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As analysts, it can be easy to get so focused on the data that we lose sight of the imperative that we answer meaningful questions (aka: validating relevant hypotheses). On this episode, we sat down with Wil Reynolds, co-founder and accidental lead generator for SEER Interactive, for a discussion that turned out to be about curiosity and the power of trying to prove yourself wrong (and being willing to invest the time to do so!). In the end, we concluded that Wil has always been a "data person," even if he doesn’t necessarily see himself as such. That is... actually kinda' profound! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Have you ever worked in a large organization where the data team(s) are perfectly structured to deliver efficient, harmonious, and meaningful results to the business with 'nary a gap nor a redundancy? If you answered "yes," then we'll go ahead and report you to HR for being a LIAR! From high growth startups to staid enterprises, figuring out how to organize the data and data-adjacent teams is always chock full of tradeoffs. And that's the topic of this episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Have you ever thought, "you know, it would be interesting to take my analytical knowledge and just totally run an organization based on what the data says?" Yeah. Us, either. That's terrifying! But, that's exactly what our guest on this episode did. Ben Lindbergh, along with his stathead-in-crime (aka, co-author) Sam Miller, took over the management of a minor league baseball team in 2015, and the result was The Only Rule Is It Has to Work: Our Wild Experiment Building a New Kind of Baseball Team. How does that apply to analytics in the business world? In a surprising number of ways, it turns out! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data is everywhere and it's simply not going away. Plenty of people do seem to ignore it to their peril, but if we are trying to make sense of the world, making good sense of data is absolutely critical. In business we call it data literacy, and, truthfully, it is a mandatory skill set for almost anyone. Data and understanding data might have a set of rules, and it seems like not everyone is committed to playing by those rules. Sometimes even our own brains get in on the act of hiding what the data actually means from us. And that's the subject of this episode with Financial Times columnist, BBC presenter, and Data Detective / How to Make the World Add Up author Tim Harford. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're baaaaaaack…! Shorter show name, a rebrand, some minor formatting and structural updates, but still "Moe Kiss with a couple of guys who listeners can't keep straight." On this episode, we talk for a little bit about what we've been doing while we were on hiatus and then dive into a topic that only Cassie Kozyrkov has dared to deeply explore before: the distinction between analysts, statisticians, data engineers, ML engineers...and data charlatans. Well, really just the first two. But, Cassie('s content) has made numerous appearances on the show, so it seemed like high time that we dug into some of her ideas. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.  

Once upon a time, there was an analyst. And that analyst had some data. She used that data to do some analysis, and from that analysis she realized she had some recommendations she could make to her organization. This was the point where our intrepid analyst reached a metaphorical fork in Communication Road: would she hastily put all of her thoughts together quickly in a slide deck with charts and graphs and bullets, or would she pause, step back, and craft a true data story? Well, if she listened to this episode of the podcast with presentation legend Nancy Duarte, author of five award-winning books (the most recent one — DataStory: Explain Data and Inspire Action Through Story — being the main focus of this episode) she would do the latter, and her story would have a happy ending indeed! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on December 17, 2019.

We can watch (sort of) what users do on our sites. That's web analytics. We can ask them how they felt about the experience. That's voice of the customer. But, can we (and should we?) actually analyze their emotional reactions? On this episode, Michael and Tim sat down with Dr. Liraz Margalit, Head of Digital Behavioral Research at Clicktale, to bend their brains a bit around that very topic. And, they left the discussion thinking differently about conversion rates, and even realizing that scroll tracking might just have a valuable application! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on June 20, 2017.

Do you model professionally? Would you like to? Or, are you uncertain. These are the topics of this episode: Bayesian statistician (among other official roles that are way less fun to say) Dr. Elea Feit joined the gang to discuss how we, as analysts, think about data put it to use. Things got pretty deep, included the exploration of questions such as, "If you run a test that includes a holdout group, is that an A/B test?" This episode ran a little long, but our confidence level is quite high that you will be totally fine with that. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.  This episode originally aired on March 13, 2018.

What’s in a job title? that which we call a senior data scientist by any other job title would model as predictively… This, dear listener, is why the hosts of this podcast crunch data rather than dabble in iambic pentameter. With sincere apologies to William Shakespeare, we sat down with Maryam Jahanshahi to discuss job titles, job descriptions, and the research, experiments, and analysis that she has conducted as a research scientist at Datapeople (formerly TapRecruit), specifically relating to data science and analytics roles. The discussion was intriguing and enlightening! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Did you hear the one about the Harvard-educated economist who embraced her inner wiring as a lateral thinker to explore topics ranging from HIV/AIDS in Africa to the impact of Hepatitis B on male-biased sex ratios in China to the range of advice and dicta doled out by doctors and parents and in-laws and friends about what to do (and not do!) during pregnancy? It’s a data-driven tale if ever there was one! Emily Oster, economics professor at Brown University and bestselling author of Expecting Better and Cribsheet, joined the show to chat about what happens when the evidence (the data!) doesn’t match conventional wisdom, and strategies for presenting and discussing topics where that’s the case. Plus causal inference! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.  This episode originally aired on October 22, 2019.

