The Analytics Power Hour (Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, and Julie Hoyer)

It seems like a simple couple of questions: 1) When and where does the analyst’s role start?, and 2) When and where does the analyst’s role end? And, do the answers to either of these questions change based on the type of organization you’re in (in-house versus agency)? As it turns out, Michael and Tim largely agree on the answers to these questions…but their agreement is pretty expansive, so this could be the episode that infuriates you, dear listener! Give it a listen, and be prepared to shake your fist at your earbuds!

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you ever feel like you've got the analytics blues because you see what needs to happen, and it's something innovative, and all the signals say it's the right thing to do... but the realities of organizational life are a brick wall on the path to progress? Welcome to corporate life, buddy. That's just the way it is! Or...is it? On this episode, the gang sits down with Evan LaPointe and gets him to jam a bit -- literally at first, and then figuratively -- about organizational dynamics, the tradeoffs between personality types, and why it can be counterproductive to always try to cater to all of the different psychologies and mindsets in any given meeting. And round tables.

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.

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.

Most of the time, we think of analytics as taking historical data for a business, munging it in various ways, and then using the results of that munging to make decisions. But, what if the business has no (or very little) historical data… because it's a startup? That's the situation venture capitalists — especially those focused on early stage startups — face constantly. We were curious as to how and where data and analytics play a role in such a world, and Sam Wong, a partner at Blackbird Ventures, joined Michael, Val, and Tim to explore the subject. Hypotheses and KPIs came up a lot, so our hypothesis that there was a relevant tie-in to the traditional focus of this show was validated, and, as a result, the valuation of the podcast itself tripled and we are accepting term sheets. For complete show notes, including links to items mentioned in this episode and a transcript of the show, 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.

Scads has been written about the distinction between skills and talents. But, how does that distinction apply to digital analysts? In this episode, Jim and Tim actually find something they agree on...at least briefly. And Michael maneuvers Tim into at least partially buying into ideas that he has previously referred to as touchy-feely crap. With quotations being dropped from Thoreau to Gygax and business writers in between, you may find yourself questioning your chosen vocation by the end of this 45-minute hour. But we hope not. Really!

Have you heard the one about the four analysts who run a podcast who walked into a resort in Hungary? Well, now you can! Or, at least get a taste of that experience. Michael, Moe, Tim, and Josh headed to Superweek last month and, among other things, did a 12-hour audio livestream to try to give interested listeners a taste of the experience. On this episode, we're bringing you just over an hour (occasionally, we "power" right past the "hour" mark) of that livestream, centered around (but not limited to!) Michael's presentation on "the last mile of analytics," which is about the importance of self-awareness, communication, and interpersonal skills when it comes to putting analytics into action. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What's the hot new technology of 2018? AI? Deep Learning? Pole-dancing robots? Maybe. Or, maybe it's customer data platforms (CDPs) -- a topic we actually covered way back in January 2017 on episode #053 with Todd Belcher, who, at the time, was with CDP provider BlueConic. Since then, Todd left BlueConic to start CDP Resource, which is, well, a resource for companies looking to select, implement, and maintain a CDP. We asked Todd to come back on the show to give us the rundown on how there is now -- finally -- clarity, consolidation, and maturity in the space, as all of the providers have aligned around a common definition of what a CDP is, what it does, and how it should do it. Alas! The space isn't even remotely there yet! We have yet to even reach the peak of inflated expectations! Which was probably why it was such an informative discussion.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Mama always said: life is like a box of chocolates, and online learning is sometimes like one of those boxes where you don’t know which piece is delicious nougat and which piece is some sort of nasty, coconut-y cream. Well, maybe not your mama. But, it’s a big world, so, surely, there’s a mother out there somewhere who would agree with the sentiment. On this episode, the gang chatted with Google Consumer Insights Analyst Lizzie Allen-Klein about different learning styles and different approaches and options for learning new (and hard!) analytical skills. And there might have been an embarrassing interlude where Tim and Michael exhibited their respective possession of some Y-chromosomes.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do analysts make things more complicated than they need to be, or is the data representing a complex world, so that is just the nature of the beast? Or is it both? Stakeholders yearn for simple answers to simple questions, but the road to delivering meaningful results seems paved with potholes of statistical complexity, data nuances, and messy tooling. What is a business to do? Frederik Werner from DHL joined Michael and Tim for a discussion that definitively determined that, well, the topic is…complicated! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Somewhere between "welcome to the company, now get to work!" and weeks of tedious orientation sessions (that, presumably, include a few hours with the legal department explaining that, should you be on a podcast, you need to include a disclaimer that the views expressed on the podcast are your own and not those of the company for which you now work), is a happy medium when it comes to onboarding an analyst. What is that happy medium, and how does one find it? It turns out the answer is that favorite of analyst phrases: "it depends." Unsatisfying? Perhaps. But, listeners who have been properly onboarded to this podcast know that “unsatisfying” is our bread and butter. So, in this episode, Moe and Michael share their thoughts and their emotional intelligence on the subject of analyst onboarding, while Tim works to make up for recent deficiencies in the show’s use of the “explicit” tag. 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 attended a conference? Did you know that analysts over-index towards introversion?* Have you ever struggled to figure out how to start a conversation over a cold pastry and a cup of tepid coffee at a conference breakfast? IS there actually a point in developing and executing a strategy when it comes to attending a conference? Is it annoying to listen to people who speak pretty regularly at conferences pontificate about speaking at conferences? Some of these questions are answered on this episode!

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

*We made this up, but it seems plausible.

