
The Analytics Power Hour
by Michael Helbling, Moe Kiss, Tim Wilson, Val Kroll, and Julie Hoyer
Is this your podcast?Insights from recent episode analysis
Audience Interest
Podcast Focus
Publishing Consistency
Platform Reach
Insights are generated by CastFox AI using publicly available data, episode content, and proprietary models.
Most discussed topics
Brands & references
Total monthly reach
Estimated from 30 chart positions in 30 markets.
By chart position
- 🇬🇧GB · Marketing#43100K to 300K
- 🇺🇸US · Marketing#8830K to 100K
- 🇩🇪DE · Marketing#1195K to 30K
- 🇦🇺AU · Marketing#1285K to 30K
- 🇳🇱NL · Marketing#6210K to 30K
- Per-Episode Audience
Est. listeners per new episode within ~30 days
174K to 569K🎙 ~2x weekly·306 episodes·Last published 2d ago - Monthly Reach
Unique listeners across all episodes (30 days)
349K to 1.1M🇬🇧26%🇻🇳26%🇺🇸9%+27 more - Active Followers
Loyal subscribers who consistently listen
105K to 341K
Market Insights
Platform Distribution
Reach across major podcast platforms, updated hourly
Total Followers
—
Total Plays
—
Total Reviews
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* Data sourced directly from platform APIs and aggregated hourly across all major podcast directories.
On the show
From 16 epsHosts
Recent guests
Recent episodes
#305: Personal Interest + Analytics Chops = Career?
Sep 1, 2026
Unknown duration
#304: I Can Haz AI?
Aug 18, 2026
Unknown duration
#303: Funnels Assume Progress. Barriers Recognize Reality.
Aug 4, 2026
Unknown duration
#302: It Was a Dark and Stormy Insight...
Jul 21, 2026
1h 08m 09s
#301: It Turns Out Analysts Are Natural AI Crafters
Jul 7, 2026
1h 16m 34s
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| Date | Episode | Topics | Guests | Brands | Places | Keywords | Sponsor | Length | |
|---|---|---|---|---|---|---|---|---|---|
| 9/1/26 | #305: Personal Interest + Analytics Chops = Career? | Stephen Follows has spent 15 years turning film-industry curiosity into a career — asking questions like how much movies really earn, why poster colors have shifted over decades, and whether old studio domains are still up for grabs. In this episode, he joined us to talk about building that unusual path, the cautionary tale of TheNumbers.com, and his theory about Sandra Bullock. Go start your passion project. This episode is brought to you, in part, by our sponsors, Prism from Ask-Y and Stape. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 8/18/26 | #304: I Can Haz AI? | Everyone's posting their AI projects online like proud pet owners sharing videos of their cat doing something marginally impressive — cute, occasionally clever, sometimes a little cringe. And the Analytics Power Hour is no different! In this co-hosts-only episode, Tim, Michael, and Julie skip the thought leadership hot takes and just… compare notes. What have they actually built? What broke? What surprised them? From a custom GPT podcast librarian to a full-blown show production app wired up to Neon, Vercel, Resend, and about five other things Michael is only sort of sure he set up correctly, to a Gemini Gem that simulates a client interaction so realistically it raises your blood pressure in a safe environment — there's a lot of ground covered. Plus: why AI-generated communication has a Stevia aftertaste, why deploying AI context across a team is way harder than it looks, and why the LLM will absolutely tell you what you want to hear about your Meta spend if you give it half a chance. This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 8/4/26 | #303: Funnels Assume Progress. Barriers Recognize Reality. | Twenty years of digital marketing created something of a monster: companies poured enormous investment into analytics teams, MarTech stacks, media capabilities, and data infrastructure—and then watched all those functions march off into their respective silos to work really, really hard at producing activity rather than impact. Rusty Rahmer, founder of Starize AI and author of Working As Designed, joined Michael, Julie, and Val to dig into why that happened, why it's still happening, and what it actually takes to flip the shovel over and use the right end. Along the way, Rusty—who Val correctly identified early as a spontaneous analogy machine—explained why customer journey maps on walls are basically the statistical average American life that literally nobody lives, why waffle fries are structurally superior to ridged chips (and what that has to do with cross-functional team design), and why the most important question a marketing leader can ask a room full of executives is also the one most likely to be met with complete silence. This episode is brought to you, in part, by our sponsors, Stape and Prism from Ask-Y. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 7/21/26 | data analysisstorytelling+4 | Aleya Harris | — | — | data storytellingbusiness insights+3 | Stape | 1h 08m 09s | ||
