Regression. Correlation. Normality. t-tests. Falsities of both the positive and negative varieties. How do these terms and techniques play nicely with digital analytics data? Are they the schoolyard bullies wielded by data scientists, destined to simply run by and kick sand in the faces of our sessions, conversion rates, and revenues per visit? Or, are they actually kind-hearted upperclassmen who are ready and willing to let us into their world? That's the topic of this show (albeit without the awkward and forced metaphors). Matt Policastro from Clearhead joined the gang to talk -- in as practical terms as possible -- about bridging the gap between traditional digital analytics data and the wonderful world of statistics. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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.

Bayesian vs. Frequentist. False Positive vs. False Negative. Truth vs. Uncertainty. It's the world of A/B testing! In this bonus mini-episode, Moe sat down with Chad Sanderson from Subway to discuss some of the pitfalls of A/B testing -- the nuances that may seem subtle, but are anything but trivial when it comes to planning and running a test.

If you have a smartphone nearby and you are not wearing a foil hat, chances are that some brand somewhere -- and probably several brands in many places -- know where you are. Is that creepy? Maybe. It's likely removing a few taps when you check what the weather will be like tomorrow, and there might just be a coupon for a discounted hamburger just waiting to pop up when you get near your favorite QSR around lunchtime! In this episode, James Fogelberg from Landmarks ID joins the gang to discuss the ins and outs of using the ubiquity of mobile to the advantage of both brands and consumers. 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 walked out of a meeting with a clear idea of the analysis that you're going to conduct, only to find yourself three days later staring at an endless ocean of crunched data and wondering in which direction you're supposed to be paddling your analysis boat? That might not be an ocean. It might be an analytics rabbit hole. In this episode, the gang explores the Analysis of Competing Hypotheses approach developed by Richards Heuer as part of his work with the CIA, inductive versus deductive reasoning, and engaging stakeholders as a mechanism for focusing an analysis. Ironically, our intrepid hosts had a really hard time avoiding topical rabbit holes during the episode. But, acknowledging the problem is the first part of the solution! For complete show notes, including links to items mentioned in this show and a transcript of the discussion, visit the show page.

That's right. We're trying to grow the reach of this podcast, so we figured we needed to do some growth h---...NO! No. No. NO!!! We're NOT going to use that term. But, it turns out that growth marketing has some interesting concepts. On the one hand, you may think, "Don't I already do that?" And the answer is quite possibly, "Yeah. Pretty much." On the other hand, you may think, "Oh, well that's an interesting lens through which to view the world." And, that is okay, too. Either way, check out this chat Moe had with Krista Seiden from Google on the subject.

Michael, Tim, and 12,998 of their closest friends descended on Las Vegas for Adobe Summit the last week of March. With luck, Tim will have worked through the after-effects of the sensory overload of Vegas combined with the sensory overload of Adobe Summit by mid-April, but who knows? The guys (really...just the guys -- Moe was in the U.S., but she was in Austin at CXL Live) sat down to share their hot takes from the show. Attribution, Adobe Sensei, Adobe Launch, the Philadelphia Eagles, and more! For a picture of Michael and Tim recording this episode, head over to the show page.

Some people (possibly even one of the co-hosts of this podcast...on this very episode) have been known to say, "People have this dependency on Excel, which is freakin' weird!" We know it wasn't Tim, because he wouldn't have filtered his language! Whether it's a symptom of weirdness, an illustration of inertia, or an invisible hand of inevitability, though, Excel remains omnipresent. Is that a good thing? Is it a bad thing? Is it merely "a thing?" In this episode, the gang dives into the topic: the good and bad of Excel, the various paths to a future where its ubiquity is no longer a given, and different strategies and considerations for moving towards that future.  For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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 show and a transcript of the show, visit the show page.

Moe sat down for a chat with privacy and GDPR expert Aurélie Pols to dive in to some of the questions that, at times, get treated as peripheral in the run-up to new regulations, but that seem like they are fairly fundamental when it comes to understanding the rationale and drivers behind those regulations: what does the Holocaust have to do with GDPR? Is GDPR something that was simply dreamed up in Europe, or are there roots in other countries (teaser: Eleanor Roosevelt). Is GDPR inherently anti-business? It's a quick chat but, hopefully, will give you some deeper perspective on the subject!