What IS customer intelligence? What is a customer? Is the customer best understood by breaking the word down into its component parts: "cuss" and "tumor?" Would that be an intelligent thing to do? Will these and related questions some day be answered by self-aware machines? Will any of *these* questions be answered on this episode? Give it a listen and find out!

The mish-mash of companies, products, and miscellany mentioned on this show include: Adobe, Oracle/ATG, SAS Customer Intelligence, Salesforce.com, Scott Brinker (Chief Martec), Domo, Data Studio 360, Tableau, iJento, Netezza, SPSS, Unfrozen Caveman Lawyer, Eight Is Enough, Legend of the Plaid Dragon (and the Slack version), Office Vibe, p-value article on fivethirtyeight.com (and the p-hacking app), and the "AI, Deep Learning, and Machine Learning" video.

Change. It's scary. It's exhilarating. It's a song by Churchill. Sometimes, be it due to your manager, due to a corporate acquisition, or due to a job change, you just wind up with a voice in your head belting out, "You want me to change, change, change!" In this episode, Nancy Koons from Team Demystified joins us to dive into our collective histories when it comes to switching analytics tools -- where we stumbled, where we succeeded, and how we've come to approach the ever shifting landscape of analytics tools. 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.

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. People, places, things, sites, and doodads mentioned in this episode were many, and they include: R, Tableau, Snowplow, adjust, Datalicious, Moe's post on Analysis of Competing Hypotheses, Moe's post on getting started in digital analytics, Jeffalytics.com, RSiteCatalyst, The Millenial Whoop, Kabaddi, Michael Yates, ABC (the Australian Broadcasting Corporation), an Event Tracking Naming Strategy from Chris Le, Simo Ahava, Nico Miceli, and Towards Universal Event Analytics - Building an Event Grammar by Snowplow co-founder Alex Dean.

Is it just us, or does it seem like we're going to need to start plotting the pace of change in the world of analytics on a logarithmic scale? The evolution of the space is exciting, but it can also be a bit dizzying. And intimidating! There's so much to learn, and there are only so many hours in a day! Why did we choose that [insert totally unrelated field of study] degree program?! These questions and more—including a quick explanation of bootstrapping for Tim’s benefit, which is NOT bootstrapping or bootstrap—are the subject of the latest episode of the show, with Kirsten Lum, the CTO of storytellers.ai, joining us to discuss strategies and tactics for the technically-non-technical analyst to thrive in an increasingly technical analytics world. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you biased? Either you answered, “Yes,” or you’re in denial. Or you’re an AI, in which case you should just go and start your own podcast instead of listening to this one. UNLESS your prediction algorithms told you that this would be the episode where we would finally announce the addition of a third co-host, and you need to collect that data point (and, damn, you’re good, BTW). On this episode, though, our THREE (count ‘em!) co-hosts dive into different types of biases that analysts (should) grapple with, how they spot them, and what they do to take advantage of them (or mitigate them, as appropriate). 

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Sure I like your theory guys, but I want to hear some stories from the trenches! Episode 11 is all about the anecdotes, with the guys sharing stories about work they've done as analysts that had the most impact. Whether from hard work or a moment of inspiration, big wins with analysis are sometimes few and far between Hear some great examples from Michael, Tim and Jim's personal experiences. What has 6 legs, three microphones and will make you a better analyst in 42 minutes? It's the Digital Analytics Power Hour.

What’s good at math and has more suitors than Taylor Swift? A digital analyst. There’s an unprecedented number of available jobs, along with aggressive recruiters, higher salaries, and better titles. When should a digital analyst choose to take a new gig? What should they consider? In episode 4, the 3 Amigos of measurement tackle this difficult question with best practices and personal anecdotes. We say it’s an hour, we only used 50 minutes, and it’s so fun to listen to it will feel like 15.

How does one build a strong culture of experimentation at an organization (and what does that even mean)? One way is to spend a few years working at a company that already has such a culture… and then jump ship to another organization that is well on its way! That's (sort of) what our guest, Lukas Vermeer, did when he left booking.com to go to Vista. With Val Kroll guest-co-hosting, we dug into the challenges — organizational, educational, and mindset-al (?) — when it comes to having an organization successfully and appropriately integrate experimentation into their operational ways. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's not only the year of mobile...AGAIN, but it's the year of mobile measurement! While our intrepid hosts have all tagged an app or two in their day, they thought it would be entertaining to be joined by someone meek, unopinionated, and inexperienced in the world of mobile analytics who would sit back and tell them how wise they were. But, instead, they recruited Lee Isensee from Search Discovery. Displaying his Bostonian politeness with lines like, "I'm sorry to cut you off again Michael, but I'm not," Lee weighs in on the subject with grace and wisdom. This episode has mobile proportions, in that we squeezed an hour into a very mobile 40 minutes.

It's a challenge as old as the smartphone (or...technically... a little bit older): we want to track and know people, but, when they're visiting us from their phone, their tablet, their work laptop, and their home desktop, we often really just know cookies and device IDs! Countless vendors tout their enabling technology...but then admit that, yes, you do have to get your customers to authenticate on all devices to provide a common key, which isn't always easy (or even remotely reasonable). Unless you're up for diving into the deep and murky waters of probabilistic linking. On this episode, Tablet Tim -- the only one of the co-hosts who is an avid tablet user -- argues that the whole topic should be pretty "meh" for many companies, while Mobile Moe smacks him down and shares her experiences (the challenges...and the wins that made them worth it!) with linking users across mobile apps, a mobile site, and a desktop site.

For complete show notes, visit the show page.

