing is what if you’re in organizations, we obviously have many clients, you know, and we know many folks like this, like in our analytics for marketers community, which if you haven’t joined, go to Trust insights.ai slash analytics for marketers free slack group of over 1200 marketers talking about stuff like this and a lot of the organizations Don’t have an r&d department or if they do, it’s in product and it’s not in marketing. For the marketers out there, what do we do? What do they do? If they don’t have, you know, someone like me who’s just messing around on a Saturday with stuff for fun? Katie Robbert 12:14 Well, before I get into that, let me step back because you had mentioned your recommendation engine version one, version two, version three. My question to you is did you learn something between each version? Christopher Penn 12:29 No, definitely I wouldn’t. Well, how not to make a recommendation and ship it Katie Robbert 12:35 to me that means that it’s not completely a sunk cost because you were able to learn new skills and figure out new techniques and that so that in and of itself, makes it valuable because then you can take that and apply it forward. And so yes, version one, shit the bed version two, kind of crapped out version three. puttered along, it got incrementally better each time. And so I think that’s one of the things that, you know, companies, if they’re like, Oh, well, if I’m not going to get anything useful out of this, then I’m not going to do it. There, the usefulness needs to be in proper context. So you might not have like a widget or a thing at the end of it. But your team is likely going to feel a little bit more fulfilled, because they had a little bit more freedom to mess around. They’re going to learn some new things that they might not have had a chance to learn if they keep doing things the exact same way, every single time because this is how we’ve always done it. So I just want to like I wanted to point that piece of it out too, because I think it’s important, as companies are thinking about, you know, what am I getting out of letting my team do this kind of research and development and experimentation? Well, you’re getting a team that, you know, is being more creative is being more you know, using their critical thinking skills. to that problem solving, and really sort of thinking outside of those, you know, boundaries of this is how we’ve always done it. Now to your question about, you know, usually r&d lives within product, not within marketing. Don’t try to do it all at once we say that with a lot of like AI projects, for example, AI is one of those things that, you know, marketers are trying to experiment with, find a very small use case and do a small proof of concept, something that, you know, you could probably back to the envelope, figure out how much it’s likely going to cost in resource time, in your time in budget, and just sort of work in a small way and start to scale it up. Once you’ve been able to prove out whatever the thing is, you know, if you don’t have proper research and development, then you need to just make like, you need to keep it small and contained. And you need to be transparent about the thing that you’re doing. Hey, I thought that we might experiment with this other way of doing something. So I’ll do it this way too. But I might also try This way just to sort of do sort of like an A B test, and we talked about that a little bit last week when we were evaluating martex facts. Unknown Speaker 15:08 Okay, that makes total sense. Christopher Penn 15:11 I would also say that it’s probably not a bad idea to be a part of an analytics community or marketing community. So you can if even if you don’t have r&d of your own, at least be listening to what other people are doing, asking them what they’re doing, and seeing if there are any lessons they’ve learned that you can import to your organization. Even if you don’t have those capabilities yourself. You know, if you hear somebody like saying, I ran an AV test with Google Optimize, and I got no usable results, like good, that’s something that it broadens your knowledge. No. Oh, yeah. If I don’t have a good testing plan up front, then I’m going to test a bunch of things that don’t matter and end up with any conclusive results. Katie Robbert 15:49 I think that’s a really, really good pro tip for people is, you know, join a community and say, What were your lessons learned from doing this thing and that way You can sort of go into it saying, Okay, this person didn’t put a plan together, let me at least put a plan together and then see how far I can get. I think that that’s such a smart way in sort of what they call it like the hive mind or crowdsourcing like asking people. What did you do in this situation? Christopher Penn 16:17 Yep. So, Katie Robbert 16:19 to wrap up, Christopher Penn 16:21 when it comes to figuring out where something is a sunk cost, and when to bailout ideally, it’s sooner rather than later. And if you structure your plan to accommodate for things like exploratory data analysis things upfront, you will hopefully reduce the amount of sunk costs. And to Katie’s point. Even if something is a sunk cost, there may be salvageable lessons or pieces of code or analysis that you can reuse for more successful projects later. If you’ve got questions about this or any other topic about analytics, join our free slack community go to Trust insights.ai slash analytics for marketers and join over 1200 other marketers talking about things just like this. And while you’re at it, Go to the Trust Insights website again TrustInsights.ai dot AI and sign up for our newsletter where you can learn about this and get fresh data every week. Thanks for listening. We’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems, visit Trust insights.ai today and let us know how we can help you

Based on a question from our analytics community, in this episode of In-Ear Insights, Katie and Chris tackle the challenges of new technology. When a new piece of technology makes a big splash, how do we evaluate it? How should we assess whether it’s right for us, whether it makes sense to pursue it? What are some of the things that can go wrong? Watch this episode to learn the answers, plus the specific AI technology that prompted this question, OpenAI’s GPT-3. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-evaluatingnewtech.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn This is In-Ear Insights the Trust Insights podcast. AI Academy for marketers is an online education platform designed to help marketers like you understand pilot and scale artificial intelligence. The AI Academy features deep dive certification courses of three to five hours, along with dozens of short courses 30 to 60 minutes each taught by leading AI and marketing experts. Jordan Katie Robbert, CEO of Trust Insights in me Christopher Penn chief data scientist of Trust Insights for three of our courses in the academy five use cases of AI for content marketing, intelligent attribution modeling for marketing, and detecting and mitigating bias in marketing AI. The Academy is designed for manager level and above marketers and largely caters to non technical audiences, meaning you don’t need a programming background or background in data science to understand and apply what you learn. One registration gives you unlimited access to all the courses and invitation to a members only slack instance, access to new courses every quarter. Join now and save $100 off registration when you go to Trust insights.ai slash AI Academy and use registration code pen 100 today that’s Trust insights.ai slash AI Academy and use registration code pen 100 today in this week’s In-Ear Insights, we’re going to start off with something totally different. A short poetry reading. The Rime of the Ancient Google, Google has gone to God you cannot conceive it or guess it all the sudden it has happened. Come Come whoever you are, oh, come now is pacing hasten. So you may be seeing what the world is going on what has happened to the data focus podcasts that we all love. The over the weekend, researchers at open AI have published the results of their the newest language model of music called GPT three Theirs was the original one a couple years ago. GPT two was last year, which is something that a lot of folks included, including TrustInsights.ai have used to generate credibly sounding natural language by machine and GPT three has come out and for all of the research and data published so far has is a substantial leap forward in terms of what computers can do to generate language. Here’s another example. This is the poem a future consensus forecasts by the Board of Governors of the Federal Reserve System. No one knows what will come forecast for teller rising power. That is probably not to be deficit of several lines. The Golden future has tourniquets, no one likes it. I love the I love the expression on your face, Katie. But more practically, this is a tool that given a prompt is able to generate more credible sounding data in natural language and so This follows on to a question that we were asked last week on Joseph Jaffe show by Bob Farnham saying, When something brand shiny new comes out, how do you evaluate it? How do you fit it into strategy? How do you know whether it’s it’s worth paying attention to or just another shiny object for the squirrel to chase after for the brain squirrels to go after? So when you think about a new technology, and obviously Katie, you’ve had to deal with me for more than five years now on this front. How do you differentiate between, like the technology being shiny and new and chasing after and Okay, well, where does this fit in? Katie Robbert Well, first, let me take a step back and say I was always terrible at deciphering poetry in high school in my English classes when that was like what we were working on. I was terrible at it. So it’s all abstract. nonsense words shoved together for me. So everything you just said. I definitely had Like the Macarena playing in my head while you were saying it, because I was like, I don’t get any of this. So that being said, when the first thing that I look for when new software or a new version or something comes out, and this might be the, you know, Product Manager in me is I’m looking for release notes, what’s new? And so if I can’t easily see what’s new in a new version, like there should be some sort of a bulleted list, like, what’s new, you know, fixed, you know, the widget bug, you know, changed from blue to green, like whatever the thing is, and also in this case, because it’s more complicated machine learning with natural language processing. I need to have an understanding at a glance of what does it do now that it didn’t do before? Why do I need this version versus the old version? So that’s one piece of it, the other piece of it and Chris, this is something that I always ask you is For the sake of the business, for our business for our clients, do they need whatever the shiny object thing is saying that it’s going to do? Give me a use case show me where we need to spend our time figuring out how this thing works, because I think the challenge with software is that there’s so much of it, and it updates so quickly, and it’s constantly changing. I think the thing that marketers and companies need to ask themselves is, what’s the problem that this thing is solving? And do I need it? Or is it just like cool and exciting and I want to play with it. If the ladder, do it on your own time, and former? Let’s figure out what problems that we currently have, that this thing will solve and then spend your time exploring it? Christopher Penn Yeah, yeah, this is still from a technology perspective. It is still early, it is still in the actual model itself is not publicly available yet. It is researchers who granting access to the private beta or publishing their findings now, but it is not generally available and I’m hopeful that in the next year or so, it will become available to the general public in ways that we can use it. To your point about the release notes it’s interesting because this when a new model is released, that does something substantially different it’s kind of like the original was a bicycle version two was a car and now there’s an airplane sitting in the driveway. And so it’s not necessarily a release notes so much as there’s a brand new thing that does something different that essentially still choose the overall goal of transportation but instead of it being you know, a car you drive it’s a plane you fly. And so there’s obviously a bit more of a learning curve to using it. Where I think it’s really important and like you said, is is understand what is the Unknown Speaker thing do? Christopher Penn Like it’s like it’s a puts a new appliance in your kitchen. Can you broadly tell, like, does it fry things? Does it bake things? Does it blend things like what what exactly is the thing If it’s not clear, then perhaps the the UI needs a bit of work with this model, the thing that fascinated me most of all that I think is has potential incredible both benefits and hazards for our industry is something called neural style transfer. So Katie, if I took your blog posts or the our conversations and embed them into the model as a, as priming as training data, and then I took Harry Potter, and I said, I want you to rewrite Harry Potter in the way that Katie robear speaks and writes, it will rewrite Harry Potter in as if you had written it as opposed to the way that the original author had written. When you think about that, that drastically changes how we think about content. Because what is content at that point, content is the idea that that is created in a certain way, but if you were to use this technology, you would be indistinguishable Theoretically, from your writing, but it also be unique, it would be something that would not be a copyright violation per se, but a derivative work. And so there are massive implications for content marketers, if you were to take, for example, a paper that McKinsey published and you were to do a neural style transfer on that you could create a McKenzie paper that was factually as correct. But in your own words. Katie Robbert It’s it’s an interesting dilemma in the sense of, you know, we go back to the question of do you need this thing? So, for organizations that are churning out large volumes of content, it sounds like perhaps it might be a worthwhile investment. You know, when we speak about and talk about in teach about artificial intelligence, one of the segments of the talks are is always build or buy. Well, how do I know if this is the right investor? For me, and so, you know, you’re talking about hardcore machine learning natural language processing. Let’s say we’re talking about something that’s a little bit more straightforward, like a social listening platform, for example, you know, choose any one of the dozens that are out there. If you’re evaluating whether or not you need this thing, you need to first know, do I have someone on my team who can use the thing? Do I just want to pay a third party company to do the thing for me? And do we have the money? Either way, and I think that those are some of the questions, you know, because what you’re describing with the natural language processing and having it right in my style, so that I never have to sit at my keyboard again, looking at a blank page sounds really enticing. But do we have someone on our team who can first figure out the model into So all of the, you know, learning information content, and then test it and then maintain it. And then you know, do everything with it. So do we have that resource? Do we have the time to do that? To do the r&d? And do we have the money? Because perhaps as an open source model, but you still have to