The Profit and Proof Podcast
Causality, Incrementality & Optimality: Deepdive into advertising Measurements with Professor Kenneth Wilbur
What does incrementality actually mean, and why does advertising measurement go wrong so often? In this episode of Humans of Measurement, Lifesight CPO Rajeev Nair sits down with Kenneth Wilbur , Professor of Marketing and Analytics at UC San Diego's Rady School of Management, to break down incrementality, causal inference, and experimentation, and the critical difference between measuring ad performance and making better business decisions. Kenneth defines incrementality as causal lift and explains why the term is increasingly misused. He makes the case that causality is a data and identification problem, not just a modeling problem, and that valid control groups are what separate real lift from correlation. Rajeev and Kenneth also get into what happens when platforms and agencies grade their own homework, and how incentives inside an organization can either shut down experimentation or build a culture of continuous learning. In this episode: - Kenneth's path from economics and communication to software engineering, marketing, and analytics - Why he built an open-access advertising measurement resource and keeps revising it with practitioner feedback - Incrementality as causal lift, and how the term gets misused - Causal measurement vs. correlational approaches like marketing mix modeling (MMM) - Why valid control groups and quasi-experiments are essential for measuring causal impact - The incentive problem when platforms and agencies grade their own homework - How organizational incentives shape experimentation culture - Why causality is a data and identification problem, not just a modeling problem - Moving beyond incrementality to optimality: profit, retention, customer value, and pricing - Admias, a simulator that teaches students to make real ad measurement and budget allocation decisions - Kenneth's current research and book recommendations Humans of Measurement is presented by Lifesight, the unified marketing measurement platform that brings together marketing mix modeling, incrementality testing, and causal attribution so brands can make decision-grade budget calls. Learn more: https://lifesight.io Follow Humans of Measurement for more conversations with the people shaping how marketing gets measured.