It's the end of the year, and we know it, and we feel fiiiiine. Or, maybe we have a little anxiety. But, for the fifth year in a row, we're wrapping up the year with a reflective episode: reflecting on changes in the analytics industry, the evolution of the podcast, and the interpersonal dynamics between Tim and Michael. From the state of diversity in the industry (and on the show), to the trends in analytics staffing and careers, to the growing impact of ethical and privacy considerations on the role of the analyst, it's an episode chock full of agreement, acrimony, and angst. And, it's an episode with a special “guest;” it's the first time that producer Josh Crowhurst is on mic doing something besides simply keeping our advertisers happy! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're seeing the title "Analytics Engineer" continue to rise, and it’s in large part due to individuals realizing that there's a name for the type of work they've found themselves doing more and more. In today's landscape, there's truly a need for someone with some Data Engineering chops with an eye towards business use cases. We were fortunate to have the one of the co-authors of The Fundamentals of Analytics Engineering, Dumky de Wilde, join us to discuss the ins and outs of this popular role! Listen in to hear more about the skills and responsibilities of this role, some fun analogies to help explain to your grandma what AE's do, and even tips for individuals in this role for how they can communicate the value and impact of their work to senior leadership! 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.

What causes us to keep returning to the topic of causal inference on this show? DAG if we know! Whether or not you're familiar with directed acyclic graphs (or… DAGs) in the context of causal inference, this episode is likely for you! DJ Rich, a data scientist at Lyft, joined us to discuss causality — why it matters, why it's tricky, and what happens when you tackle causally modelling the complexity of a large-scale, two-sided market! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's hard enough keeping up with the times when digital analytics is exclusively Desktop/Mobile/Tablet devices. Now, what if we had to work with data that came from everything? Join us this episode where we lean heavily on the wisdom and experience of Intel's David McBride, and talk about the Internet of Things, Measurement, and perhaps Millennials - all for the low low price of 50 minutes of your time.

People, places, and things reference in this episode include:

• Kickstarter wearables projects
• Faraday Cage
• IFTTT (If This Then That)
• Maker Faire
• Qualcomm
• MIT Media Lab
• Tom Emrich
• Intel Curie
• Raspberry Pi
• SMS Audio

What does neuroscience have to do with the work of the analyst? It turns out that neuroplasticity is to the modern analyst what plastics were to Benjamin Braddock, and it all comes down to Hebb's Law. Or, put another way, successfully working with peers and stakeholders can take some focused effort, some feedback, and some practice, and that's what "coach" James Hayes joined the episode to discuss! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Data gets accessed and used in an organization through a variety of different tools (be they built, bought, or both). That work can be quick and smooth, or it can be tedious and time-consuming. What can make the difference, in modernspeak, is the specifics of the "data products" and "data platforms" being used for those tasks. Those specifics, in turn, often fall on the shoulders of (data) product managers! In this episode, Austin Byrne, Group Product Lead for Data at Canva, joined us for a discussion about the similarities and differences between typical product management and data product management! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you long for the days when your mother could ask you, "Now, what do you actually do for your job?" and "all" you had to do was explain websites and digital analytics? The "analyst" is now a role that can be defined an infinite number of ways in its breadth and depth. Is the analyst who is starting to do data transformations to create clean views still an analyst? Or is she a data engineer? A data scientist? On this episode, we explore the idea of an "analytics engineer" with Claire Carroll from Fishtown Analytics who, while she did not coin the term, can certainly be credited with its growth as a concept. And there is a brief but intense spat about the role of "analytics translator," which Claire sat out, but observed with bemusement. 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.

Are you a data scientist? I mean, are you really a data scientist? What does that even mean...other than a healthy salary increase? On this episode of the show, Ian Thomas, Chief Data Officer for Publicis Spine sat down with the three co-citizen-data-scientists who regularly host the show to delve into the subject!

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Philosopher, poet, and essayist George Santayana wrote, "Those who cannot remember the past are condemned to repeat it." We thought we'd have him on to reflect about the history of digital analytics...but he died in 1952. Ambrose Bierce wrote The Devil's Dictionary, which we think is brilliant, so we thought we would have him on...but he died in 1842! Lucky for us, we landed the best of both worlds with very-much-alive philosopher, poet, essayist, DAA founder and chairman, and eMetrics founder Jim Sterne.

People, places, and things mentioned in this episode officially ran a full, certifiable gamut:

• The Devil's Data Dictionary
• The Digital Analytics Association (DAA)
• eMetrics
• The Web Analyst's Code of Ethics
• Some "web analytics" platforms: Sawmill (still going strong!), Analog (less so), NetGenesis (verymuchlessso)
• The IAB
• The DMA
• A bunch of people (or, in one case, an archetype, and, in another a conscious, gestalt, artificial intelligence system): Krista Seiden, Seth Romanow, Eric Peterson, June Li, Stéphane Hamel, Josh Aberant, HiPPOs, Skynet

What does Machiavelli have to do with International Women's Day? It turns out, if you read his writings through the lens of the challenges that women face in the workplace, then quite a lot! So much, in fact, that a whole book could be written on the subject. And one was! Machiavelli for Women author Stacey Vanek Smith joins Moe and guest host Julie Hoyer for this women-only episode of the show. If you're a dude and think this show isn't for you, too, then think again! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you know someone who always seems to have read the latest books and can cite concepts and ideas and authors and titles in any situation? Do you hate that person? Honestly, so do we. But that didn't stop us from recording an episode that, potentially, will grate on your nerves in such a way that you have to draw on your inner grit (Grit: The Power of Passion and Perseverance by Angela Duckworth) to get through it. But, with luck, there will be some good ideas that make it into your long-term memory (Brain Rules: 12 Principles for Surviving and Thriving at Work, Home, and School by John Medina), and it will be information delivered in a gender-neutral manner, unlike so much of the world (Invisible Women: Exposing Data Bias in a World Designed for Men by Caroline Criado-Perez). Give it a shot, though. It may help you become a better leader in your organization (Dare to Lead by Brené Brown).