pay someone to do the thing? Christopher Penn Yeah, I would say of, you know, people processing platform, I think your biggest limitation for something like this for any new technology really is the people and their skills because that’s, that is that is either the limiter or the enabler that you have to to leap forward on something. If you don’t have the skills in house. It doesn’t really matter what the technology is, you cannot use it, it’s like, again, if you cannot fly a plane, putting a plane in your driveway looks cool. And you can be proud that you own a plane now, but you still can’t go anywhere with it because you can’t fly. Katie Robbert I will say though, if you’re keeping your plane in your driveway, you clearly You’re doing it wrong, you probably shouldn’t own a plane in the first place. Christopher Penn It depends if you live in the middle and the Alaskan Bush, then you actually do because there’s no roads. But Katie Robbert let’s go with that one. Christopher Penn But so too. I think the other thing that we have to keep our minds open about that a hint is hinted at and Bob’s question is, does this have the potential to have a transformative effect as opposed to an iterative effect on our business? Meaning does this open up new revenue lines? Does this open up new brand new capabilities that not only do we not have, but the industry as a whole does not have something that our old buddy and friend Todd Jefferson used to say is, you know, there’s three things you could do that a newsworthy first best only you can do the first something the best is something that the only one that does something at some something and if you have a technology Do you like this and you have the people and the processes in place, you could achieve all three very, very quickly. And as we all know, from other machine learning and AI projects, there is a substantial advantage to you if you are first mover because the sooner you get going, the sooner you’re collecting data that you can use, and the more of a disadvantage competitors are at. Because they have to not play catch up on the technology has to catch up on the data collection as well. Katie Robbert Mm hmm. Well, and so, you know, it’s tricky, because part of my brain is like, Yeah, absolutely. That makes sense. But the other part of my brain is, what is the likelihood that a marketing team has a skilled data scientist on their staff in order to be experimenting with these new models, that to your point, Chris aren’t even fully available to the public yet are still in their testing phase, in order to be first out of the gate with something that’s easy. A unique position for a team to be in. And so it’s, you know, I sort of struggled with, you know, having the conversation about these, you know, natural language processing models, when I know that a lot of teams are still struggling with some of the basics. Christopher Penn When you deal with a situation like that it like you were saying earlier, it goes back to build or buy, right? If you cannot build you have to buy, right, you know, simply is, is no alternative. And that may mean by an agency like Trust Insights to say like, Hey, we need to engage you as a, as a client to offer this capability because we simply cannot do it. It’s just not in the cards, and there’s not going to be a budget to do that. So for those folks who are on the agency side, the challenge then is are you willing to make a big enough bet in your own agency, to create this capability that you can then sell and potentially offer as a substantial multiplier to your clients? Something that, again, first best only if you’re if you cannot find this capability anywhere else, and it’s valuable, and you know, it’s and it’s worth paying for. Do you? Do you take that bet? Katie Robbert So, you know, it goes. So this is sort of all circling around the question of how do you evaluate something that’s brand new, you know, to the market to the industry, as to whether or not it’s a shiny object or something of value to you. Because for every, you know, 10 people that call something a shiny object, one person is going to say, but I need that. And so the question is really coming down to how do you evaluate, I need that versus that shiny object. And so Chris, a lot of what you’re talking about, is really evaluating. You know, especially if it’s a brand new technology like, you know, what are your business models? What are your clients or customers need? You know, is there something like this already in the industry that would make it a worthwhile investment to you to be first out of the gate, does it solve an existing problem? Or are you looking for a problem to solve with this solution? And and can you make the investment? So those are those are not small questions for someone to ask their company and their team when evaluating something like this. Christopher Penn It reminds me a lot actually have a clinical trial right now phase one, does it cause harm? phase two, does it do anything? And phase three, does it do better than the existing standard of care? And I think that’s not a bad framework for looking at a piece of technology, right does is it something that’s going to actively cause harm to our systems? Does it work at all like, is it hype and vaporware? Or is there they’re there and then three, if there’s a there there, is it better than what we can currently offer? Because to your point GPT three and its cousins. In this language transform a family requires a massive investment in technology. And in brainpower and skills, you, this is not a piece of software like Microsoft Word that you download off the internet and put on your computer. This is and it’s like, literally the engine of a car, like there’s no it doesn’t come with the rest of the car, you have to build the rest of the car. It just you put you’ve got a bigger, bigger, bigger, faster engine. And so that phase three is it may turn out that for your company. It’s not better than the existing standard, because you can’t use it well. And how, when, when in the pharma world, how do they decide what drugs to invest in, like, you know, knowing that something’s gonna be a big better potential dud. You know, I’m looking at the clinical trials process. Katie Robbert I mean, that’s a heavy question. For a Monday morning, you know, it’s well, so a clinical trial depending on the type of trial so I worked primarily in the SP cir world, so small business innovative research. And so those trials, the phase one was six months, the phase two was 18 to 18 to 24 months. And phase three was commercialization. And, but so that right there, you’re already talking about a three year commitment to testing this thing. And that’s for, you know, software, when you’re talking about pharmaceutical drugs, you know, you’re talking about a longer, you know, typically a multi year thing, when we’re talking about software that may or may not intervene with, you know, people’s, you know, medical decisions, that kind of thing. It’s a little less of a time investment, but you should be treating it just as seriously because this is your this is your business. And so the first thing that you should really be thinking about, you know, Chris, to your point is that testing that proof of concept and so the way that I would approach that is the first thing I would do is run numbers, I would find the data both financial and otherwise. Because what I would want to know is if we take the example, this natural language processing software is going to start to write blogs for me, in my voice in my style. Okay. First and foremost, how long does it take me typically to write a blog, and what’s my hourly rate? So that’s one piece of data. How many blogs do I write per week, per month per year? That’s my second piece of data. You know, what, kind of, you know, return on investment do I get from these blog posts that I put out there? That’s my third piece of data. So I now have three hard data points to compare against my investment in this natural language processing software, where I need to know what is it going to cost me to set it up? What is it going to cost me to train it and how long is it going to take before I can replace my own physical writing, with the machine doing my writing and then how much time do I then as the human need to spend editing and publishing this content. And so there’s a lot of different variables that I would want to explore. And that would help me at least in this specific example, understand, is it worth the investment to go down this road? And so perhaps the software can do it better than I can, but it may not be cost effective. So it may not be a better option for me. Christopher Penn Yep. So that fits the it may not exceed the existing standard because using standard is the right balance of efficiency and effectiveness. Right. Okay, so if you’d like to chat about the model itself and things like that, and it’s of interest to you pop on over to our free slack group TrustInsights.ai dot AI slash analytics for marketers will be a topic of discussion. I’m sure that will come up multiple times over the next year. So as tools like this, or models like this or have tools built around them is going to be fascinating and it is absolutely a great water cooler for your other ordinary Regular non crazy far out there analytics questions as well so we have over 1200 folks who are enjoying the occasional conversation on marketing analytics and your follow up questions on this episode please drop a comment over the wherever it is that you’re consuming this or over on our website TrustInsights.ai dot AI and we will talk to you soon will help solving your company’s data analytics and digital marketing problems. Visit Trust insights.ai today and let us know how we can help you

In this week’s In-Ear Insights, Katie and Chris discuss unnecessary reporting. Why does this happen? What can we do to streamline our reporting processes and make sure we’re focused on the most valuable reporting and analytics first? Listen to this episode to learn about change management, KPI identification, and reporting as a security blanket. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-reportingnecessities.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn In this week’s In-Ear Insights we are talking about reporting specifically, what do we do when we’re creating reporting? We are the reporting vending machines as it were in our organizations. But we’re not sure anyone’s actually reading them. So way back in the day, we work for this very large telecom company that the CEO had these 680 page reports that were you know, these gigantic things. We had to print them out at one point we had to print them out every hour on the hour for this company. And the question was that we always just who’s reading this and in the end, it turned out that it really get read. I think we’ve seen it. Many different organizations, people saying, look, we need this report, we need this report. Two weeks a month go by later. Hey, did you read that report? No. So Katie, what do we do? When we have executives and stakeholders, we’re asking for reporting, and then just not reading it not doing anything with it. Katie Robbert We cry and set things on fire. No. So, you know, it’s, it’s a very common issue that we all have run into, at some point. We’ve either been on the delivery side of all of the reports, or we’ve been on the receiving side and just like, Oh, yeah, I meant to get to that I never got to it. You know, and so ultimately, it sort of starts with that conversation of what decisions are you trying to make with this now? In the course of your career, Chris, I’m sure you’ve run into this a lot because I have as well. A lot of it comes down to the person on the receiving end. It’s almost like a security blanket like the more data that they’re given, the more confident they feel, even if they don’t understand it, but they just sort of feel like they’re in that power seat of give me all of the data, I want all of the data, I just want to know that we can pull the data collect the data, even if I don’t understand the data, I want to see that the chart is going up into the right even if it is meaningless, because then it’s like something that if someone questions like no look, data, look, we’re doing the thing. You know, and so I feel like a lot of it is it’s sort of like working out and exercising, it’s actually more of a mental game than it is a physical game. So the physical act of pulling the reports is not nearly as intense as the mental exercise of trying to get someone to understand. You don’t need to drown yourself in data in order to see that things are working. Christopher Penn I’ve never heard reported discuss the talk about as a security blanket, but that is is a really good analogy because yeah, it doesn’t actually serve any function like you don’t need it for warmth, you don’t need it for shelter. It’s literally just a, a emotional support object. So reporting as emotional support objects. Katie Robbert I, you know, I really do think that’s a lot of what it is because it’s this is this might be a little bit of a tangent, and you know, we’ll make sure that I pull it back into the recording and how we audit them. But, you know, a lot of times as people move up through the ranks in their professional career, they’re not often trained properly, it’s just sort of like you’ve been sitting in the seat. Therefore, now you get to move on to the next seat, but there’s no proper training in terms of what that next hire thing looks like. And a lot of times there’s not someone to teach them. Here’s what it’s supposed to look like, here’s what you’re actually responsible for. And so they pull those security blankets with them in the In the context of lots of reporting, because at least if you’re surrounding yourself by reporting and data and busy work and those things, then you look busy, you appear busy and, you know, data and data makes people feel better, even if it’s not doing something Christopher Penn that that makes sense. I mean, if that is really as long as more perception than it is the actual reality, because if you were, if you’re good at something, it actually doesn’t look like much like when you watch someone who’s like a master craftsman, like oh, yeah, bang, bang, done. Like, how did you do that? Is that took you 30 seconds, it would take me like five hours like that’s because you know, I’m an expert, and you’re not. But it’s interesting, because that then conflicts with the perception we have in our culture, have you got to look busy to look important, as opposed to being masterful in your techniques. So when it comes to reporting then what is masterful reporting look like? Because to me In that analogy, masterful reporting looks like two or three simple metrics on a dashboard, like, Hey, here’s the things that you’re actually gonna make decisions on. You need to know more, you know where to go get it. But it doesn’t look like someone’s cartoonish attempt at understanding a drawing like you know, us a spacecraft control panel, which, even that when you look at, like the control module of the SpaceX Dragon rocket, there’s not a lot out there. And by design, it’s only the essentials that you actually need to make decisions on there’s nothing superfluous because don’t cannot afford distractions. Katie Robbert Well, and that’s exactly it. So I don’t know what the dragon x dashboard thing is. But I can only imagine because we’ve all seen those reports that have like, you know, the half rainbow chart and then a pie chart and then a graph that goes up and then numbers and colors and Blinky keys and arrows and all kinds of things. But they don’t tell you anything and so Chris, to your point, a proper helpful report probably only has a couple of metrics on it. But then that then ask the