Category Archives: customer data

239: Personalization is a creativity problem not a data problem, with Christina Garnett

Christina spent 5 years teaching math before she became a customer marketing expert, and it shows in how ruthlessly she takes apart the personalization playbook. She makes the case for core memories a brand can’t buy, walks through what actually happened when Spotify Wrapped got the AI treatment, and introduces earned context, the idea that having someone’s data and having the right to use it are 2 completely different things. More

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