Synthetic data: it's a fascinating topic that sounds like science fiction but is rapidly becoming a practical tool in the data landscape. From machine learning applications to safeguarding privacy, synthetic data offers a compelling alternative to real-world datasets that might be incomplete or unwieldy. With the help of Winston Li, founder of Arima, a startup specializing in synthetic data and marketing mix modelling, we explore how this artificial data is generated, where its strengths truly lie, and the potential pitfalls to watch out for! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

No matter how simple a metric's name makes it sound, the details are often downright devilish. What is a website visit? What is revenue? What is a customer? Go one level deeper with a metric like customer acquisition cost (CAC) or customer lifetime value (CLV or LTV, depending on how you acronym), and things can get messy in a hurry. In some cases, there are multiple "right" definitions, depending on how the metric is being used. In some cases, there are incentive structures to thumb the definitional scale one way or another. In some cases, a hastily made choice becomes a well-established, yet misguided, norm. In some cases, public companies simply throw their hands up and stop reporting a key metric! Dan McCarthy, Associate Professor of Marketing at the Robert H. Smith School of Business at the University of Maryland, spends a lot of time and thought culling through public filings and disclosures therein trying to make sense of metric definitions, so he was a great guest to have to dig into the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We all know that data doesn't speak for itself, but what happens when multiple instruments of measurement contain flaws or gaps that impede our ability to measure what matters on their own? Turning to our intuition and triangulation of what's happening in the broader macro sense can often help explain our understanding of our customers' ever-changing choices, opinions, and actions. Thankfully we had Erika Olson, co-founder of fwd. — which in our opinion is essentially the Freakonomics of marketing consultancies — join Tim, Moe and Val for this discussion to dive into some real-world examples of things that are inherently hard to measure and ways to overcome those challenges. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

udging by the number of inbound pitches we get from PR firms, AI is absolutely going to replace most of the work of the analyst some time in the next few weeks. It's just a matter of time until some startup gets enough market traction to make that happen (business tip: niche podcasts are likely not a productive path to market dominance, no matter what Claude from Marketing says). We're skeptical. But that doesn't mean we don't think there are a lot of useful applications of generative AI for the analyst. We do! As Moe posited in this episode, one useful analogy is that thinking of using generative AI effectively is like getting a marketer effectively using MMM when they've been living in an MTA world (it's more nuanced and complicated). Our guest (NOT from a PR firm solicitation!), Martin Broadhurst, agreed: it's dicey to fully embrace generative AI without some understanding of what it's actually doing. Things got a little spicy, but no humans or AI were harmed in the making of the episode. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How good are humans at distinguishing between human-generated thoughts and AI-generated…thoughts? Could doing an extremely unscientific exploration of the question also generate some useful discussion? We decided to dig in and find out with a show recorded in front of a live audience at Marketing Analytics Summit in Phoenix! With Michael in the role of Peter Sagal, Julie, Tim, and Val went head-to-GPU by answering a range of analytics-oriented questions. Two co-hosts delivered their own answers, and one co-host delivered ChatGPT's, and the audience had to figure out which was which. Plus, a bit of audience Q&A, which included Michael channeling his inner Charlie Day! This episode also features the walk-on music that was written and performed live by Josh Silverbauer (no relation to Josh Crowhurst, the producer of this very podcast who also wrote and recorded the show's standard intro music; what is it about guys named Josh?!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the  show page.

Professional development is a big topic—way more than just thinking about what job you want in five years and setting milestones along the way. Thankfully we had Helen Crossley, Senior Director of Marketing Science at Meta, join Michael, Moe, and Val to dive deep into this topic! We explored how to set really good, meaningful goals, the challenges across each stage from junior analyst to leader, and how to give great feedback. We also spent quite a bit of time discussing the new challenges that becoming a first-time manager presents and, hopefully, some helpful tips and thought exercises to help out our listeners who are or are about to be faced with this challenge. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Long-time listeners to this show know that its origin and inspiration was the lobby bar of analytics conferences—the place where analysts casually gather to unwind after a day of slides interspersed with between-session conversations initiated awkwardly and then ended abruptly when the next session begins. Of the many conferences where this occurs, Marketing Analytics Summit (née, eMetrics) is the one in which this show is most deeply rooted. And, we'll be recording an episode in front of a live audience with all of the North America-based co-hosts on Friday, June 7, 2024, in Phoenix, Arizona at the next one! To call that out, including announcing a promo code for any listeners interested in joining us for the event, Michael, Val, and Tim turned on the mics for a bonus episode with a little reminiscing about past experiences at the conference, including Val's mildly disturbing retention of dates and physical artifacts. Visit the show page for, well, not much more than you see here.