question of, well, if I’m only pulling two metrics, what the heck am I doing with the rest of my time? And that’s sort of that’s where I’m trying to get into with the psychology of it. Because, you know, if you’re a masterful, it shouldn’t take you that long. Therefore, you can do other things. If you don’t know what else to do. That’s a whole separate issue. So, you know, this is something that you know, I always joke about with my friends, it’s like, I can get a lot of my job done in an hour, one hour a day, if I really buckle down and focus but I procrastinate, I get distracted. I work from home, my dogs need stuff, whatever. So it does take me you know, more like five or six hours. But if I really buckle down, it only takes me the one hour and then I’m like, Oh crap, what do I do with the other seven hours? Have my workday? Well, we’ve built a culture where, once you get your work done, you’re good. Just move on with yourself, like move on with your life. A lot of companies don’t operate that way. So I think that reporting becomes that default busywork thing will go pull more numbers, go pull more data, and really good efficient reports. You don’t have multiple pages, and you don’t have to look at everything. six ways to Sunday, because you have that one number that says, it’s working or it’s not. Christopher Penn Right. So how do we start tackling this problem? How do we start figuring out because there’s two aspects, there’s the reporting itself, which reporting is necessary and which isn’t. And then there’s the psychological aspects for the stakeholder. I need my security blanket. And for the producer, if the producer as you were saying with with level of competence that the proof producer isn’t super competent, if they’re not masterful, they may be creating reporting just to look busy. So So where do we Start. Katie Robbert So we start with, I know this will come as a huge surprise to you, we start with, what’s the question you’re trying to answer, we start with a plan. Because what we’ve seen over and over again, is that a lot of times an organization or a team will start with one report, and then someone somewhere else will say, Oh, can you add on this metric? And then someone somewhere else will say, Oh, can you add on this metric as well, and it’s sort of snowballs into, you know, months later, you’re then doing 10 versions of the same report. But you’re so buried in the day to day that you aren’t even aware that that’s what’s happening. And so it really takes a disciplined concerted effort to step back and say, What is the question that we are trying to answer with all of these reports? And are all of these reports absolutely necessary? And are there redundancies in those reports where I can create it once and then send it to multiple people? Or I can create it once and do different versions of the insights depending on the outcome? But I spend more of my time with the actions versus pulling the data. And that’s just something that I don’t think enough organizations and teams are doing is actually stopping. And taking a step back to say, what the heck are we trying to accomplish with all this data? Christopher Penn So what you’re saying is we need the vanilla ice approached, stop, collaborate and listen. Katie Robbert You know, I quote, you know, ice a lot. Because it’s, if you got a problem, yo, I’ll solve it. But no, I mean, but that’s exactly. You have to stop, collaborate, and listen. And the stopping part is the part that I think that scares people the most. Because, well, if I stopped doing this, then I’m not getting my job done. Or I’m missing something or I’m not collecting data. I can’t stop. I’m too busy. I can’t stop this is going on. I can’t stop whatever. And so it’s the actual stopping part that I think a lot of people struggle with, Christopher Penn is when we think about existing webinar on Friday with the folks over at marketingprofs. And one of the questions I was asked was, you know, in the realm of marketing technology, now, how do we evaluate dashboarding and reporting problems, such as I have these problems communicating these things, and I was like, you don’t have a technology problem that’s, you know, you have either sounds like either a people or a process problem. Which is a greater problem in this case to, to deal with, because the stopping problem is both a people and a process problem. How do you how do you cut out that? Katie Robbert Nine times out of 10, fixing a people problem is more challenging than fixing a process problem. You know, the sort of the benchmark that I always use for reporting is if I can’t figure out what this thing is saying in 10 seconds, it’s too complicated. If I can’t make a decision with this immediately, it’s too complicated. And that takes a lot of self awareness on my part of do I even understand what the problem is that we’re trying to solve and Am I over? And so there’s a lot of internal work that I had to do first to be able to get to that place where I said, You know what, what I’m doing isn’t good enough. It’s not efficient enough. Let me try it again. That’s a people problem. The process problem, once you identify should be fairly straightforward of how are we collecting this data? How are we reporting on this data? What is the output look like? What is the frequency and consistency of it? Those are things that you should be able to tweak along the way. It’s the people, it’s a little bit more challenging, because again, you’re talking about the psychology of it. And changing behaviors is a really difficult thing. So if you have executives who are used to getting, you know, 80 page reports, even if they don’t read the whole thing, you’re going to be you’re gonna have a really challenging time saying, we’re no longer going to deliver these 80 page reports. We’re going to send you a single page and it’s going to have all of the information you need. So then they’re going to say, Well, what do I do with the other 79 pages? Where’s that? The rest of that information, how can I make a decision? And so it’s the change management, that’s really going to be the most challenging part. Christopher Penn How do you get the executive to that level of awareness stuff? Because that sounds like Nirvana to be able to say, yeah, here’s, here’s what you need to make a decision based on conversations. And not only will it save you time, because you want to read 80 pages of fluff, you will make better decisions and you’ll make more money. In theory, the the executive should be like done, I’m all in. I know, in reality, like, but where’s my security blanket? Katie Robbert You know, I hate to say it, but I’m, I’m going to, I think it will get worse before it gets better. One of the approaches, this is not the only approach, but one of the approaches that I would take is actually showing them both versions. So you have the 80 page report. So you’ve already pulled the data, it should be fairly straightforward to distill it down to one page. And then you literally have to sort of handhold and walk them through, this is what it says here. But also, if you look at this one page report, you get all of the same information in a streamlined way. And it’s sort of that retraining of it’s okay to only have one page to make decisions on, because you have all of the information here. You know, that’s one approach. The other is, if sometimes working depends on the person is the cold turkey approach where you just say, I’m not doing the 80 pages anymore, here’s your one page report, good luck, you’re probably going to get a lot of resistance because you’re ripping off the band aid really quickly, and it’s painful and people don’t like that approach. So you probably have to take the longer route to get there. But if you go the longer route, the benefits will come long term. Christopher Penn Okay. And then on for the person who’s just trying to look busy, what do we do with them, particularly if they’re not very good at their job? Katie Robbert You know, I think that you start to really challenge them to, you know, document your process. What are you doing? We want to have full transparency into everything that you’re creating, or you give them an opportunity to step up and say, I want you to evaluate all of the different reports that you’re doing, I want you to present them to me as if I’m each different audience member so that I can get a sense of the value of all of these different reports. And that really gives that person the opportunity go, you know, what, you’re not really getting anything out of this report. Or you can sort of see if they’re kind of de essing their way through any of the insights, but really give them the opportunity to evaluate it on their own. And if they can’t, then that’s a different decision. Christopher Penn Yeah, because I, I can definitely see, there are folks that we’ve interacted with who they spend so much time and I’m guilty of this too. They spend more time on the methodology and a whole lot less time on the actual outcome when the stakeholder really doesn’t care how you got the number as long as it’s right. Katie Robbert Mm hmm. I mean, Chris, how many times have you handed me a report? And I’ve made it back to you and said, so what? Christopher Penn Only days I think and why. Katie Robbert So I guess a question for you, Chris, as someone who does generate a lot of reports, what is your what it how do you think about it? Like when you hand off a report? In your mind, Okay, I’m done. Here you go. Like Where? So where is that disconnect of the insights? Christopher Penn It depends, I think, reports feel a lot like farming right? In the sense of you. You have this initial draft, you have this first cut, but it’s not mature. It hasn’t hasn’t grown up yet hasn’t fully matured. And it takes cycles to get to that point of saying, Well, actually, no, we don’t need this part. Well, actually, no, we don’t need this part. It’s kind of like, what’s the old? The old quote that from Michelangelo? Like? Yeah. When asked, you know, how do you create sculptures from a block of marble so I just chip away everything that isn’t the sculpture. Which is not super helpful for the beginning artist, but clearly from his perspective, that’s what he’s doing. And I feel like reports are in some ways the same thing. Here’s the block, is that data, right? And you have to start chipping away at it until you figure out, Okay, these are the essential parts that are left. And that takes lots of feedback loops. Lots of how about this? Well, how about this? How about this until you get to a point where it’s, it’s good enough. And one of the challenges is that if the person you’re working with doesn’t have domain expertise, or or is operates at a certain level, then you and that person will interact in a way that will get to what that person needs. But if you’re trying to create something that is a higher level than either the two of you are capable operating at you’ll you won’t get there because it’s simply beyond your reach. And that’s one things I find myself challenged with when I’m writing code is I can write code to a certain level When building a report, but then if there are challenges after that it’s very difficult to figure out okay, well, what would be the even better way of doing this would be even better way of doing this? That’s, I think the part that, to your point about self awareness is lacking in a lot of reporting, is, you’re not asking the question often enough, well, what’s Is there a better way of doing this? Can I make this faster? Can I make this cleaner? Can I have fewer steps? Or can I make the code more efficient? And it’s hard? It’s really hard to do that. Because you have to know you have to know what you know, and you have to know what you don’t know. And that second bucket is the biggest obstacle I find for me personally, is I? I don’t know what I don’t know. Katie Robbert Well, and so you’re hitting upon one of the challenges is that you know, you’re confident in what you’re doing to say, I want to do these things faster so I can move on to something else. But for a lot of other marketers, there’s that insecurity of if I’m not doing this What else am I doing? And so there’s a resistance. And I’ve talked about this on other podcasts, there’s a resistance to do things faster and better and more efficiently, because then your job is automated away, and what else am I doing? I don’t have any other skills or I don’t have any other responsibilities. And am I automating myself out of a job? And so I think that, you know, one of the ways to sort of combat this analysis paralysis, where you have so many reports around you, and you doing so many reports is really challenge yourself to say, Can I take action with every single one of these data points that I’m putting on a PowerPoint slide? And that’s going to take a little bit time you know, you, you won’t necessarily get it all at once the first time it will, Chris, to your point, take a lot of feedback. But that’s one of the best ways to start with is this report efficient. But it’s also training you to really be thinking how those critters Thinking skills versus just, you know, punching in numbers and typing things and putting charts on a PowerPoint, like it’s giving you the opportunity to level up your thinking. And so when I look at a report, I’m always thinking, what can I do with this information? Can I take action on this? Or is this just awareness for me? So, you know, we know at Trust Insights, yeah, social media is great. But it’s not, it’s not something that we necessarily focus on. So if I’m looking at total number of followers, one of the metrics that I collect every month is, are the social media channels growing. And so you could argue, I don’t do a whole lot with that information. It’s more of an awareness. And so in my mind, it’s a are we growing? are we reaching a wider audience? But I don’t actively take steps with that data so I could stop collecting it. And so that’s the way that I would challenge someone to start thinking about that. tricks that they are collecting and sharing is, am I going to do anything with this information? So Chris, one of the analogies that you give a lot, which, you know, we should probably come up with a better one is it? Well, if you’re not going to, you know, change your behaviors, why are you going to step on the scale and look at how much you weigh? You know, so if you’re not going to start exercising more, or change your diet, why are you looking at the number because you’re just, you know, upsetting yourself or, you know, defeating yourself or whatever the thing is, if you’re not going to change the behavior with the number Why are you looking at it in the first place? Christopher Penn Yeah, I mean, we say it off often. data without decision is distraction. It’s the thing that also comes to mind is that, particularly for the report producers, there’s this mistaken belief that complexity indicates sophistication. And the opposite is true, Einstein said is a great quote, you know, the definition of genius is taking something complex and making it simple. You, as you level up your reporting skills, that’s, that’s what we aspire to, that’s where we want to go is to be able to have report that, yeah, your teenage kid could look at and go, Oh, I know what’s going on. As opposed to having something that is too complex, I get, you know, in an environment where you’re concerned about your job security, making something unnecessarily complex and fragile benefits you by making the organization dependent on you. But is that an organization you then want to work for? As opposed to if you can make something simple and then innovate something new, where you’re continually providing new value to the organization that in the long term makes you a much more valuable employee than somebody who is just, you know, one part in a machine that is easily broken? Katie Robbert Well, and again, you’re hitting upon the psychology of it. And so, you know, I think that we see a lot is people make things unnecessarily complicated to make them feel smarter, make them feel more valuable, make it harder for them to be replaced or automated away. And if you could, in a perfect world shed that insecurity, you could find that you could probably make your reporting a lot more efficient. And then you could spend your time doing other more valuable things, such as taking action on the data. But it’s the psychology piece, it’s the people part of the puzzle that makes it the most difficult. I couldn’t think of another p word for alliteration. Christopher Penn That’s okay. People always the problem anyway. So to wrap up, do an audit of what reports you have and what decisions you make from them. And if the answer is none, you have an opportunity to reduce that reports, production and its workload and be on the lookout for where the problem is in your organization. Is it a process problem, or is it a people problem that you’ve got with too much and too complex reporting, and make your decisions appropriately. If you have follow up questions about this or anything else we’ve talked about in this podcast, please drop into our free slack group go to Trust insights.ai slash analytics for marketers, you can join over 1200 other analytics professionals for free just a chat through all the different questions you have about analytics reporting data and find like that and as always stop by the Trust Insights website over TrustInsights.ai dot AI for things like our newsletter and blog and other fun stuff. Thanks for listening and we’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this week’s In-Ear Insights, Katie and Chris discuss companies and individuals stuck in reporting nightmares. Why do we keep behaving like reporting vending machines, cranking out report after report, instead of focusing on building reporting systems that let our stakeholders get what they want, when they want it? Why are marketers resistant to automation, and what are some first steps towards embracing automation you can take to free yourself from reporting nightmares? Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-reportingautomation.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn Today, in this week’s in In-Ear Insights, we’re talking fundamentally about automation. So we’re having, doing some work last week with a client and looking at this massive spreadsheet of all these reports and spreadsheets and things like that. And in reviewing this, one of the things I thought was, Why are we Why is this this organization creating, you know, one off reports for every single ask, it’s so time consuming, and really not the best use of talent when you look at the collection of requests and say You could just put this in a dashboard, you could put this in like Google Data Studio, or Tableau or Domo, or the dashboarding system of your choice and hand it to the users. As long as it’s well designed, they should be able to figure out what it is with minimal explanation. But then they can self serve. They can get their data whenever they want it as often as they need it. And most importantly, for the analyst, you don’t have to essentially be the report machine. You know, the report then machine people come in and put it on, put a coin in pull, push some buttons, and you spit out a report. So Katie, why is it that organizations and people and and analysts are stuck in this space of I got to be the report vending machine, I got to spit the thing out, I got to do the thing. Instead of taking a step back and going, how can I make a system that will solve these requests forever, which will make everybody happy because I see analysts won’t be spitting out reports like a like a little, you know, crank box toy, and the users won’t have to wait, you know, hours. days maybe to get the information they need so they can make decisions faster. Katie Robbert There’s definitely a couple of different reasons. So one is, you know, depending on how large your team is, you may not realize that that’s what’s happening at all. It may be a, I’m constantly behind the eight ball, I just need to get the thing done. I don’t have time to think about the historical work that I’ve already done to say, I’ve done this report six times over, why do I have to do it again? So that’s one scenario. Another scenario is maybe use the analyst say, Hey, I already did this report for the sales team. Do you want to just use that report? And the marketing team says, No, I have a unique set of needs. I need to know exactly how this affects me personally, do this uniquely for me. And so there’s sort of the that’s the second reason and then the third reason, probably the most common reason is will I automate myself out of a job? And I think that there’s a lot of psychology that comes into this kind of a conversation because You’re talking about basically automating away the thing that you’re getting paid to do. Therefore, if it’s automated and you’re not doing it, what are you doing instead? And I think that that is probably the most common followed by, there is not enough time to step back and see that you already have this wide library of reporting. You know, not everybody knows how to automate, not everybody knows how to pull it into Google Data Studio, tell the story, oh, I can just spin up a dashboard, and it automatically works for me. Not everybody has the right systems in place to do that. So there’s a lot of different factors as to why these things aren’t being done. And once you can understand why, then you can start to solve for how do I fix it moving forward? Do I even want to fix it? So from where we sit? We always you know, we come at it from a different perspective. We always say yes, you do want to fix it, because the more you automate, the more time you have to do other valuable things. tasks, but we’re coming at it from an outside perspective, internally, there may be some hesitation, well, I don’t know if there are other valuable tasks for me to be doing. I used to work with a very large data team. And it was a struggle for years, myself and the director of engineering would approach them probably about once a quarter and say we have some ideas for automation, for how you’re reporting, which takes 60 days from start to finish, for one report, to be automated, and I got constant pushback from the team. And, you know, over time, we finally realized that they didn’t want to automate any of it because then they didn’t have the leg to stand on to say we need more money. We’re doing some very, you know, special, unique snowflake work, whatever the thing was, if we had automated it, the rest of the company would have been able to see that they didn’t need 10 people on the team that they probably only need about two or three. Christopher Penn So let’s let’s unpack that because I think I can see that both ways. When I was in high school, I worked as a summer admin at at&t and one of my jobs there, which was awful was to take 700 pages of printed paper every day, tally up the number of people who had rejoined at&t from like MCI or sprint back at the time on post it notes and then put that together into a spreadsheet that then got email that got printed out and handed off to the my next command. That took seven and a half hours a day. I said, this is really stupid. You’re printing out a spreadsheet, walking it two floors down so that I can rekey in the information. Can you just email me the spreadsheet and this was in the early 1990s. And person was like, what’s email like it’s this in draft coming up. But what ended up happening was I got to the point where this person emailed me the spreadsheet, I built a Visual Basic macro, super simple. Back then the just did the thing for me. And so within five minutes of my workday, I was essentially done with the day’s worth of work, I spent the rest of the day wandering around the building aimlessly, which is great for as a summer intern, obviously, they no longer need that position, because five minutes to work with one push of a button. But the other side is if you stop being the system and you create the system, someone has to maintain the system and the more automation you create, the more complex the system gets until you reach a point where the system cannot function without you as the maintainer and operator of it. And so increases at least from my perspective, increases your job security because now nobody knows how the heck the thing works. Except you and and you’ve clearly demonstrated it’s indispensable. And so I don’t I don’t understand the mindset of if I do this out of my myself out of a job and stuff. If I do this, I’ll be even more indispensable. How do you how do you resolve that? That brain space? Katie Robbert Well, I guess I want to take a step back for a second. And not everybody knows where to start how to automate. So you know, you’re talking about creating macros in my brain, like, do I even know how to create a macro? I don’t know that I do. Or if I do, it’s buried deep within the recesses of my brain. And so, you know, you, Chris are unique in the fact that you approach everything with, I can solve this with code, because you know how to code or that’s just how your brain is sort of wired to think about solving a problem. A lot of marketers aren’t wired that way, or they’ve never had an opportunity to think about it that way. And so that’s where I think the hesitation is, is perhaps they see that automation might be a solution, but they don’t even know where to start. You know, and so we’ve talked with a lot of clients and a lot of other marketers where, yes, they’re aware of Google Data Studio, but they don’t know how to start with a system like that. Whereas to you and I, it’s a very simple system. But we’ve been working with it for a long time. So I think before we can get into how do we, you know, sort of the psychology, where does someone even start? If they know nothing about automation? I mean, Christopher Penn to me, you start with curiosity, you have to have that, that impulse to say, How can I do this better? You know, the rule encoding is you should never copy and paste more than twice, right? If you copy and paste more than twice, twice, you should be writing, you know, a routine before it. But the very beginning of your journey starts with that curiosity of Is there a better way to do this? One of the perennial cliches is there’s an app for that, right. There’s an app for everything. There’s a reason why the MAR tech landscape has an 8000 solutions on it because there literally is an app for that whatever that is. And so, I would say the for the knowledge technical person, the first step on the journey is just to ask the question, is there an app for that? Is there a way to do this in an automated fashion and start googling, you know, even if it’s something as simple as when I do my month, my morning blog post, I have a keyboard shortcut because there’s an app that just lets you store essentially templates, right? And instead of having to copy and paste from a an Evernote notebook or whatever, just hit two keys on my keyboard, it spits out my blog template, okay, that’s not fully automated, but it’s, it saves me 510 15 minutes a day. And if folks start thinking, Oh, what are the things I just do over and over and over again, is there a way to at least automate some of it to start trimming away not, you know, just open up our studio or Jupiter notebook and start writing code? that’s unrealistic. But could I create a keyboard shortcut that just copies and pastes my email signature, I created a keyboard shortcut that would put up a blog post templates. I don’t have to do this. You’ve been doing a tremendous amount with Asana. task management software in terms of like, Here’s just the templates for how we do this sort of thing so that it’s not a machine doing all of it. But it’s a defined process. And there are ways they’re compressed some of the tasks into easily copy paste of things. So I would say that’s the start of it. How do you think about when you’re building a system of processes, making it efficient and, and repeatable Katie Robbert documentation? That’s where I always start. And so because I am someone where I can’t just abstractly visualize something, I can’t start googling for a question. I need to start to write out what the heck am I even doing? And so for me, it always starts with, Alright, let me literally write out step by step. What I am doing in this thing to see, you know, do I do the same stuff more than once and I, you know, we get you and I get to the same conclusion, we just approach it a bit. Because I need to see okay, step one, open your browser. Step two, find this application. Step three, click on this button. And so that’s the way that my brain needs to process it. But once I get that system, you know, set up those steps right now, then I can start to see Oh, okay, now, I’m going to highlight this step here, because this is where I might be able to save myself some time. This is where I maybe might be doing it the long way. It makes me think back to when I was running the development team. You know, we also had QA under us and one of the quote unquote, newer things to the team, not to the industry, in general was automated testing. And so we would struggle with our release dates because the way that the software was built, we had to test it on a variety of different operating systems and browser combination. All the way from like Windows 95, windows, you know, 10, I think we were on or Windows XP, which was a horrendous thing. But we forget about that now. But the point being is that we also had to been tested on Firefox and Internet Explorer and Chrome. And so all those different combinations. And so we had two people in our QA department, testing all of those different combinations plus trying to juggle the priorities of other product teams around the company. And so one day we were talking and somebody said, Did you know that we could build automated test scripts, and we all just sort of took an audible gasp and said, What? And it became this debate of like, Well, you know, can we trust the automation and what does that mean? And so eventually, we were able to come to a consensus that we could automate some of the testing, the very routine testing where you basically go in a straight line through the website and the product, and that the QA engineers would test the new stuff. Because you always need to make sure you’re testing the original stuff to make sure it doesn’t break. And it was an interesting process to go through. Because I learned a lot about the psychology of the team, the insecurities of the team. What happens if I’m automating, but once they sort of got over that hump of automation wasn’t the enemy. It freed up so much more time of the QA team and the development team to do more and more things. And they kept saying, what else can we automate? What else can we automate? And so we almost kind of created this automation monster. But it took a lot of documenting what’s happening, making sure there was a lot of transparency and it wasn’t something that happened overnight. It took quite a few months for us to get up and running with this automation. And I think that’s sort of the other thing I think about when approaching automation is it’s not an instantaneous solution is something that does take planning is something that does take maintenance. You can’t just, you know, declare automation and suddenly everything is working on its own like little, you know, magical machine cogs. Like you have to put a lot of thought into it. Christopher Penn I think it’s a really good example because it speaks to that very