Sometimes, the best way to get alignment, generate new ideas, hash out different perspectives, or just effectively collaborate is to shift a gathering of peers from being a "meeting run by the organizer" to a "workshop run by a facilitator." Both meetings and workshops should have clear objectives, but workshops, when planned and run well, shift the mindset of the participants even before they arrive in the meeting room (which may make sense to have as a room at an off-site location). On this episode, we chat with master facilitator Jody Weir from THE ICONIC about her experiences, tips, and techniques for running an effective workshop. If you haven't committed to run one by the end of the show, then Michael failed in his role as podcast facilitator. 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.

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.

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.

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.

"QA and patience and reviews by a peer. Data viz testing, hold no chart too dear. Don't be an a*e; 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.

Once upon a time, website behavioral data was extracted from the log files of web servers. That data was messy to work with and missing some information that analysts really wanted. This was the OG "server-side" data collection. Then, the JavaScript page tag arrived on the scene, and the data became richer and cleaner and easier to implement. That data was collected by tags firing in the user's browser (which was called "client-side" data collection). But then ad blockers and browser quirks and cross-device behavior turned out to introduce pockets of unreliability into THAT data. And now here we are. What was old is now somewhat new again, and there is a lot to be unpacked with the ins and outs and tradeoffs of client-side vs. server-side data collection. On this episode, Mike Robins from Poplin Data joined the gang to explore the topic from various angles. 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.

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.

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

How accurate is your data? How accurate is any of our data? If our data is more accurate, will we make better decisions? How MUCH better? Why do the show blurbs of late have so many questions? THAT is a question we can ACCURATELY answer: because the shows grapple with challenging questions! On this episode, Snowplow co-founder Yali Sassoon joined us to chat about the nuts and bolts of data accuracy: the inherent messiness of client-side tracking (but, also, the limitations of server-side tracking), strategies of incrementally improving data accuracy (and the costs therein), and the different types of scenarios where different aspects of data accuracy matter in different ways! Pour yourself a drink (a 2 oz. shot of a fine Scotch will do... which would be 59.1471 ml if you want an accurate and precise metric pour), settle in, and give 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.

READ ME!!! LISTEN!!! DO YOU KNOW WHY THIS IS IN ALL CAPS?! IS IT RAISING YOUR HEART RATE?! IS IT MAKING YOU A LITTLE IRRITATED?! IT MIGHT BE! IF IT IS, WE COULD MEASURE IT, AND MAYBE WE WOULD REALIZE THAT WE WERE INDUCING A SUBCONSCIOUS EMOTIONAL RESPONSE AND REALLY SHOULD TURN OFF THE CAPS LOCK! That's the topic of this episode: the brain. Specifically: neuroscience. Even more specifically: neurodesign and neuromarketing and the measurement and analytics therein. We're talking EEGs, eye tracking, predictive eye tracking, heart rate monitoring, and the like (and why it matters) with Diana Lucaci from True Impact. 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.

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

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.

Have you ever noticed that 68.2% of the people who explain machine learning use a "this picture is a cat" example, and another 24.3% use "this picture is a dog?" Is there really a place for machine learning and the world of computer vision (or machine vision, which we have conclusively determined is a synonym) in the real world of digital analytics? The short answer is the go-to answer of every analyst: it depends. On this episode, we sat down with Ali Vanderveld, Director of Data Science at ShopRunner, to chat about some real world applications of computer vision, as well as the many facets and considerations therein! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

What percentage of digital ad impressions and clicks do you think is actually the work of non-human bots? Pick a number. Now double it. Double it again. You're getting close. A recent study by Pixalate found that 19 percent of traffic from programmatic ads in the U.S. is fraudulent. David Raab from the CDP Institute found this number to be "optimistic." Ad fraud historian Dr. Augustine Fou, our guest on this show, has compelling evidence that the actual number could easily be north of 50 percent. Why? Who benefits? Why is it hard to tamp out? Is it illegal (it isn't!)? We explore these topics and more 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.

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.

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.

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.

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.

Did you hear the one about how the AI eliminated cancer? It just wiped out the human race! As machine learning and artificial intelligence are woven more and more into the fabric of our daily lives, we are increasingly seeing that decisions based purely on code require a lot of care to ensure that the code truly behaves as we would like it to. As one high profile example after another demonstrates, this is a tricky challenge. On this episode, Finn Lattimore from Gradient Institute joined the gang to discuss the different dimensions of the challenge! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

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

Remember that time you ran a lunch-and-learn at your company to show a handful of co-workers some Excel tips? What would have happened if you actually needed to fully train them on Excel, and there were approximately a gazillion users*? Or, have you ever watched a Google Analytics or Google Tag Manager training video? Or perused their documentation? How does Google actually think about educating a massive and diverse set of users on their platform? And, what can we learn from that when it comes to educating our in-house users on tool, processes, and concepts? In this episode, Justin Cutroni from Google joined the gang to discuss this very topic! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page.

A simple recipe for a delicious analytics platform: combine 3 cups of data schema with a pinch of JavaScript in a large pot of cloud storage. Bake in the deployment oven for a couple of months, and savory insights will emerge. Right? Why does this recipe have both 5-star and 1-star ratings?! On this episode, long-standing digital analytics maven June Dershewitz, Director of Analytics at Twitch, drops by the podcast's analytics kitchen to discuss the relative merits of building versus buying an analytics platform. Or, of course, doing something in between!

The episode was originally 3.5 hours long, but we edited out most of Michael's tangents into gaming geekdown, which brought the run-time down to a more normal length.

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