Category Archives: customer data

236: What AI replaces in data work and when not to reach for it with Julie Beynon

Julie came into data sideways from content marketing and turned that into a 15-year career running lean data teams at some of the coolest martech startups in the landscape. In this episode she breaks down how they cloned their best analyst into an AI agent named JimBot, why she pushes back on AI projects without ever saying no, and how she gets companies to fund the invisible foundation work nobody claps for.More

235: Why consent banners, tags and privacy policies never match, with Stéphane Hamel

Stéphane Hamel built the first web analytics QA tool back in 2006, watched an ad blocker quietly borrow his logic, and spent the next 20 years learning that tracking got harder to see every year while the industry got better at documenting it. Now he’s building MANTIS, a privacy observability platform that watches what a website actually does instead of what its policy claims.More

227: The Correlation masquerade (The Dungeon of martech architecture, part 3)

Your warehouse is clean, your agents are running, everything looks like it’s working, and that’s the trap. This floor is where AI mistakes correlation for causation and scales the mistake at machine speed, eroding revenue behind a green dashboard. We unpack the fix: holdouts, guardrails, and a causal context graph that proves what actually works.More

226: The Eye of context (The Dungeon of martech architecture, part 2)

Agents operating on data without anything to help them causes “believable nonsense.” Data quality stops agents from misrepresenting what the warehouse contains but you need context engineering to put the right meaning, rules, and situational information in front of the model at the right moment.More

225: The Fall of CRM gravity (The Dungeon of martech architecture, part 1)

At some point in the last decade, the CRM became a shared apartment with 19 roommates, each adding their own version of the source of truth. This episode argues for a cleaner path: raw data into the warehouse, transformation with tools like dbt, then activation through reverse ETL so definitions stay centralized and audiences stay portable. More

219: Elizabeth Dobbs: Inside Databricks’ stack with 3 AI agents, 1 lakehouse, and 6 years of data work

Liz spent 6 years at Databricks building the data infrastructure before deploying any AI on top of it. She’s shipped 3 production agents (Marge, Tagatha, and Atlas) and she’ll tell you exactly what broke first and why the team kept going anyway. You’ll hear how a marketing lakehouse becomes the foundation that makes every agent actually work, why the agent label debate is a distraction.More

213: John Whalen: The next marketing advantage is pre-testing ideas on synthetic users

John has spent his career studying how people actually think, and his conclusion is uncomfortable for anyone who believes their marketing decisions are more rational than they are. In this episode, John explores how synthetic users built from cognitive science principles can fill the massive research gap that most teams quietly ignore, and why removing the human interviewer from the room might be the fastest way to finally hear the truth.More

212: Tobias Konitzer: The Causal AI revolution and the boomerang effect in marketing decision science

Tobi challenged marketing’s fixation on prediction. He has built highly accurate LTV models, but accuracy alone does not move revenue. Marketing is intervention. Correlation shows patterns; causality tells you what happens when you pull a lever. That shift reshapes experimentation, explains why dynamic allocation can outperform static A B tests, and highlights how self learning systems can backfire or get stuck in local maxima.More