binary mindset. A lot of people operate in either you’re not automated or you’re fully automated and you’re out of a job immediately, right. And it’s much more like cooking like you don’t walk into the kitchen as somebody who’s untrained and immediately make a five course meal, right? You start with making toast or boiling some water for for pasta, and you know, yes, there are some people who do manage to burn boiling water, but for the most part, you start small you start in little phases and you work your way up to eventually be able to cook a large dinner or for dinner party, things like that. And automation i think is no different where if you’re trying to go from zero to using Watson Studio, yeah, you’re gonna fail, right there’s you’re not going to succeed because it’s it’s too big a jump But if you go from no automation to, let’s just start copying and pasting our templates instead of manually typing everything. And then you move to one of there’s a app that I can use as a keyboard shortcut to paste my template for me. And then you go to, I wonder if there’s an app that will schedule that for me. So I don’t even have to remember to do that. And you like you said, You ladder your way up. I think there’s two different sorts of personalities, maybe more than two. But I think we, we sort of embody that, well, there’s the process person who can write out the thing, and then diagnose and say, like, here’s where these things go, are clearly repetitive. And then there’s sort of like the technology and tools person who goes, Well, I have a tool for that I have a tool that can do that. I have a tool that can do that. And you kind of need both. Where I think a lot of marketers fall down is that the non technical marketer doesn’t have that buddy in it or marketing technology that they have beers with on a Friday and say, hey, how would How would you tackle this? Right? How would you do this thing Hey, have you ever run across a tool that does this? And I think that’s a great place to get started. is having that person go? Oh, well, yeah, you could just write, you know, a three line macro would do that for you. And then and now you’re starting to have the language you need to be able to say, okay, there’s a thing called macros. How do I start googling for what is a macro? How do I use macros? And that I think will help people start their journeys? Katie Robbert Well, you just hit upon two specific things that I want to talk about. So one, you mentioned templates. Templates are probably the easiest baby step into automation. And so, you know, if you’re creating the same report month over month, and what you’re finding after a few months is the only thing changing is a couple of numbers. Stop recreating it from scratch. And I know that, you know, a lot of marketers might be rolling your eyes like Yeah, no kidding. But it’s amazing where what you know, versus what Do are two very different things and it is a discipline. And so knowing that you should create a template and having an using a template, you know, that’s a really easy step into automation. And you’ll find like, okay, the narrative never changes. The executive summary never changes, the intro never changes. So why am I recreating it every single time trying to, you know, wordsmith it to make it sound new and fun and interesting, when I know my executives don’t even read it. That’s a whole separate issue. That’s a separate thing. You know, so start with a template start with an email template. This is actually something I was talking about with one of our contractors, she said, Well, if you just have an email template, then I can send out the email on your behalf. And I’ll just use the template and I was like, Oh, yeah, I guess I don’t have to rewrite it every single time and then, you know, and it was just one of those like, aha moments. If that will save me, not a lot of time, but it will save me some time. And eventually, these things, these tiny little small moments of automation add up to a bigger amount of time. You know, the other thing, I guess there’s two other things, Chris, that you had mentioned was starting too big. And I think that that’s where a lot of those automation projects or those attempts fail, is trying to automate everything all at once, when really start small and pick those little moments that you feel very comfortable with. Like if I just automate this one thing. That will save me a little bit of time, if I decide to make these two things that will save me some time. The third thing I just wanted to touch upon, Chris, that you had mentioned was, you know, marketers having that friend, that technical minded friend, I think the trap that a lot of us fall into is we think we need to know, all of it solely ourselves. And so if I’m a more traditional offline marketer, and I don’t know a lot about digital, I immediately assume what I know To You know, learn everything I can about digital, I need to learn everything I can about tech in it and solve this problem myself when to your point, Chris, that’s really not true. There are a lot of other people out there who are willing to help and collaborate. And automation a lot of times is a collaborative effort. And so don’t be afraid to ask around. Have Has anyone done this? Or do you know of any tools? Or, you know, whatever the question is, I guess the fourth thing is when I go out for beers on a Friday night, I’m not talking about work. Christopher Penn That’s fair. Well, if we’re just paying for it, you could talk about talk America. How’s that? Fair enough. I think that’s a great point to wrap up on, which is, you know, use the power of the community who have access to and if you don’t have access to a community, I’ll put in a quick plug here. Go join the Trust Insights analytics for marketers Slack, go to Trust insights.ai slash analytics for marketers is over 1000 marketers and they’re asking all kinds of tech To go and non technical questions about marketing analytics, I had a question this morning about how do how do you benchmark a company for Acquisition compared to its peers? Lots of questions. So if you have these technical questions like hey, has anyone ever done x stop on by and asked totally free to join? That’s TrustInsights.ai dot AI slash analytics for marketers. If you have questions about this episode or any other over to the website, TrustInsights.ai dot AI, drop us a line and say hi and we’ll talk to you soon take care want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this episode of In-Ear Insights, take the audio version of Trust Insights Competitive Social Media Analytics class, taught by Christopher Penn. Want to watch the full version? Click here to access it. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/competitivesocialmedianalytics.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Hi, my name is Christopher Penn chief data scientist at Trust insights.ai. And welcome to this masterclass on competitive social media analytics strategy. Well, that’s a mouthful, isn’t it? today what we’re going to be going through is the what the why and the how of using data, data driven competitive social media analytics, for your strategy to figure out how to get more out of your social media. So let’s get started. We’re going to switch things around here. This class is rated, well, it’s not rated but the theory the ideas, the concepts appropriate for everybody. The application, the some of the techniques we’re going to show are really appropriate for the most advanced practitioners of social media marketing, data driven social media marketing. So if you feel like things suddenly get really intense It’s okay, you can go back and watch this as many times as you want while while this is available, and if you have questions, my contact information is available at the end. So let’s talk about competitive social media analytics and just competitive social media strategy. What What do people want? What does what did the executives want? In the 2019 PricewaterhouseCoopers survey of CEOs? They were asked what data do they want? And what are they getting three of these categories data about your brand and reputation, data about risks to which the business is exposed, and benchmarking data on the performance of your industry. peers are all competitive data points, right? These are all competitive data sets. CEOs are saying, you know, 90% 93% 85% of CEOs want this information and see it as critical or important to their work. Of course, how many of them are getting it? 2422 and 18% this is not good news for marketers, right? Because we’re, we’re not delivering what the C suite really desperately wants us to be giving them. And it’s too bad because in so many ways if we had that competitive data that analytics that shed some light on what our competitors were up to, we could be transforming our marketing, even more impactfully then social media from the 2019. The August 2019, cmo survey Chief Marketing Officer survey, CEO CMOS were asked what degree does the use of marketing analytics contribute your company’s performance? And it is rated higher than mobile marketing and social media marketing. So analytics, the use of data and unlocking its secrets is vitally important. When CMOS were asked, what quality knowledge assets do you have that you think a highest competitive intelligence, rated third out of six which is really quite shocking, you know, this is on a scale of one to seven CMOS don’t have the data either or they do. They don’t think it’s great quality. So let’s talk about why this is what is it we’re struggling with as marketers. As social media marketers, we’re having a real hard time proving the value of what we do. Right social media. Again cmo survey makes no headway on contributions to company performance, none. In terms of No, no changes for the better. CMOS believe that social media contributes not really to company performance. This is bad news. If we are the social media managers, right, if we are trying to make a case for increased budgets, more adventurous experiments, we’re not showing that we’re making good headway here. And this is the one that personally drives a stick through my heart. The use of analytics to make decisions has been up and down, it’s kind of going in the right direction. But still, six out of 10 companies are not using data to make decisions. And when we think about a competition, if we’re in that six out of 10 group and our competitions in the four out of 10. And we know that marketing analytics delivers higher impact than social media, we could be in a whole lot of trouble. What’s the impact? Well, no surprise, if our competitors are using analytics to make decisions and watching us and we’re not watching them, we’re going to lose, we’re going to lose money, we’re going to lose opportunity. And worst case scenario might even lose our employment if our if our company goes under. Now, I’m not saying social media analytics alone would would drive us out of business, but it would be symptomatic of greater problems within the organization. So why are we talking about competitive analytics? Don’t we have enough to worry about with our own data? Yes. We do and that’s a topic for another class. But competitive data matters for three major reasons. Number one, we want to understand the conversations, the context, the landscape of what’s happening in our industry. And we can watch ourselves and our audiences is that’s great. But there’s a bias there, right? people interact with us, because they like us, or they like us enough to interact with us anyway. Or they want to have some sort of meaningful interactions with us, whether it’s customer service complaints, or what have you. But it’s all biased towards us. If we don’t offer certain product service. If we don’t think in a certain way, if we don’t appeal to a certain kind of audience. We may never know that the market opportunity is greater. Competitive analytics gives us the ability to see that bigger picture. Maybe, you know, the coffee shop down the street has got lattes, and we don’t we don’t know that that’s missing from our repertoire. Because if we just monitor our conversations, unless a customer says Hey, why don’t you have lattes? We might never Know that Oh, actually, that’s something people want. But if we were monitoring our competitor, and all of them are saying off their customers saying, I love the cinnamon Spice Latte. Well, now we know that something’s missing. So that’s the first reason. The second reason we want to see what’s working. Social media. Marketing is difficult. It is difficult, and it’s getting more and more difficult every day unless you’re just swiping the credit card repeatedly. And you know, putting more dollars into Mark Zuckerberg pockets. So we want to know what’s out there. We want to see the best of the best what’s working, what can we be doing to improve our social media? And one of the best ways to do that is to look at our competitors and to see what’s working for them. Are they doing some things that we could conceptually do ourselves? Are we doing some things that they’re not doing, we need to know what’s working. And third, especially in the process of content creation for social media. Sometimes you get stuck in a rut and you want Find those ideas for inspiration, get a sense of what’s, what’s out there? what’s possible, what are some creative ideas that we could use to maybe add a little pizzazz to the work that we’re doing? So we go as I say, let’s do some competitive analysis. And then we go and do it wrong. And what do I mean by doing it wrong? We treat competitive analysis like we’re spending time on Tinder, right? Just start browsing through profiles, swipe left, swipe right, it looks cool. And doing it very unscientifically, we just kind of fumble around Bumble around I guess, if you want to extend the analogy. And we don’t approach it from a scientific logical perspective to understand what works, what doesn’t and why we should be paying some attention to some things and not other things. It’s super easy. Just look at someone’s profile and, you know, read a few posts and call that competitive analysis. And I suppose it is, but it’s not scientific and we’re talking about data driven, competitive social media analytics. data driven means we make decisions with data. We don’t rely on gut. We don’t rely on instinct. Those can be important later on. But when we’re approaching a problem from a data driven perspective, we want to look at data and then make decisions from it. Remember, only four out of 10 companies are using data to make decisions. So there’s a lot of opportunity in every industry, for you to be the one making decisions with data and getting those extra benefits that we solve because again, market analytics contributes more to company performance, and social or mobile by themselves. So what are we going to need? How do we get started with this can’t just go swipe their profiles, apparently, we’re going to need different kinds of data and as ideally as much as is available about our competitors. So we will need things like what content they’re posting. If we have the opportunity to look at some of the rich media that they’re creating. Link They’re sharing audiences that they’re appealing to engagement metrics, ads and sponsorships, all sorts of information, anything that’s available that’s publicly available without doing anything unethical. We want that data and we want to be able to analyze to understand what is out there. Now, here’s where almost every competitive social media analysis goes wrong. People start gathering all this data, and then they immediately