Unfortunately, we lost some of this episode (even our recording platform was tired of hearing about books?). We know what we talked about then, even if we have no audio record, so we've included those books in the show notes as well. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

This topic was such a big deal that we managed to have no guests, and yet we had five people on the mic! Why? Because this episode doubles as a marker of a shift in the show itself. Beyond that, though, we had a lively discussion about how every business stakeholder professes to being committed to being data driven. That should make every stakeholder super easy to work with, right? And, yet, analysts often find themselves struggling to get on the same page with their counterparts due to the realities of the data: what it can and can't do and how it is most effectively worked with. Not a small topic! There were even pop quizzes (feel free to let us know how you'd score the answers)! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

One of the biggest challenges for the analyst or data scientist is figuring out just how wide and just how deep to go with stakeholders when it comes to key (but, often, complicated) concepts that underpin the work that's being delivered to them. Tell them too little, and they may overinterpret or misinterpret what's been presented. Tell them too much, and they may tune out or fall asleep… and, as a result, overinterpret or misinterpret what's been presented. On this episode, Dr. Nicholas Cifuentes-Goodbody from WorldQuant University joined Julie, Val, and Tim to discuss how to effectively thread that particular needle. 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.

"QA and patience and reviews by a peer. Data viz testing, hold no chart too dear. Don’t be an asshole; automate 'til it stings. These are a few of our favorite things!" With apologies to Julie Andrews, on this episode, Moe, Tim, and Michael shared some of the tactical tips and techniques that they have found themselves putting to use on a regular basis in their analytics work. The resulting show: multiple tips, minimal disagreements, and moderate laughter. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We're not sure what's going on with this episode. For some reason, we have a bunch of first-time listeners, and they're all from Apple devices! Maybe it's because the show only comes out every two weeks, and the first-party cookies we've been using to track our listeners are now expiring after seven days! (This is a hilarious episode description if you're well-versed in the ins and outs and ethical and philosophical aspects of WebKit's Intelligent Tracking Prevention (ITP) 2.1. If you're not, then you might want to listen to the gang chat with Kasper Rasmussen from Accutics about the topic, as it's likely already impacting the traffic to your site!)

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Every consumer is now aware, at some level, that they are constantly generating data simply by moving through the world. And, every organization that puts physical devices or digital experiences into the paths of consumers has to make decisions about what data they collect, how they manage it, and what they do with it (both the immediate plans and what unknown plans may emerge in the distant future). The questions, decisions, and mindsets that this reality brings into play are just one big gray area after another. Angela Bassa grapples with these issues on a daily basis both professionally and personally, so we sat down with her for a lively and thought-provoking 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.

We try not to navel gaze too much on this show, but our 200th episode felt like just enough of a milestone that we could do a mid-year "look back, look forward" show with a 7-year range. And we tracked down our original Commonwealth representative to join us for that discussion. Did we (first) party (cookie) like it was 1999? Maybe not, but that's the sort of reference you get with Jim Cain, the founder of Napkyn Analytics, and a co-founder of this very podcast! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's a nasty rumor, but we heard that there are a couple of domains out there on the interwebs that are not pure play eCommerce or online lead gen sites. Why, there are government sites and nonprofit sites and CPG sites and academic institution sites and content sites and more! What is a poor digital analyst to do in the absence of a clear online conversion to measure and optimize towards? Give this less-than-two-thirds-of-an-hour episode a listen to hear our multinational and ruggedly good looking co-hosts wax eloquently (or, at least, wax) on the subject.

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!

Let's pretend your goal as an analyst is to eloquently and accurately explain reinforcement learning. Now, let's pretend that you get to try that explanation again and again, and we'll give you an electric shock every time you state something inaccurately and a cookie every time you say something right. Well, you're an analyst, so you're now wondering if this is some clever play on words about cookies. As it happened, we didn't give Matt Gershoff from Conductrics any cookies of any kind in his return to the show. Instead, we gave him a lifetime's supply of opportunities to say, "Well, no, it's not really like that," which is a special kind of nourishment for the exceptionally smart and patient! In other words, the gang walked through a range of analogies and examples about machine learning, reinforcement learning, and AI with Matt, and no electric sheep were harmed in the process. 

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.

Listen. Really. That's what you can do. You can listen to this episode and find out what you learn. Or you can NOT listen to the show and NOT find out what you learn. You can't do both, which means that, one way or the other, you WILL be creating your very own counterfactual! That, dear listener, is a fundamental concept when it comes to causal inference. Smart analysts and data scientists the world over are excited about the subject, because it provides a means of thinking and application techniques for actually getting to causality. Bradley Fay from DraftKings is one of those smart data scientists, so the gang sat down with him to discuss the subject! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