| 7/7/26 | AI customizationbusiness technology+4 | Rob Collie | MicrosoftP3 Adaptive+1 | — | AICrafters+5 | Stape | 1h 16m 34s | ||
| 6/23/26 | semantic layersdata modeling+3 | Jacob Matson | MotherDuck | — | semantic layerdata jungle+3 | Stape | 58m 10s | ||
| 6/9/26 | analyticschange management+4 | Yehonatan Schwarzmer | — | — | analytics platformBI tool migration+5 | Stape | 1h 00m 46s | ||
| 5/26/26 | AIstakeholder management+3 | — | Marketing Analytics Summit | — | analyticsAI+4 | — | 52m 18s | ||
| 5/12/26 | analyticsmental models+4 | — | AI Slop | — | analyticsmental models+6 | — | 1h 06m 03s | ||
| 4/28/26 | precision vs accuracydata trust+3 | Arik Friedman | Atlassian | — | data accuracydata precision+3 | — | 1h 04m 05s | ||
Want analysis for the episodes below?Free for Pro Submit a request, we'll have your selected episodes analyzed within an hour. Free, at no cost to you, for Pro users. | |||||||||
| 4/14/26 | researchanalytics+5 | Stefanie Zammit | Bang & Olufsen | — | researchanalytics+7 | — | 1h 09m 29s | ||
| 3/31/26 | AI adoptionanalytics teams+3 | John Lovett | SEER Interactive | — | AIanalytics+5 | — | 1h 08m 00s | ||
| 3/17/26 | tool selectionfeature comparisons+3 | — | Google Analytics Alternatives | — | tool selectionfeature comparisons+3 | — | 1h 05m 33s | ||
| 3/3/26 | AI adoptionproductivity+4 | Aubrey Blanche | — | — | AIproductivity+4 | — | 1h 04m 17s | ||
| 2/17/26 | data workdata practitioners+4 | — | — | — | data analysisSQL+5 | — | 1h 02m 38s | ||
| 2/3/26 | organizational learningexperimentation+3 | Mårten Schultzberg | Spotify | — | learningexperimentation+4 | — | 1h 06m 17s | ||
| 1/20/26 | business acumendata analysis+3 | — | — | — | databusiness acumen+5 | Recast | 1h 10m 15s | ||
| 1/6/26 | AIacronyms+3 | Sam Redfern | — | — | MCPmodel context protocol+6 | Recast | 1h 00m 39s | ||
| 12/23/25 | year in reviewindustry reflections+3 | — | 2025 Year in Review | — | year in reviewshow highlights+3 | Recast | 1h 00m 49s | ||
| 12/9/25 | #286: Metrics Layers. Data Dictionaries. Maybe It's All Semantic (Layers)? With Cindi Howson | Semantic layers are having something of a moment, but they're not actually new as a concept. Ever since the first database table was designed with cryptic field names that no business user could possibly understand, there's been a need for some form of mapping and translation. Should every company be considering employing a semantic layer? Is the idea of a single, comprehensive semantic layer within an organization a monolithic concept that is doomed to fail? These questions and more get bandied about on this episode, where we were joined by industry legend Cindi Howson, Chief Data & AI Strategy Officer at Thoughtspot. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. This episode's Measurement Bite from show sponsor Recast is an explanation of multicollinearity from Michael Kaminsky! | — | ||||||
| 11/25/25 | #285: Our Prior Is That Many Analysts Are Confounded by Bayesian Statistics | Before you listen to this episode, can you quantify how useful you expect it to be? That's a prior! And "priors" is a word that gets used a lot in this discussion with Michael Kaminsky as we try to demystify the world of Bayesian statistics. Luckily, you can just listen to the episode once and then update your expectation—no need to simulate listening to the show a few thousand times or crunch any numbers whatsoever. The most important takeaway is that you'll know you've achieved Bayesian clarity when you come to realize that human beings are naturally Bayesian, and the underlying principles behind Bayesian statistics are inherently intuitive. This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are generally more practically useful in business than p-values) from Michael Kaminsky! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 11/11/25 | #284: I Used to Think...But Not Any More | As the world turns, a couple of things happen: 1) we grow and learn, and 2) the world changes. On this episode, inspired by a job interview question, the hosts walked through a range of thoughts and beliefs they had at one time that they no longer have today. Analytics intake forms are good…or bad? Analytics centers of excellence are the sign of a mature organization…or they're just one of many potential options? Privacy concerns are something no one really cares about…or they are something everyone cares deeply about? Voices were raised. Light profanity was employed. Laughter ensued. This episode's Measurement Bite from show sponsor Recast is a brief explanation of statistical significance (and why shorthanding it is problematic…and why confidence intervals are often more practically useful in business than p-values) from Michael Kaminsky. For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 10/28/25 | #283: Good Things (Can) Come in Small Datasets