What data should you look at to decide marketing strategy? How do you use that data with a shoestring budget during leaner times? In this episode, Katie and Chris walk through common Google Analytics metrics that lend themselves well to marketing strategy decisions, and a KPI decision framework. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-shoestringstrategy.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn This is In-Ear Insights the Trust Insights podcast. AI Academy for marketers is an online education platform designed to help marketers like you understand pilot and scale artificial intelligence. The AI Academy features deep dive certification courses have three to five hours, along with dozens of short courses 30 to 60 minutes each taught by leading AI and marketing experts. Join Katie robear, CEO of TrustInsights.ai in me Christopher Penn chief data scientist to Trust Insights for three of our courses in the academy five use cases of AI for content marketing intelligence, intelligent attribution modeling for marketing, and detecting and mitigating bias in marketing AI. The Academy is designed for manager level and above marketers and largely caters to non technical audiences, meaning you don’t need a programming background or background in data science to understand and apply what you learn. One registration gives you unlimited access to all the courses and invitation to a members only slack instance, access to new courses every quarter. Join now and save $100 off registration when you go to Trust insights.ai slash AI Academy and use registration code pen 100. Today, that’s Trust insights.ai slash AI Academy and use registration code pen 100. Today, in this week’s In-Ear Insights, we’re talking strategy data and what to do when you don’t have Oh, I don’t know, a million dollars worth of talent and hardware and software and code and all that stuff. So, Katie, what’s going on? What is the strategy on a shoestring? Katie Robbert Well, I’ve been thinking a lot about, you know, the current situation that a lot of us find ourselves in where businesses slowed down. Maybe it’s because it’s seasonal, maybe because there’s other things going on in the economy. But that doesn’t mean that businesses don’t still need to move forward and figure out their next moves. And so a lot of questions that we’ve been seeing is How do I know what to do? You know, when the world opens up? You know, we’re, as we’re recording this, we’re still in the middle of a global pandemic. And a lot of questions have been like, how do we know what our next moves are? Or what do I do next? And so it really started to make me think well, what resources do people already have or should already have, that will help them reset their strategy without having to spend a million dollars? And my go to and I think Chris, your go to is always will start with the data that you do have. And for us, that’s primarily Google Analytics data, and Google Analytics, you can have a free account tied into your website, it captures a lot of information about your prospects and your customers that can help you understand, here’s what I should do next. And Google Analytics doesn’t cost you anything other than your time. Christopher Penn True There is the minor challenge, though that it is, it’s kind of like a frying pan, right? In that if you don’t know how to cook, and you don’t know what you’re doing with it and things, it has limited utility it is it’s good. And you know, if someone gave me a nice cast iron pan for free, I would not say no to us. But if I didn’t know how to cook, I might, you know, duct tape to the front of the car something as you know, as a very small piece of armor, as opposed to using it for its intended purpose. So when something goes wrong, particularly with Google Analytics, I feel like there’s a lot of opportunity for people to misinterpret the data. So when you’re thinking about marketing on a shoestring now, we like to say if you have time, but no money, you have to learn it if you have money, but no time you have to buy it. What’s the balance? When you look at the average business, where’s the balance, acknowledging the fact that we are biased towards a heavier use of data than most? Katie Robbert Well, if you’re talking about balance, You know, we started this conversation saying like things have slowed down, that then makes the assumption that you have time. Google Analytics a lot along with a lot of other software systems have a lot of really good free training resources. There’s the Google Analytics Academy. So if you’re finding yourself with time, this is a great time to learn how to use Google Analytics properly, how to set up your accounts properly, how to set up Tag Manager properly, this is a great opportunity to do that. So that when you’re collecting data that kind of makes you scratch your head. You have a starting place to say, okay, that doesn’t look right. Let me try to troubleshoot it myself, because I’ve done a lot of this training on my own. You know that there’s a lot of communities where if you just drop in a question, somebody is more than willing to talk at you for hours on end about everything that you’re doing wrong, but within that, you may get some useful information. Not that I’ve had that experience myself. Christopher Penn Well, what I would say, well, in terms of communities, do you want to join analytics? Can you go to analytics for marketers go to Trust insights.ai slash analytics for marketers, and join our free slack community where, if you’d like, we will talk to you for hours. That’s true. Katie Robbert But you know, the question about balance is you should as a company, you should have some data that tells you something about the health of your website, your digital properties, assuming you have digital properties. If you don’t, then you probably have financial data, customer data, transactional data, you should have some of that in some place. And I think that that’s really the starting point is, you know, trying to figure out what do you have? Christopher Penn I’m actually going to pull a you and say, actually, that’s it. That’s not the best starting point. The best starting point is To have a plan and a strategy that that looks at, what is the goal you’re trying to achieve? If there’s no actual gold, and you know, for example, if you run a roadside coffee shop stand in the middle of nowhere. And all the other people who come in are the locals who live in the town. And, you know, there’s no internet access, whatever, you don’t really need to Google Analytics, right? You just you need to be putting out flyers on trees to remind people you exist. But more importantly, and I think this is important, because what goes along with what you said, If you make a list of those metrics from the top of the funnel to the bottom of the funnel operations wise, ask yourself at each stage, you could just just do this on a sheet of paper. Can I measure this in Google Analytics? Can I measure this in Google Analytics? Can I measure close sales? Can I measure you know shopping carts filled whatever it is in your business, and you have to work until you get to a point where you say yes, so like for us, we’re a b2b business. We can’t measure sale in Google Analytics of like our consulting services, we can actually measure a lead that’s been generated from someone downloading a white paper. So for us, we have to figure out what are the things we can measure? And then what are the things that are beyond that measurement, we have to do inference. And if you don’t have that map, you run the risk of trying to try to measure things you can’t measure, which we’ve had a lot of conversations with, you know, prospective customers saying, Well, how do I measure this? Well, you can’t, or you measure too much of the wrong stuff. And you’re like, here’s my dashboard with 500 widgets and dials and, and things and your stakeholders go, I don’t see why this is important. Katie Robbert So Chris, from your perspective, how do you solve that problem? Christopher Penn It really is like the gap, that sheet of paper and you start mapping, you know, from the top of the funnel to the bottom of the funnel, here’s the data I have, like you said, here’s what we have. Here’s what we know. Can I measure this in Google Analytics? If this is the tool that you’re going to use? Can I measure this in Google Analytics? Yes or no? To your point about learning, the things you can measure, you say yes, the things you can’t measure, you say no, but it’s a good chance. For many marketers, there’s gonna be a bunch of I don’t know if I can. And I think that’s the best place to start your learning journey from a professional development perspective is there’s gonna be a whole bunch of I don’t know, is filling those I don’t know it was go learn that specific thing. Go to Stack Overflow, go to Google Analytics forums, go to analytics for markers and ask, Is this something that you could measure in Google Analytics? And if it is great, if it’s not, how could I start to pick away at that? Katie Robbert So to bring it back to the original topic of strategy on a shoestring. One of the reasons w

In this episode of In-Ear Insights (the @TrustInsights podcast), Katie and Chris discuss generation gaps, audience demographics, and what to do when your analytics indicates you’ve got the wrong audience. How do you conduct market research and then pivot based on the results? Tune in now to find out. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-audiencemarketresearch.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn In this week’s in ear insights we are talking about the generational gaps and 10 changes in audience data. How do you understand what a group of people that you’re marketing to is thinking? If you are not in that group of people? Katie, you want to set the stage for how we came about this? Katie Robbert Yeah, absolutely. So we were asked by one of our clients last week about they’re starting to think about their 2021 planning, and they’re trying to figure out how to propose certain initiatives and within that comes from research and They are a company that you know is business to consumer. So they’re trying to reach younger generations, the you know, 18 to 30 year old, the 30 to 40 year old, and trying to understand those behaviors and where those particular sets of consumers get their information and how they behave online. And it started to go down this conversation of they have a desktop website that’s a little bit old and clunky, and no real mobile experience, no real app or anything. And so we started doing that research. And we very quickly realized that the younger generations they’re trying to target are 100% on mobile. And so that comes up with a really interesting question of, you know, how do you pivot to reach that audience? Or will you even because is your product something they even want or are interested in and so, it just started, you know, making me think As someone who I’m personally not great on social media, it’s just not my thing. I don’t really, really enjoy it. But there’s all these new social media apps, you know, you have Tick Tock and I mean, Lord knows, I’ll probably name a bunch of things that aren’t even real things. And so it just sort of got the wheel spinning of like, how do you reach those audiences in an authentic way where they’re not looking at it going, Grandpa cut it out? Like this is my thing. Christopher Penn Well, what do you do this? How do you how do you develop empathy for a group that is not you? So you know, obviously, there’s some things for example it with with all the different current events, I will never and you and I will never know what it is truly like to be a black American, right? It’s just simply because we are not that thing, but we can develop empathy. So how do you develop empathy in a repeatable, almost scientific way? Katie Robbert It’s a lot of fun. listening. And it’s listening to actual conversations, not just reading what people have posted online, because there’s a lot of missing, you know, inflection in the terms, there might be sarcasm, there might be sadness, there might be different emotions that you can’t get necessarily from just reading something. So you actually have to take the time and put in the effort to stop and talk to these audiences that you’re trying to target. And so if I was the client, and I said, I want to bring on the 18 to 25 year old, you know, audience segment, I need to find a bunch of 18 to 25 year olds and talk to them and learn their behaviors. Because I might be targeting them and trying to learn how they behave online. But ultimately, they might say, I’m not even interested in your product. Why are you Why are you bothering with me? This is not how we are operate. Um, one of the reasons why this came up for me was I saw, you know, a friend of mine was selling his car and he has two teenagers and someone had made the comment. Why don’t you just hold on to the car for them as a first car? And he said, Oh, well, they don’t see a reason to drive. They have Uber. They think driving is for old people. And it was one of those moments where it just sort of, I filed it away for later because it was Oh, okay. The younger generation really does do things so drastically differently from even my generation. And I’m what like Gen Y don’t really know. But like, you know, I don’t feel like I’m that far off from that age group. But yet somehow we’re, you know, millions of miles apart. I don’t know. I mean, Christopher Penn I don’t feel like you are millions of miles apart in. One of the things that always I keep in the back of my head for situations like this is what you know, Jeff Bezos built Amazon. Which is focus on the things that don’t change, like the how you get around has changed for that generation, for example. But the fact that they still need to get around has not changed, right? So they still need to get from point A to point B. So if your market is people who drive cars, then yes, that that 18 year old Tiktok sensation may not be the person but their driver sure is because they need the services to keep their their car in good running order. But I think it it’s when you step back, there’s the market research aspect, which is super important, but I think you’re right. There’s also the the persona level research where you maybe, you know, so some of the higher end market research firms do this. They have people who just shadow your subject audience for a day, you know, they sit in your house, and you obviously get paid to show up today, but just observing how this Done. Edison research, did a phenomenal piece on smart speakers watching people how use how they use them, and had people in you know, of people’s homes and stuff and discovered like for a certain segment of the audience, predominantly elderly people, those smart speakers are actually conversation companions like they they do something to, you know, hold off loneliness and stuff. And this was just fascinating to see that behavior again, focusing on something that doesn’t change, you still want some kind of interaction with your environment. So, when we look at something like a tick tock, for example, when you look at that, what do you see that is consistent with human behavior? Katie Robbert So first off, disclaimer, I have never opened tik tok in my life. I don’t have it as an app on my phone. And it was only recently I’m still like, Is it like vine? Is it Instagram? I don’t know. So you know, but the The commonality is that sense of community that sense of? Well, part of it is the look at me look at me generation wear, look what I can do. I tied my shoes, I have to put it online. That’s something I’ll never understand. And that makes me an old fuddy duddy. But like, it is it’s that sense of connection and community. That is the common thread through all social media. Christopher Penn is there’s a lot of self validation. Yeah. And to your question, it is an awful lot like mine. Good. So then as a marketer, if people are still