Is your organization customer-centric? Does your product team dive into the demographics of your customers to figure out what features will make them as happy as possible? If so, then you're doing it all wrong! Perhaps. On this episode, the gang chats with Dr. Peter Fader (@faderp) from The Wharton School and Zodiac Metrics, about putting customer lifetime value (CLV) front and center when it comes to developing and executing marketing strategies. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on August 29, 2017. 

Thanks for stopping by. Please get comfortable. We’re going to be taking a few notes while you listen, but pay that no mind. Now, what we’d like you to do is listen to the podcast. Oh. And don’t worry about that big mirror over there. There may be 2 or 3 or 10 people watching. Wow. We’re terrible moderators when it comes to this sort of thing. That’s why Els Aerts from AGConsult joined us to discuss user research: what it is, where it should fit in an organization’s toolkit, and some tips for doing it well. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on May 8, 2018.

Have you ever seen a one-man show in the theater? It's awesome. Unless it's terrible. The same can be said for one-person digital analytics teams. It can be awesome, in that you get to, literally, do EVERY aspect of analytics. It can be terrible because, well, you've got to do EVERYTHING, and it's easy for the fun stuff to get squeezed out of the day. On this episode, we head back Down Under for a chat with Moe Kiss, product (and digital) analyst at THE ICONIC. Whether you pronounce "data" as DAY-tuh or DAH-tuh, Moe's perspective will almost certainly motivate you find new ways to push yourself and your organization forward. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on December 6, 2016.

In recognition of International Women's Day, and because it's a really important topic, this is a very special episode. The two straight, white, cisgender male co-hosts of this podcast sat this episode out, while Moe took over the mic for an in-depth discussion with Alison Vorsatz from Fairygodboss and Aubrey Blanche from Atlassian about diversity (a term they both try to avoid) in the workplace. If this episode doesn't change your perspective and compel you to action, you are almost certainly not a human being. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode originally aired on March 7, 2019.

As analysts, we often have unique knowledge of the data, specialized responsibilities for data-related deliverables, and an expectation that we'll be at the ready to dive into high priority requests. What happens, then, when we're out of the office, be that for a planned vacation, for an unexpected illness, or for bringing a new human being into the world? And, what happens if it's that last one and you're also the most beloved co-host of the top-rated explicit analytics podcast? Tune in to this episode to find out, as we used Moe in a dual role of being both a co-host and a guest (again!) to explore the challenges (and opportunities!) of being out of the office. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

You know that sinking feeling: the automated report went out first thing Monday morning, and your Slack messages have been blowing up ever since because revenue flatlined on Saturday afternoon! You frantically start digging in (spilling your coffee in the process!) while you're torn between hoping that it's "just a data issue" (which would be good for the company but a black mark on the data team) and that it's a "real issue with the site" (not good for the business, but at least your report was accurate!). Okay. So, maybe you've never had that exact scenario, but we've all dealt with data breakages occurring in various unexpected nooks and crannies of our data ecosystem. It can be daunting to make a business case to invest in monitoring and observing all the various data pipes and tables to proactively identify data issues. But, as our data gets broader and deeper and more business-critical, can we afford not to? On this episode, we were joined by Barr Moses, co-founder and CEO of Monte Carlo to chat about practical strategies and frameworks for monitoring data and reducing data downtime! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As we put the awfulness of 2020 in the rearview mirror, we thought it might be fun to look back to another bleak period: the 2007-2008 financial crisis! Why? Because Tim hasn't stopped talking about Subprime Attention Crisis — the Tim Hwang book that draws a parallel between the digital advertising ecosystem and the subprime lending crisis from a decade ago — we decided to all give it a read and then sit down for a discussion with the author. From the opacity brought on by the many moving parts to misaligned incentives to the fact that, well, even more than just the internet is built on digital advertising dollars, it was a fascinating discussion! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Google bought Urchin in 2005 and, virtually overnight, made digital analytics available to all companies, no matter how large or how small. Optimizely was founded in January 2010 and had a similar (but lesser) impact on the world of A/B testing. What can we learn from ruminating on the past, the present, and the future (server-side testing! sample ratio mismatch checking! Bayesian approaches!) of experimentation? Quite a bit, if we pull in an industry veteran and pragmatic thinker like Ton Wesseling from Online Dialogue! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As unlikely as it seemed at many times throughout the year, 2020 actually IS finally drawing to a close, and that means it's time for our annual look back on the year: what happened with the podcast, what happened with the industry, and what happened as the entire world caught fire by way of wood-fuelled, climate-assisted combustion and by virus-induced fevers. In hindsight, there were faint hints of what the rest of the year would bring when our co-hosts and producer were together in person at Superweek in late January, but exactly how upside-down the world went still took them by surprise. One thing stayed constant, though: Tim and Moe continue to be able to talk past each other and violently argue about something about which they, basically, agree. On this episode: cover letters! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's the holiday season and, despite Tim's 27-slide deck making a case for why we should do an Airing of Grievances-themed show, we went in another direction. On this episode, we explore a delightful tale that exists at the intersection of "Giving Back to the Community" and "Growing the Analytics Talent Pool." Rob Jackson joined the gang to be peppered with questions about the what, why, and how of his digital marketing social enterprise: WYK Digital. It's an inspiring story of breaking down some of the barriers to digital-focused jobs for underserved youth. And doing so in the middle of a pandemic, no less! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.