with Joe Domaleski | Does size matter? When it comes to datasets, the conventional wisdom seems to be a resounding, "Yes!" But what about small datasets? Small- and mid-sized businesses and nonprofits, especially, often have limited web traffic, small email lists, CRM systems that can comfortably operate under the free tier, and lead and order counts that don't lend themselves to "big data" descriptors. Even large enterprises have scenarios where some datasets easily fit into Google Sheets with limited scrolling required. Should this data be dismissed out of hand, or should it be treated as what it is: potentially useful? Joe Domaleski from Country Fried Creative works with a lot of businesses that are operating in the small data world, and he was so intrigued by the potential of putting data to use on behalf of his clients that he's mid-way through getting a Master's degree in Analytics from Georgia Tech! He wrote a really useful article about the ins and outs of small data, so we brought him on for a discussion on the topic! This episode's Measurement Bite from show sponsor Recast is an explanation of synthetic controls and how they can be used as counterfactuals from Michael Kaminsky! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 10/14/25 | #282: Using (and Creating!) Data to Understand Pop Culture with Chris Dalla Riva | Data does not just magically spring into existence. Someone, somewhere, has to decide what data gets created and the rules for its creation. We would claim that this often starts as a pretty simple exercise, and then, over time, that simplicity balloons to be pretty complex! What if, for instance, you decided to listen to every #1 song on the Billboard Hot 100 going back to its inception in 1958? You may start by just capturing the song name, the artist, and the week(s) it was the #1 song. But, before you know it, you may find that you're adding in artist details…and songwriter details…and producer details…and genre details…and instrumentation details, and your dataset has 105 columns! But, oh, the questions that dataset could answer! And that's exactly the dataset that our guest for this episode, Chris Dalla Riva, created. He uses it (with a range of supplemental datasets) for his pieces in his Substack, Can't Get Much Higher, as well as the underlying raw material for his upcoming book, Uncharted Territory: What Numbers Tell Us about the Biggest Hit Songs and Ourselves. While the underlying material was music, the parallels to more staid business data were many when it comes to the underlying processes and challenges for doing that work! This episode's Measurement Bite from show sponsor Recast is an explanation of the miracle of randomization when it comes to addressing unobserved confounders from Michael Kaminsky! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
| 9/30/25 | #281: Analytics: The View from the Corner Office with Anna Lee | From spreadsheets to strategy: what does data look like from the CEO's chair? For this episode, we sat down with Anna Lee, CEO of Flybuys and former CFO/COO of THE ICONIC, to get her view on data-led leadership and what great looks like in data and analytics. Discover how Anna's journey from finance to the corner office has shaped her approach to leveraging evidence for strategic decision-making. From productive curiosity, to informed pragmatism, and how data teams can build trust with leadership, this is a candid conversation about analytics from the top down. Whether you're embedded in a squad or building the next big data platform, this one's for anyone who's ever wondered what it takes to truly influence the C-suite! This episode's Measurement Bite from show sponsor Recast is an overview of the fundamental problem of causal inference from Michael Kaminsky! For complete show notes, including links to items mentioned in this episode and a transcript of the show, visit the show page. | — | ||||||
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Chart history for The Analytics Power Hour
Peaked at #4 in VN, top 10 in 2 of 31 tracked markets, currently #4 in VN.
| Market | Genre | Peak | Current | Trend |
|---|---|---|---|---|
| VN | — | #4 | #4 | — |
| Finland | — | #10 | #10 | — |
| NG | — | #21 | #21 | — |
| KE | — | #37 | #37 | — |
| United Kingdom | — | #43 | #43 | — |
| Netherlands | — | #62 | #62 | — |
| Brazil | — | #68 | #68 | — |
| CH | — | #78 | #78 | — |
| GR | — | #80 | #80 | — |
| IS | — | #84 | #84 | — |
| South Africa | — | #85 | #85 | — |
| United States | — | #88 | #88 | — |
| MY | — | #90 | #90 | — |
| Mexico | — | #92 | #92 | — |
| Ireland | — | #99 | #99 | — |
| PL | — | #99 | #99 | — |
| CL | — | #107 | #107 | — |
| Denmark | — | #110 | #110 | — |
| SA | — | #110 | #110 | — |
| India | — | #110 | #110 | — |
Chart Positions
31 placements across 30 markets.
Chart Positions
31 placements across 30 markets.