looking for validation, if people are still looking for the approval of their peers, if they are still, some of them are aspiring to be the next Kardashians or whatever. How do you then work with that if you’re if you are trying to essentially recruit these people to your audience and your product or services important but seen as not essential by that bad group of folks. How do you connect the bridge? How do you use empathy to connect that bridge? Katie Robbert Well, it sounds like you’re starting to go down the road, a little bit of influencer marketing and using Tiktok as your source of influencers versus, you know, Instagram or Facebook. But, you know, it’s, again, I think it all comes down to having that conversation of do we even have anything that you and your peers would be interested in? You know, what can we do differently? And I think that that’s a question that a lot of companies Well, first of all, I don’t know that a lot of companies are reaching out directly to talk to those folks. I mean, we know that there’s still a large portion of companies that aren’t even using data to make decisions. So I would be surprised if they were also then creating focus groups of different audience segments to figure out you know, your thoughts and opinions and behaviors, but in the event that they are, I think it’s asking the question of We even have anything that you’re interested in. You know, you mentioned Edison research, another great company that focuses on user behavior is IDEO. And they’re out of Boston. And they spend an enormous amount of time shadowing people, and just watching how they go about their daily, you know, tasks and where they struggle and where there’s room for improvement. And that is the kind of user research that a lot of companies don’t do. They really sort of put this controlled little website in front of you and say, hit this button, okay. Everybody could find the button good. We checked the box on user research, we’re done. We can launch our website. But that’s not really the kind of user research that’s needed. In the case, like the one that we’re talking about where you’re trying to break into a whole different demographic, and you know nothing about their behaviors. You just have a lot of assumptions. Christopher Penn Yeah, it’s basically anthropology. You have to go in and study the Though the the Unknown Speaker needles in while? Yeah, Katie Robbert that’s exactly it. It’s a lot like anthropology and I think that that is a huge skill set needed for marketers. Christopher Penn And it’s something that for good or ill machines cannot do reliably yet we are getting there with some things like facial recognition and stuff, which of course brings a whole other entirely different discussion topic for another episode. But the other thing that I think of companies are missing is there’s no overall strategy to your point. Is this the market for us? You know, or do we need a product marketing campaign for us to build a product that does serve that audience’s needs and I think there’s there is a massive gap there where Yeah, if you have, say, a brand new grill cleaning spray for your your charcoal grill there’s a certain audience that that uses that product or service and trying to market it to apartment dwellers who aren’t even allowed to have any kind of grill seems like a bit of a mess. So there’s there’s some aspect of that. So the question would be for that company, what else can you make for this? We did some work a while ago for food and beverage company, where they were trawling through their customer inboxes. And we’ve identified there were two new products on the market that they were being asked, Do you have a formulation to help stabilize these things? And they’re like, we don’t even know what these things I didn’t know what they were either. I’d never heard of him. But to your point, there’s a lot of listening that could go into that. Even if it’s just looking at your customer service inbox, where else should people be listening? Katie Robbert I mean, social media is a good place to start. But I would definitely caution you that that is not the only source of data that you should be using because there is, you know, social media is a tough place to get really true. Good opinions, social media tends to be a very emotional place where people leave their remarks, you might do better in more private or targeted forums, like specific Reddit forums or sub forums, you know, reviews of similar companies. So Yelp reviews, Google reviews, just to start to get the lexicon of how people talk about things to start to get their language. So at the very least, you can repeat back to them things that they are comfortable hearing like words and terms and phrases, but you know, there you can’t, you’re absolutely right AI cannot have that conversation with someone and get that insight. So I would say, you know, the thing that’s the missing step is that either a focus group or a one on one interview with you know, your target customer base, because you need to understand sort of those like nuances of body language and the nuances tone and certain word choices. And, you know, sometimes the things that aren’t said are just as powerful as the things that are said and the pauses and the silences. Christopher Penn The challenge for some companies is that market research is, as our friend Tom Webster says reassuringly expensive to do all How do you tackle that when you do not have reassuringly large budgets? Katie Robbert You know, there are smaller ways to start. I mean, obviously, you’ve done your data collection with your social listening and your customer complaint office, but, you know, maybe it’s a small Gen pop survey, you know, just to start to get a little bit more color into opinions and behaviors. Or maybe it’s, you know, starting a free community for certain interests and then asking questions there. It’s, it’s a lighter weight, ask. You know, it’s not that in depth big market research project, but at least that You’re getting the right kinds of people that you want to be attracting into your audience base. You know, once you’ve done some of those things, maybe you can say, Hey, I don’t have a huge budget, but perhaps I could talk to one or two people for 20 minutes and just sort of ask them more in more questions to get information that will help further my research. So I think there’s it doesn’t have to be a big blown out. But I don’t know that you can completely skip over the talking to people, regardless of your budget. Christopher Penn Okay. How do you get around though, confirmation bias because a lot of things companies do wrong really wrong with researches. A silly example is how much you like our product a lot. Really a lot of super love your product, right? Katie Robbert Oh, yeah, that’s a tough one. Designing any kind of research. It is. It’s a different skill set. And it’s not something that marketers inherently have. It doesn’t unless you take a market research course you’re not going to be taught how to properly, you know, do survey construction and, you know, develop unbiased questions. So, you know, do a little research on your own. You know, it might be sort of googling around for best practices for creating an unbiased market research survey. I’m sure those resources exist. Again, joining a community like the one that we have. It’s TrustInsights.ai dot AI slash analytics for marketers. It’s a free slack community, you can ask those questions in a community to say, Hey, I have a list of survey questions. Can someone eyeball these to see if they they look biased or not? And so it’s getting that gut check against the work that you’re creating, don’t create it in a vacuum. You know, it’s it’s tough because budgets are really small right now, but you want to do the best work, you want to continue to grow and scale and so it’s really trying to think differently around the resources that you have at your disposal. Maybe using them in a way that you didn’t think you could. Christopher Penn For a product marketing perspective, how do we pivot? If we know we have something that our desired target audience doesn’t want, right? At what point? Do we say we need to start r&d? Like right now? Or do we try to adapt something? How do we tackle that? Katie Robbert Well, I think first and foremost, if you know, you have something that your target audience doesn’t want, don’t continue to try to shove it down their throats and say, buy this thing. I know, eventually, I’m going to wear you down, you’re gonna want it, which unfortunately, I do see a lot of companies do they’re not willing to pivot because it can be costly. And so again, it starts with collecting that information from that target audience of what do you want? And then really taking a hard look at the services that you have or the products and saying, Is there something that we can pivot to, you know, be adaptable to create something for this audience? Maybe maybe we don’t. I think r&d is something that should be done all of the time. Even if it’s done in like very small doses. You should always have something kind of like, you know, stewing in the background. What do you think Chris? Christopher Penn I’m of two minds of this. I, I loved the, you know, the tinkering around and stuff like that. And, you know, certainly we do a ton of it. But also, it’s one of those things with that as, as priorities change as companies scale your r&d does have to at some point, get a little more focused than just, you know, pure academic research, because otherwise you can burn a lot of time for for no good reason. When it comes to, like, how do you build something new again, goes back to that anthropology. When you look at the way somebody does something. What are the obstacles, and the challenge for a lot of companies is going to be you will, in some ways be competing with the problem you’re trying to solve is suffering. So let’s use Uber as an example. If Uber is a problem. There’s some aspects of the Uber experience, it’s a problem and you’re trying to find a way to insert yourself into the conversation, you are actually competing theoretically with Uber itself, because hopefully Ubers own product marketing managers are doing the same kind of research and going, Oh, this part of the app is problematic, right? Where this part of the experience is problematic, how to resolve that. Getting around that requires you to have some level of insight into what your competitors capabilities are. And that is yet again, an entirely different research area. Because, you know, let’s say you’re trying to deal with the issue of safety. You know, some people rightfully feel that they don’t feel safe. And if it were, well, how do you? How do you work around that? Uber is already working on autonomous vehicles, so there’s no other driver in the car. There’s no way for you to have an unpleasant interaction with a driver who isn’t there. So how do you work around that? That’s those are the challenges that are I think a lot of market researchers also forget about when they’re doing product marketing research. Katie Robbert I would agree with that. And so it sounds like what we’re coming around to is, you can’t get around doing some level of research, if you’re trying to break into a new audience segment or just, you know, create marketing plans in general, if you want to continue to move forward and, you know, not be the product from the 1990s that hasn’t changed and evolved at all. Yeah, companies need to start using some data, any data to make these decisions to find out what does my audience want, the worst possible thing that you can do is sit there, you know, in your office chair and go, I know what I want. Therefore, it must be what my audience wants. That is the absolute wrong thing to do, and I’ve seen it happen in action, and I have Seen it tank products, because the stakeholders, the decision makers are what we call an N of one. I know best. I want to move this button over here, I want it to be blue. I want it to, you know, wake me up at six o’clock in the morning, whatever the thing is, but that doesn’t mean that you speak for the majority of your audience. And I think that that is such a huge mistake. That is so common. It happens all the time. Christopher Penn Yep. As always, we will recommend starting with, as you mentioned with Facebook example and 100% mobile users use the data to figure out how bad the problem is first. In this case, in this example, the problem is 100% of this customer segment does not like the the way the Unknown Speaker desktop. Christopher Penn Exactly. So if you got more questions about this and market research in general, stop by our slack group, as Katie mentioned, over at Trust insights.ai slash analytics for marketers, and bring your questions. This is a fun topic to discuss. And it’s great to get the question Have over 1000 other markers there. As always, we’ll find this episode and many others over at the website TrustInsights.ai dot AI We look forward to hearing from you there Take care and stay safe want help solving your company’s data analytics and digital marketing problems. This is Trust insights.ai today and let us know how we can help you

In this week’s In-Ear Insights, Katie and Chris discuss math. Specifically, what are the math skills that marketers of all stripes, but especially those who have an interest in marketing analytics, marketing data science, and machine learning/AI applied to marketing need to know. Do you need a Ph.D.? Do you need to spend a year in a data science bootcamp? The answers might surprise you. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-mathskillsformarketing.