These are interesting times in which we work, are they not? For many analysts, "remote work" is what they call "every weekday" or, for those who don't have things fully figured out, "every day that ends in 'Y.'" For other analysts, the current pandemic has forced them into being an unplanned — and not necessarily desired — full-time remote worker. Juggling kids, silencing pets, finding a horizontal work surface, and grappling with which pair of sweatpants to don are all the sorts of challenges (opportunities?!) that remote working can bring. On this show, we explore our experiences and thoughts and tips on the topic. Except for Tim, who thinks remote work is like in-office work: "Leave me alone, and just do your job!" For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Broadly writ, we’re all in the business of data work in some form, right? It’s almost like we’re all swimming around in a big data lake, and our peers are swimming around it, too, and so are our business partners. There might be some HiPPOs and some SLOTHs splashing around in the shallow end, and the contours of the lake keep changing. Is lifeguarding…or writing SQL…or prompt engineering to get AI to write SQL…or identifying business problems a job or a skill? Does it matter? Aren’t we all just trying to get to the Insights Water Slide? Katie Bauer, Head of Data at Gloss Genius and thought-provoker at Wrong But Useful, joined Michael, Julie, and Val for a much less metaphorically tortured exploration of the ever-shifting landscape in which the modern data professional operates. Or swims. Or sinks? For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

As an analyst, it's never a good idea to make predictions without data. With that said, for our first predictions episode, we've chosen to make some big and small predictions for the digital analytics space for the remainder of 2016 -- using only experience and intuition! Join us in Episode 30 as we rely solely on intuition to predict the next 9 months of a multi-billion dollar industry - all in under 45 minutes. Note: Due to the lag between recording and release, our prediction during the episode about a certain Heisman Trophy winner actually came true...before this episode launched.

People, places, and things mentioned in this episode:

• Tealium
• Ensighten
• Signal
• Mixpanel
• Amazon Redshift
• Looker
• Adobe Analytics
• Google Analytics
• Optimizely
• Adobe Target
• Johnny Manziel
• Cleveland Browns
• Paul DePodesta
• Moneyball
• Ben Gaines
• Median Absolute Deviation (MAD)
• Brian Clifton
• Domo
• Sweetspot Intelligence
• Tableau Software
• eMetrics
• "I Predict a Riot" (Kaiser Chiefs)

Do you know something that is really simple? Really Simple Syndication (aka, RSS). Did you know that RSS is the backbone of podcast delivery? Well, aren't you clever! What's NOT really simple is effectively measuring podcasts when a key underlying component is a glorified text file that tells an app how to download an audio file. Advertisers, publishers, and content producers the world over have been stuck with "downloads" as their key -- and pretty much only -- metric for years. That's like just counting "hits" on a website! But, NPR is leading an initiative to change all that through Remote Audio Data, or RAD. Stacey Goers, product manager for podcasts at National Public Radio, joins the gang on this episode to discuss that effort: how it works, how it's rolling out, and the myriad parallels podcast analytics has to website and mobile analytics!

“For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Cookies are getting aggressively expired or blocked outright. Referring site information is getting stripped. Adoption of Brave as a browser is on the rise! Yet, marketers still need to quantify the impact of their investments. What is an analyst to do? Does the answer lie in server-side technical solutions? Well, it's not a bad idea to consider that. But, it's almost certainly not "the answer" to the multi-touch attribution question(s). Arguably, a better solution was one proposed by Jan Baptist van Helmont in 1648: randomized controlled trials. On this episode, data scientist Dr. Joe Sutherland returns to the show to talk about the ins and outs of problem formulation, experimental design, the cost of data, and, ultimately, causal inference. This is one of those rare shows where there actually IS a solution to a problem that vexes analysts and their stakeholders. The trick is really just getting the industry to understand and apply the approach! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

While we don’t often call it out explicitly, the driving force behind much of what and how much data we collect is driven by a "just in case" mentality: we don't know exactly HOW that next piece of data will be put to use, but we better collect it to minimize the potential for future regret about NOT collecting it. Data collection is an optionality play—we strive to capture "all the data" so that we have as many potential options as possible for how it gets crunched somewhere down the road. On this episode, we explored the many ways this deeply ingrained and longstanding mindset is problematic, and we were joined by the inimitable Matt Gershoff from Conductrics for the discussion! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Humans are creatures of habit. And analysts — those of us who haven’t been so drawn into the world of artificial intelligence that we have become cyborgs, at least — are humans. In this episode, the gang explores the good and the bad side of analytical habits: what analyses we gravitate towards, how we go about approaching those analyses, and, to some extent, how those habits are impacted by our organizational environments. With a side dish of, “What is a data scientist, anyway?” (because who can resist a question that is both rhetorical AND controversial?!).

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Want to hear Jim Cain wing it for 40 minutes as he discusses a survey he never read? Interested in hearing Tim Wilson boil with incandescent rage as Jim triggers the Indiana Jones style pressure pads under his overpriced hotel snacks? Then this is the episode for you! Michael, Jim and Tim talk through the responses they found most interesting (VERY heavy use of that word in this episode) from the Duke University Fuqua School of Business's bi-annual CMO survey (cmosurvey.org).

Have you ever read an analytics job description? Have you found yourself wondering, "Is it just me, or is there something fishy going on here?" Who better to verbally cogitate this question writ large than a couple of guys who haven't actually applied for a job in a few years? Join Michael and Tim as they dive into the world of analytics job descriptions and chat about the red flags they find...and the various tangential thoughts that the exercise itself sparks. Resources mentioned in this episode include: the Digital Analytics Association, Google Tag Manager Updates: Workspaces and User Manager by Amanda Schroeder from LunaMetrics, Revamped User Interface in Google Tag Manager by Simo Ahava.

