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

209: Maria Solodilova: Why Adtech is really a marketplace with its own economics

Maria takes us on a guided tour across the adtech landscape from a bird’s-eye view, describing a real-time marketplace where mobile ad mediation converts app usage into revenue through auctions that price every impression. She explains how supply-side work at Yango Ads centers on SDK integration, auction behavior, and performance tradeoffs that directly shape earnings once systems operate in production.More

203: Jordan Resnick: How to distinguish fake traffic from real machine customers

Distinguishing fake traffic from real machine customers requires reading behavior. Jordan shows how AI-driven bots now scroll, click, and submit forms while inflating dashboards with activity that never converts. The signal lives in speed, sequencing, and follow-through. Teams that act protect the conversion point, block synthetic demand early, and report only after traffic earns trust.More

178: Guta Tolmasquim: Connecting brand to revenue with attribution algorithms that reflect brand complexity

Brand measurement often feels like a polite performance nobody fully believes, and Guta learned this firsthand moving from performance marketing spreadsheets to startup rebrands that showed clear sales bumps everyone could feel. When she built Purple Metrics, she refused to pretend algorithms could explain everything, designing tools that encourage gradual shifts over sudden upheaval.More

158: Jeff Lee: How Calm’s Billion customer message machine unites martech and engineering

Jeff built a billion-message marketing machine at Calm with three people. But it was a journey. Push notifications sparked a three-year battle until a new CPO, unburdened by notification trauma, green-lit the project in six weeks. It was a four-year battle for ML recommendations but the data tells an unexpected story. Jeff also shares tactics for winning over the most skeptical product and engineering teams. It starts with operational empathy by taking a stab at a working prototype of your idea but also recognizing that product and martech decisions often stem from personal bias that requires champions who have experienced positive outcomes.More

128: Vish Gupta: Why simplification should come before automation if you want to avoid a Frankenstack

We touch on the pitfalls of Frankenstein stacks and the perks of self-service martech. Vish explains why martech isn’t just for engineers and highlights the efficiency of customized Asana intake forms. We also tackle the dangers of over-specialization for senior leaders. Additionally, we explore the intersection of martech and large language models (LLMs), providing insights on how to stay ahead in the evolving landscape.More

116: Kevin Hu: How data observability and anomaly detection can enhance MOps

Dr. Kevin Hu gives us a masterclass on everything data. Data analysis, data storytelling, data quality, data observability and data anomaly detection. We unpack the importance of balancing data perfection with actually doing the work of activating that data. He highlights data observability and anomaly detection as a key to preempting errors, ensuring data integrity for a seamless user experience. More

103: Britney Muller: Deciphering the alien nature and the ethical complexities of LLMs

Britney offers a comprehensive view of the intersection of marketing and LLMs, blending technical know-how with ethical mindfulness and human-centric approaches. Her perspectives encourage professionals in the field to not only embrace AI for its efficiencies but to also understand and address its complexities, ensuring its development and application are both responsible and inclusive.More