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn In this week’s in In-Ear Insights, we’re talking about required math skills in our analytics for marketers Slack, which by the way, if you’re not a member of please come on over to TrustInsights.ai dot AI slash analytics for market was Bill asked when we spoken Social Media Marketing World I asked a rather pointed question what if I’m bad at math I respond to drum that you may not have had the best teachers because math is a language. You can learn other languages, you can learn math, and he had a follow on question. What mathematical skills do you believe are required to work with marketing analytics effectively? So Katie, do you want to know which of those you want to tackle first, like, are you bad at math? And also, what math skills do you think are necessary to be good at analytics? Katie Robbert So I’ll tackle the first part of it. I am self admittedly bad at math. I always have been. I understand the concepts of it, but I definitely transpose numbers. So I was always really good at geometry and proofs and those kinds of things. So there’s a side of math that is very logical to me. You know, I think I had decent math teachers, I have not been able to learn more than one language I, you know, at best speak broken English on a good day. So, you know, I don’t know that saying that you had bad teachers is necessarily the way some people are just not wired to be good at math, and I happen to be one of those people. That said, I don’t think you have to be have a PhD in math in order to be good at analytics. I think that you need to understand the fundamental concepts, you know, basic addition, subtraction, division multiplication, to get where it is you need to go because ultimately what you’re doing is you’re trying to answer a question. And so you know, maybe you rely on someone like a Chris pen to help you get to that. Answer. But ultimately, you need to understand what question you’re trying to answer first, because just looking at a spreadsheet of numbers is going to make your eyes go crossed and blurry. And it’s going to be really difficult to do. So I guess the first is to answer the first question. You don’t necessarily have to be a wizard in math, in order to be able to do analytics and reporting. And so the second question is, what math skills should I have? Well, again, have the fundamentals, understanding the concepts of how numbers go together, you know, you don’t have to be able to necessarily master things like regression analysis, but understanding what it is, is going to be really helpful. What do I mean, Chris, you’re gonna feel a little bit differently than I do about this. Christopher Penn Yes, and no. It may amuse you to find out that I was a straight c student in math for most of my life, except for Statistics and Probability which I failed. So that’s Certainly, there is I have no shortage of understanding the frustration people have. And my comment about bad teachers is exactly that. Because of those experiences I had. I didn’t have the best teachers, I had people who were extremely good practitioners of the field. But we’re not good communicators, and explainers of what it was that they were teaching. You’re absolutely right that the basics are essential. There’s, you have to develop also the mindset. And I think this is what you’re getting at as well in the minds of how do you think about the numbers when you have a row of numbers in a spreadsheet? What do you see a lot of people see the numbers, and then they try to look at them, and somebody is trying to do the math in their heads. And that’s a really bad idea because your brain is just not equipped for that. There are very few people who can do complex mathematics in their heads without any assistance. But when you start thinking about Okay, what do I want to know? The number one thing that people should focus on in analytics is change. What has changed when you look at your website traffic, your email opens, etc. A number by itself means nothing, right? The open rate for this week’s TrustInsights.ai newsletter was 21%. Cool. So what is that good? Is that bad? Does it change from the previous week for the previous month? How does it compare versus other industry publications, knowing that gives context to the number? And that’s what I think a lot of marketers are actually looking for is the context. What does this mean? So that we can decide what we should do? And to your point, that doesn’t require much more than basic division, or you know, and some very simple formulas. The other area that I think is essential, and it’s an interesting thing to talk about is statistics and probability. Our friend and colleague Tom Webster over at Edison research has a very interesting perspective on this. He says that statistics and probability are not mathematics. They are it is a separate It discipline entirely. The fact that they both use numbers is true. But it’s like the difference between writing a white paper and writing poetry. They’re not the same thing. They both use letters and words, but they’re completely different in their, in their intense and outcomes. And there is a lot to statistics and probability that are I think are essential to mathematics essential to analytics. The first one being, even just knowing statistical validity is something statistically significant. We see this a ton, especially in email marketing. Our A B test here says that this email got 540 opens and this one got 539. Version A is the winner, huh? Nope. failed, you failed statistical significance. There’s no way you can attribute that with that small sample size. To say that a one and B did not if the likelihood of it being random chance is as high or higher than that outcome. So being able to do that type of math to even understand are is what my data is telling me valid is an essential skill. And again, if it’s not valid, you have no business making decisions on it. If you do, you’re going to end up shooting yourself in the foot. Katie Robbert So, would you say that most people know to even start with is my is my pile of data? valid? statistically valid? Christopher Penn That’s one of the starting points. Yes, I think Katie Robbert understanding Yes, yes. They do know, to start there. Christopher Penn I say that’s where you should start. Do people know that? No, absolutely not. Katie Robbert Yeah, that was my question is Do people know that that’s even where they should start. So for example, you know, let’s say I’m looking at the open rate of my newsletter. And, you know, I’m seeing a, you know, point 01 percent change week over week. In my brain, I’m like, oh, okay, it’s going up, therefore good. Yeah. Do I need to then worry about the statistical validity of that point? 01 percent? Or do I just care that it’s going up? Christopher Penn It depends. The key question is, is that happening because of something you’re doing? Or is it happening because of random chance of noise? Right? If it’s point 01 percent? If it’s point 01 percent, it’s almost certainly gonna be noise. Right? And so the thing to do there might be to look at a different timeframe. So week over week, month over month is, are you seeing greater aggregated change there and again, you can run a chi square test or a T test or a two tailed test, or whatever the stats thing you want to do to assess that, whether that’s the case or not, but ultimately, you want to know, is an action you are taking, having an actual impact or is what you’re seeing in the data noise? This is like, this is brain bending for a lot of people if you look in your Google Analytics, right? It says Yo, your users were down, you know, 1% for last week, it’s got that red our down, no, the bad things have happened. Are you sure? Or is that noise? Do you know whether that is a an important enough distinction? If your addressable audience is the entire United States, for example, and your traffic is down, you know, 1% that may not mean anything. On the other hand, if it’s, you know, if you have 10 users who doubt Well, you can have 1% of 10 users 100 users and you’re down one user, is that is that statistically significant? It might be, depending on your population. So there’s a lot of things you have to unpack it to figure out. is this important? Is this worth paying attention to? Is this worth worrying about? There are other cases where once you get into bigger effects, like we have a client that you know, less sick run over the last six weeks, while 70% of the traffic, guess what, that’s not Okay, there’s something clearly went off the rails there. And you should know the difference between those two different scenarios, you may not necessarily have to run a formal chi squared test to determine that. But you can clearly tell there’s a big enough effect like,

In this week’s In-Ear Insights, Katie and Chris discuss the importance of transparency and disclosure in research and data. These are troubled, uncertain times – not for the macro situation, but for our willingness to collectively accept statements of fact without verifying those facts or inspecting the methodology behind a piece of data. They also look more deeply into the new Trust Insights report, Social Media Audience Trends During The Pandemic. Tune in now to hear about the research and what went into it. Watch the video here: Listen to the audio here: http://traffic.libsyn.com/inearinsights/tipodcast-methodology.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher Penn In this week’s In-Ear Insights, we’re talking about social media audience. And for those who may have missed it, we released a paper about this recently on the Trust Insights website, you can hop on over there and get it. And by the time this, this episode airs, we’ll done a webinar with our friends over at Talkwalker. So I want to talk about two things. This paper number one, I actually got some interesting, constructive criticism or just criticism, I guess, from a few folks who said, it took too long to get to the point they were 10 pages up front of like charts and things like that, and not enough like action up front just like hit people with the conclusion. And one of the things I’ve responded to a few of these folks about was, I feel like there’s so much information right now, especially this is being dumped on people’s heads that has no source, no methodology, no explanation. People just present data as as unassailable fact. And my whole logic for putting the methodology and the data Up front was to say, like, Look, we’re not pulling things out of places where the sun doesn’t shine, we’re actually pulling things out from real data and research. So Katie, what’s your thought about? Do you just go straight into the hard hitting stuff, put the methodology in the back and hope people sort it out and figure out or do you walk people through the method by which you draw the conclusions so that they can understand the conclusions have validity? How do you feel about that? Katie Robbert You know, I, I can see it from both sides. So in terms of writing, you know, any kind of research, I was trained academically, and so a lot of the methodology has to be put up front because you have to demonstrate the validity of the thing that you’re doing. But it’s a little bit different in the marketing world where, unfortunately, methodology just doesn’t exist. And so I think that the approach that we’ve taken is a little bit more academic and it’s something that It’s unfamiliar to people who are not accustomed to reading academic style research papers. And so I think the bottom line is that the methodology belongs in the research that we’re doing, we feel very strongly that, you know, we want to demonstrate that we’re not just making it up. You know, however, I can definitely see the argument for a little bit more of the executive summary, the so what up front, even if it’s just like, a couple of bullet points to say, in this paper, you will learn the following three things and then go into methodology. I mean, that’s the other thing is people can skip over the methodology and go straight to the conclusion. There is nothing. It’s sort of that same thing of like, if you don’t like something you see on social media, just keep scrolling. Then, in this sense, it’s a digital paper, just keep scrolling. But I do think that having the methodology, clear and put right out there They’re in people’s faces is important. And you know whether or not they choose to read it, totally a personal preference. But then we know at the end of the day that we have done our due diligence to say, this is validated research, we’re not just putting numbers on a pretty slide and saying, Here you go, here it is. Christopher Penn Yeah, that was my feeling as well is having it be a little bit longer to the conclusions is, to me, okay, I know, it’s not as interesting particularly for folks who don’t want to do the methodology review or, you know, maybe don’t have the, the statistical background to dig through it. But I feel like from a values perspective, you know, we one of our core values is we are we are transparent and open like, Hey, here’s how the, here’s how we did the thing. So that if somebody says, Yeah, I don’t agree with you, we can say great, explain how you would have done it differently with this same data because we want to learn to we know for Sure, we’re not using the best of the best out there, there’s always something better. There’s always something more thorough, there’s always something to be learned. And I feel like having that transparency and openness helps us communicate to people, we are open to learning more. Katie Robbert Well, you know, it’s interesting too, because while methodology and research is very objective, the way in which we individually think about it and approach it is very subjective in the sense that, you know, you and I might look at the exact same data and come up with a slightly different interpretation of it. And so, having the methodology of how all of the numbers came together is important, but we’re never going to be able to detail the methodology of how we actually individually thought about it. And I think that’s what makes it a little bit more unique. And so, you know, if I recall that another piece of the feedback that you got was why aren’t Are you putting it all out there for people to see how you did the thing and I, I’m in the same camp as you, Chris, were number one, our values, you know, promote transparency. But number two, just because we tell you how we got the numbers doesn’t mean that you can replicate how we thought about it. Because that then comes from our own personal and professional experience of doing this for many years and our training both formal and informal. And so I have zero issue with putting out the methodology because someone else who’s trying to replicate it will not necessarily come to the same conclusions and if they do, that’s great, then that means that we have helped taught people how to fish instead of giving them the fish. Christopher Penn As Jay Baer likes to say having the recipe doesn’t make you a chef. This is something I don’t have as much background. As you do. Can you talk through briefly what it means for something to be peer reviewed in the academic world obviously, it means that other people qualified people have looked at it and your conclusions and said, well, doesn’t look like you did anything obviously wrong. Katie Robbert I mean, that’s essentially what it is. And so, you know, let’s say you are a prominent, you know, researcher and child behavioral issues. If you are doing, you know, an academic study or a clinical trial, then when you submit your paper for peer review, then other people who have similar backgrounds, similar credentials, maybe even slightly more experienced, and have also had peer reviewed, peer reviewed published, academic papers will then read your paper, and sort of what they’re trying to do is make sure that anything that goes out is as validated and foolproof as possible. So they’re looking for, you know, does this methodology check out, do the numbers add up, and so it’s an academic version of A QA process essentially. And so in our instance, if, you know we at Trust Insights are doing this research paper, it’s one of the reasons why we partner with a company like Talkwalker, because they are the experts in their own data. But they’re also the experts in the data that it polls in this instance, social media platforms. And so they are the ones who are peer reviewing our information for us to say, you know, what, we’re in our data every day, this doesn’t make sense, or this is really interesting insight. I didn’t even know we could pull this out. Let me see if I can work backwards and get to the same thing. And that’s how we’re peer reviewing. And it’s one of the things that we find really important for the way that we work, because we’re so close to the work that we do. And we really value those outside opinions and peers and feedback to make sure that the work that we’re putting out is as good as it can be. Unknown Speaker Yeah, now, Christopher Penn at least you’re consistent. We just spent the first eight minutes of the show talking about the methodology. Let’s talk about. So the big thing that we’ve noticed and and there’s a lot, the approach to this is looking at search data. And the reason why is people will type things into Google, that they wouldn’t necessarily say aloud. This is not in the paper by this is a podcast exclusive. I was actually in a discussion group of markers over the weekend. And one of them was saying, I bet there’s gonna be a huge baby boom, you know, after this whole pandemic. And when you look at all the search terms around things like birth control, or supplies for personal intimacy, those actually dropped by about 20% across the board at the start of the pandemic, because people in many cases isolating by themselves, so I’m like, I don’t know that that baby booms actually going to happen. It will be interesting to se