It's that one-time-of-the-year when we do a little bit of navel-gazing, a little bit of prognostication, and, when the year is a year like 2022, a little more cursing than usual. Not only did the podcast hit a fairly meaningless vanity metric milestone this year, but we also maintained our explicit rating! Executive producer Josh Crowhurst joined us to look back on the podcast and the analytics industry in 2022, as well as to do a little bit of crystal ball gazing into 2023 and beyond! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

How is an outlier in the data like obscenity? A case could be made that they're both the sort of thing where we know it when we see it, but that can be awfully tricky to perfectly define and detect. Visualize many data sets, and some of the data points are obvious outliers, but just as many (or more) fall in a gray area—especially if they're sneaky inliers. z-score, MAD, modified z-score, interquartile range (IQR), time-series decomposition, smoothing, forecasting, and many other techniques are available to the analyst for detecting outliers. Depending on the data, though, the most appropriate method (or combination of methods) for identifying outliers can change! We sat down with Brett Kennedy, author of Outlier Detection in Python, 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. 

This episode originally aired on December 17, 2019.

Our podcast junkie co-host heard the following statement on another podcast a while back when he was out for a jog: "I actually think the word 'uncertainty' is used in English in a very different way than the word 'uncertainty' is used in statistics." He almost ran into a tree (causation is unclear: he's not known for his gross motor skills, which may have been a confounder). Not only is that quote, essentially, the theme for this episode, but the person who said it, Dr. Rebecca Goldin from George Mason University, was our guest! And we are absolutely CERTAIN that it was every bit as enlightening a discussion as it was a fun one! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

There comes a time in every analyst's career where they consider starting up their own consultancy. Or, if not that, then at least joining an agency or a consultancy. The nature of most businesses is to grow, and with growth comes the potential for an "exit." This episode dives into that world in an attempt to demystify some of the ins and outs of the acquisition of analytics consultancies, from the owners' perspectives, employees' perspectives, and acquiring companies' perspectives. Since these are all perspectives that none of your dear co-hosts really have, Bob Morris, the co-founder and managing partner for Bravery Group, joined us for a discussion of EBITDA, TTM, CIMs, and even aspects of the space that are not captured by acronyms! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Back by popular demand: attribution! This time, we brought in an adult on the subject: Jim Novo of The Drilling Down Project. A lot of questions get tackled in this episode: Should "gut feel" ever trump "the data?" Which is a better analogy for attribution: PV=nRT or the distillation of bourbon? Will this podcast *ever* have flawless audio quality? These questions and more definitively answered. All in under 52 minutes.

Did you know that there were monks in the 1400s doing text-based sentiment analysis? Can you name the 2016 movie that starred Amy Adams as a linguist? Have you ever laid awake at night wondering if stopword removal is ever problematic? Is the best therapist you ever had named ELIZA? The common theme across all of these questions is the broad and deep topic of natural language processing (NLP), a topic we've been wanting to form and exchange words regarding for quite some time. Dr. Joe Sutherland, the Head of Data Science at Search Discovery, joined the discussion and converted many of his thoughts on the subject into semantic constructs that, ultimately, were digitized into audio files for your auditory consumption. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

The data has problems. It ALWAYS has problems. Sometimes they're longstanding and well-documented issues that the analyst deeply understands but that regularly trip up business partners. Sometimes they're unexpected interruptions in the data flowing through a complex tech stack. Sometimes they're a dashboard that needs to have its logic tweaked when the calendar rolls into a new year. The analyst often finds herself on point with any and all data problems—identifying an issue when conducting an analysis, receiving an alert about a broken data feed, or simply getting sent a screen capture by a business partner calling out that something looks off in a chart. It takes situational skill and well-tuned judgment calls to figure out what to communicate and when and to whom when any of these happen. And if you don't find some really useful perspectives from Julie, Michael, and Moe on this episode, then we might just have a problem with YOU! (Not really.) For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We were curious about… curiosity. We know it's a critical trait for analysts, but is it an innate characteristic, a teachable skill, or some combination of both? We were curious. How can the breadth and depth of a candidate's curiosity be assessed as part of the interview process? We were curious. Who could we kick these questions (and others) around with? We were NOT curious about that! MaryBeth Maskovas, founder and Principal Consultant at Insight Lime Analytics, joined Michael, Julie, and Tim to explore the topic. 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.

Ethics in AI is a broad, deep, and tough subject. It's also, arguably, one of the most important subjects for analysts, data scientists, and organizations overall to deliberately and determinedly tackle as a standard part of how they do work. On this episode, Renée Cummings, Professor of Practice in Data Science and Data Activist in Residence at the University of Virginia (among many other roles), joined us for a discussion of the subject. Her knowledge of the topic is as deep as her passion for it, and both are bordering on the limitless, so it was an incredibly informative chat! 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.

The world is an oyster. It's also a system. A complex system! Companies are components in that system, and they're systems unto themselves! And marketing departments, and digital marketing, and the data therein, are systems, too. As analysts, we're looking for pearls in these systems (and you were wondering where we were going with this)! Join Michael and Tim as they chat with Christopher Berry of the Canadian Broadcasting Corporation (CBC) about "systems thinking." You'll be smarter for it! As a special "feature" (not a bug!) for this episode, we've done a bit of a throwback to the earliest days of this podcast, in that Michael's audio sounds a little bit like he was chatting through a tin can with a string tied to it. We apologize for that! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's human nature to want to compare yourself or your organization against your competition, but how valuable are benchmarks to your business strategy? Benchmarks can be dangerous. You can rarely put your hands on all the background and context since, by definition, benchmark data is external to your organization. And you can also argue that benchmarks are a lazy way to evaluate performance, or at least some co-hosts on this episode feel that way! Eric Sandosham, founder and partner at Red & White Consulting Partners (and prolific writer), along with Moe, Tim, and Val break down the problems with benchmarking and offer some alternatives to consider when you get the itch to reach for one! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