For those who celebrate or acknowledge it, Christmas is now in the rearview mirror. Father Time has a beard that reaches down to his toes, and he's ready to hand over the clock to an absolutely adorable little Baby Time when 2024 rolls in. That means it's time for our annual set of reflections on the analytics and data science industry. Somehow, the authoring of this description of the show was completely unaided by an LLM, although the show did include quite a bit of discussion around generative AI. It also included the announcement of a local LLM based on all of our podcast episodes to date (updated with each new episode going forward!), which you can try out here! The discussion was wide-ranging beyond AI: Google Analytics 4, Marketing Mix Modelling (MMM), the technical/engineering side of analytics versus the softer skills of creative analytical thought and engaging with stakeholders, and more, as well as a look ahead to 2024! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What's more sexy: analytics or innovation? What about combining them! That sounds great, and Thomas Davenport would be so proud if you pulled it off, but the reality is that the idea of innovation through analytics is one thing, while the reality of making it happen is another thing entirely. Dr. Tiffany Perkins-Munn, Head of Marketing Data & Analytics at JPMorgan Chase & Co., joined us for a discussion on the subject! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Multi-touch attribution, media mix modeling, matched market testing. Are these the three Ms of marketing measurement (Egad! The alliteration continues!)? Seriously. What's with all the Ms here? Has anyone ever used experimentation to build a diminishing return curve for the impact of a media measurement technique based on how far along in the alphabet the letter of that technique is? Is "M" optimal?! Trust us. You will look back on this description after listening to this episode with John Wallace from LiftLab and find it… at least mildly amusing. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We've always said that the genesis of this podcast was the lobby bar of analytics conferences across multiple continents, and this year's Marketing Analytics Summit in Las Vegas was a reminder of our roots on that front. All three co-hosts made the trip to Caesars Palace for the event. Moe presented on bringing a product mindset to analytics (by "presented on," we mean "workshopped content for a future podcast episode"), and the closing keynote was a recording of the show in front of a live (and thoughtful and engaged) audience. Give it a listen, and it will almost be like you were there! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Hey there, mister. That's a mighty nice multi-touch attribution model you're using there. It would be a shame to see it get mixed up with a media model. Or... would it? What happens if you think about media mix models as a tool that can be combined with experimentation to responsibly measure the incrementality of your marketing (while also still finding a crust of bread in the corner for so-called "click attribution")? According to a 2019 paper published by ThirdLove (which happens to have been Michael's last call on our last episode), that's a pretty nice way to go, and we thought it would be fun to see if we could raise Tim's blood pressure by giving him something to vigorously agree with for once. It was. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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. 

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.

Once every four years in the United States, there is this thing called a "presidential election." It's a pretty boring affair, in that there is so much harmony amongst the electorate, and the two main candidates are pretty indistinguishable when it comes to their world views, policy ideas, and temperaments. But, despite the blandness of the contest, digging in to how the professionals go about forecasting the outcome is an intriguing topic. It turns out that forecasting, be it of the political or the marketing variety, is chock full of considerations like data quality, the quantification of uncertainty, and even () the opportunity to run simulations! On this episode, we sat down with G. Elliott Morris, creator of The Crosstab newsletter and a member of the political forecasting team for The Economist, to chat about the ins and outs of predicting the future with a limited set of historical data and a boatload of uncertainty. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It sometimes seems like there must be a Moore's Law of marketing technology (or "martech," as the cool kids call it, and our site is on a .io domain, so we're definitely the cool kids) whereby the number of platforms available doubles every 6 to 8 weeks. And, every couple of months, it seems, a whole new category emerges. From CMS to DAM to CRM to TMS to DMP to DSP to CDP, it's an alphabet soup of TLAs that no one can make sense of PDQ! On this episode, Michael, Moe, and Tim sat down with the man who coined the name for one of those categories back in 2013: David Raab, the founder of the CDP Institute! It was a lively chat about the messy world of vendor overload and how to frame, assess, and successfully manage martech stacks. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

For our special International Women's Day episode, we committed a type one error and peeked at our results, so we are releasing this winner three days early. As good analysts, we set out to optimise the podcast by swapping out Tim and Michael for two guests (it's rare for Tim to be in the control group, but he's an outlier either way). Unfortunately, it turns out we confused testing with personalisation, so we invited along a family member, Michele Kiss, as well as CRO expert Valerie Kroll, to talk about the evolution of the space from conversion rate optimisation (CRO) to experimentation. In Val's words, good experimentation programs are all about optimising to de-risk product feature roll-outs and marketing tactics, all the while learning about our users and prospects. Stay tuned for the three tips from our guests on how to set up the best version of an experimentation framework, as well as the stats on the show's gender breakdown since our start in 2015! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Multi-touch attribution is like fat free cheese: it sounds like a great idea, it seems like technology would have made it amazing and delicious by now, and, yet, the reality is incredibly unsatisfying. Since we've recently covered how browsers are making the analyst's lot in life more difficult, and since multi-touch attribution is affected by those changes, we figured it was high time to revisit the topic. It's something we've covered before (twice, actually). But interest in the topic has not diminished, while a claim could be made that reality has gone from being merely a cold dishrag to the face to being a bucket of ice over the head. We sat down with Priscilla Cheung to hash out the topic. No fat free cheese was consumed during the making of the 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 it would be a great idea to have a drink or two, grab a microphone, and then air your grievances in a public forum? Well, we did! This episode of the show was recorded in front of a live audience (No laugh tracks! No canned applause!) at the Marketing Analytics Summit (MAS) in Las Vegas. Moe, Michael, and Tim used a "What Grinds Our Gears?" application to discuss a range of challenges and frustrations that analysts face. They (well, Moe and Tim, of course) disagreed on a few of them, but they occasionally even proposed some ways to address the challenges, too. To more effectively simulate the experience, we recommend pairing this episode with a nice Japanese whiskey, which is what the live audience did! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Perspective is a good thing. We've all agonized about a misreported metric or an unsatisfying entry page analysis and had to remind ourselves that we're not exactly saving lives with our work. On this episode, though, the gang actually meanders into life-and-death territory by chatting about one of the uses of data outside of the world of digital marketing and websites and eCommerce: natural disaster preparation and response. Sherilyn Burris from Cascia Consulting joins Michael, Moe, and Tim to chat about her experiences in a variety of roles in just that area, how she uses data, how the data landscape has evolved over the past 15 years, and what she has learned about communicating data to politicians, to the media, and to the general public (which has some intriguing parallels to the communication of data in digital analytics!). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.