You've got some solid experience under your belt, and you're starting to feel like you're ready to move into a data leadership role. What does that even mean? Shifting your keystrokes from SQL to slide decks? Maybe (but maybe not). Katie Bauer, Head of Data at GlossGenius, has held multiple data leadership roles over the course of her career, and she penned a thoughtful post on the various tactics she employed to find a role that is a good fit. She wrote the post so that she wouldn't have to keep repeating herself when data folks in her network reached out for advice. But that didn't stop this podcast from reaching out to record a lively discussion on the topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Are you down with ITP? What about ETP? Are you pretty sure that the decline in returning visitors to your site that has everyone in a tizzy is largely due to increasingly restrictive cookie handling by browsers? Do you really, really, REALLY want Google, Apple, Mozilla, and even Microsoft to get on the same page when it comes to cookie handling and JavaScript subtleties? So many questions! Lucky for us (and you!), Measure Slack legend (and L.L. Bean Senior Programmer/Analyst) Cory Underwood has some answers. Or, at least, he will depress you in delightful ways. For complete show notes, including links to items mentioned in this episode, a transcript of the show, and an update on ITP 2.3 from Cory, visit the show page.

Back in the day, we explained the difference between a visitor, a visit, and a pageview to stakeholders using an analogy of a person walking into a physical store. Now, digital channels are dominating, and physical stores are struggling...which is an opportunity to apply what we've learned about behavioral analysis on the web to in-(REAL)-store consumer behavior. Gary Angel from Digital Mortar (@digitalmortar) returned to the show (our first ever repeat guest!) to walk us through the many, many similarities, as well as to explain some of the unique challenges and opportunities of in-store analytics. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We had a hypothesis that our listeners might be interested in hearing an expert on digital optimization. In this episode we test that hypothesis. Listen and learn as Kelly Wortham from EY runs circles around the lads, and brings them to an understanding of what digital testing means in 2015. In an hour so optimized it only takes 45 minutes, it's 2015's penultimate episode of the Digital Analytics Power hour.

People, places, and things mentioned in this episode include:

• Taguchi vs. Full Factorial test design
• kelly dot wortham at ey dot com (to get added to Kelly's twice-monthly testing teleconference)

Attribution is like a box of chocolates. It can be really expensive, or it can be really cheap. It requires making a lot of decisions as to how you actually want to consume it. It may leave you feeling ill! Join the guys for a 45-minute walk across the attribution landscape. And back. And back again. Because mama always said you shouldn't stop at the last click.

Once upon a time, in an industry near and dear, lived an analyst. And that analyst needed to present the results of her analysis to a big, scary, business user. This is not a tale for the faint of heart, dear listener. We're talking the Brothers Grimm before Disney got their sugar-tipped screenwriting pens on the stories! Actually, this isn't a fairy tale at all. It's a practical reality of the analyst's role: effectively communicating the results of our work out to the business. Join Michael and Tim and special guest, Storytelling Maven Brent Dykes, as they look for a happy ending to The Tale of the Analyst with Data to Be Conveyed.

Tangential tales referenced in this episode include: Web Analytics Action Hero, Brent Dykes Articles on Forbes.com, The Wizard of Oz, Made to Stick, Data Storytelling: The Essential Data Science Skill Everyone Needs, The Story of Maths, and mockaroo.com.

What does it really take to bring data science into the enterprise? Or... what does it take to bring it into your part of the enterprise? In this episode, the gang sits down with Dr. Katie Sasso from the Columbus Collaboratory...because that's similar to what she does! From the criticality of defining the business problem clearly, to ensuring the experts with the deep knowledge of the data itself are included in the process, to the realities of information security and devops support needs, it was a pretty wide-ranging discussion. And there were convolutional neural networks (briefly).

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

To win at digital measurement in 2015, you need more data capture tools than just your web analytics tool of record. In episode 3, the 3 musketeers of measurement try to outline what they feel the core tools are that any digital analyst should be familiar with and thinking about. What do you need to have? What should you want to have? What should you be careful about? Get ready to have the hype separated from the important, and done so efficiently it will make an hour feel like 43 minutes.

In this inaugural episode of the Digital Analytics Power Hour, Michael, Jim, and Tim discuss how a digital analyst working today can become better at what he or she does. Tending bar? Cold calling the CFO? The guys share their own origin stories -- what drew them to web analytics, as well as what made them stay -- and tell a few tales of what worked for them as they evolved their own careers.

Do you have a spare shingle lying around? Have you been thinking about painting, "Analyst for Hire - Will Work for Cookies" on it and hanging it up on your front door? It seems like a lot of analysts are pondering whether the next company they should work for should be their own. Adam Ribaudo did just that (figuratively -- we have no evidence of an actual painted shingle) 2.5 years ago. He now works for Noise to Signal, a company he joined...just as soon as he founded it! On this episode, we grill Adam about how he keeps his vast workforce in line, as well as what his thoughts are about the decisions made by Noise to Signal's upper management.

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

It's a lot of work to produce each episode of this show, so we were pretty sure that, by this time, we would have just turned the whole kit and kaboodle over to AI. Alas! It seems like the critical thinking and curiosity and mixing of different personalities in a discussion are safely human tasks… for now. Dr. Brandeis Marshall joined Michael, Julie, and Moe for a discussion about AI that, not surprisingly, got a little bleak at times, but it also had a fair amount of hope and handy perspectives through which to think about this space. We recommend listening to it rather than running the transcript through an LLM for a summary! 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 built a data-related "thing" — a dashboard, a data catalog, an experimentation platform, even — only to find that, rather than having the masses race to adopt it and use it on a daily basis, it gets an initial surge in usage… and then quietly dies? That's sorta' the topic of this episode. Except that's a pretty clunky and overly narrow summary. Partly, because it's a hard topic to summarize. But, data as a product and data products are the topic, and Eric Weber, the data scientist behind the From Data to Product newsletter, joined us for a discussion that we've been trying to make happen for months. It was worth the wait! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Do you care about acquiring customers? Do you care about data? Do you like wearing shoes that have soles that are 2-3″ thick? Put those three things together and it means you care — or should care — about customer data platforms. On this episode, Todd Belcher from BlueConic joins us to explain what CDPs are and what they’re good for. Tune in to hear Todd masterfully steer clear of a sales pitch for his company…while Michael transitions on the fly from getting a basic understanding of CDPs…to installing BlueConic on this site…to pitching BlueConic himself!

For complete show notes, including links and the show transcript, go to: http://www.analyticshour.io/2017/01/03/053-customer-data-platforms-with-todd-belcher-2/.

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.

As a general rule, analysts are drawn to precision: let's understand the business problem and then go figure out how the data can be acquired and crunched to provide something specific and useful. Fair enough. Where, then, do pencil and paper and 10-second sketches fit in? Or hastily and collaboratively drawn flippy chart or whiteboard sketches? We could draw you a picture to explain, but podcasts are an audio medium, so, instead, we brought on the illustrious illustrator, consultant, and author, Dan White. From triangles, to rolling snowballs, to trees, to Venn diagrams, to the conjoined triangles of success, this episode paints a pretty clear picture of the power of the quick sketch! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Who would have thought that we'd get to 2020 and still be debating whether recurring reports should include "insights?" As it turns out, Tim did an ad hoc analysis back in 2015 where he predicted exactly that! Unfortunately, the evidence is buried in the outbox of his email account at a previous employer. So, instead, we've opted to just tackle the topic head-on: what is a report, anyway? What are the different types of reports? What should they include? What should they leave out? And where does "analysis" fall in all of this? We have so many opinions on the subject that we didn't even bring on a guest for this episode! So, pop in your earbuds, pull out your notebook, and start taking notes, as we'll expect a *report* on what you think of the show once you're done giving it a listen! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Fifteen years ago, digital analytics tooling was pretty straightforward (something that looks at log files). In 2015, there are literally hundreds of tools that can be used to measure every aspect of a digital sales and marketing ecosystem. Most companies still think “Google or Adobe?” when making a digital analytics tool purchase. Are they missing out? With very special guest Hiten Shah from KISSmetrics, Michael, Tim and Jim talk a little tooling and a lot of trash - in almost 60 minutes.

Here at the Analytics Power Hour, we have a very clear delineation of who owns what when it comes to the show production. And ownership is the topic of this episode. It's possible that the owner of the episode description feels like this is an awfully touchy-feely topic, but said owner also knows that teamwork means going along with the majority when it comes to show topics. I guess that's joint ownership? Can that work? Sadly, that, specifically, was not discussed, but the show definitely earned its explicit rating with this episode! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

We thought we deserved a break from the podcast, so we went looking for some AI to take over the episode. Amazon Polly wasn't quite up to the task, unfortunately, so we wound up sitting down as humans with another human -- Erik Driessen from Greenhouse -- to chat about the different ways that automation can be put to use in the service of analytics: from pixel deployment to automated alerts to daily reports, there are both opportunities and pitfalls!

For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

KPIs? Really? It’s 2024. Can’t we just ask Claude to generate those for us? We say… no. There are lots and lots of things that AI can take on or streamline, but getting meaningful, outcome-oriented alignment within a set of business partners as they plan a campaign, project, or initiative isn’t one of them! Or, at least, we’re pretty sure that’s what our special guest for this episode would say. He’s been thinking about (and ranting about) organizations’ failure to take goal establishment, KPI identification, and target-setting seriously enough for years (we found a post he wrote in 2009 on the subject!). He also really helped us earn our explicit tag for this episode — scatologically and onanistically, we’re afraid. But solid content nonetheless, so hopefully you can hear past that! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

One year of shows. It was our initial Big Hairy Audacious Goal, and we did it. We hoped you had as much fun this year listening as we did recording, and we'd like to take a chance to reflect. Did we hit our initial KPIs (because of course we had them)? Did we have a favorite show? Is there something we'd like to do next year? Tune in and end 2015 by listening to a podcast about a podcast. We think our navels look awesome. Come gaze with us!

It’s 1:00 AM, and you can’t sleep. The paid search manager needs to know whether brand keywords can be turned off without impacting revenue. The product team needs the latest A/B test results analyzed before they can start on their next sprint. The display media intern urgently needs your help figuring out why the campaign tracking parameters he added for the campaign that launches in two days are breaking the site (you’re pretty sure he’s confusing “&” and “?” again). And the team running the site redesign needs to know YESTERDAY what fields they need to include in the new headless CMS to support analytics. You’re pulled in a million directions, and every request is valid. How do you manage your world without losing your sanity? On this episode, analytics philosopher Astrid Illum from DFDS joins the gang to discuss those challenges. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

Every year kicks off with an air of expectation. How much of our Professional Life in 2025 is going to look a lot like 2024? How much will look different, but we have a pretty good idea of what the difference will be? What will surprise us entirely—the unknown unknowns? By definition, that last one is unknowable. But we thought it would be fun to sit down with returning guest Barr Moses from Monte Carlo to see what we could nail down anyway. The result? A pretty wide-ranging discussion about data observability, data completeness vs. data connectedness, structured data vs. unstructured data, and where AI sits from an input and an output and a processing engine. And more. Moe and Tim even briefly saw eye to eye on a thing or two (although maybe that was just a hallucination). For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.