Tag Archives: incrementality

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

200: Matthew Castino: How Canva measures marketing

Matt leads the measurement function at Canva, he reshaped measurement so centralized models stayed steady while embedded data scientists guided decisions locally, and he built one forecasting engine that finance and marketing can trust together.More

182: Simon Lejeune: Wealthsimple’s VP of Growth on 2 keys to be a top 5% marketer

Simon Lejeune learned early that chasing small wins keeps growth teams stuck, a lesson that landed hard when Hopper’s CEO dismissed his price‑point test as a “local maximum” and pushed him toward ideas bold enough to reshape the business. That experience drives how he leads at Wealthsimple, where he tells teams to stop polishing the same hill and start climbing new mountains.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

176: Rajeev Nair: Causal AI and a unified measurement framework

Rajeev believes measurement only works when it’s unified or multi-modal, a stack that blends multi-touch attribution, incrementality, media mix modeling and causal AI, each used for the decision it fits. At Lifesight, that means using causal machine learning to surface hidden experiments in messy historical data and designing geo tests that reveal what actually drives lift. Attribution alone can’t tell you what changed outcomes.More

165: Ashley Faus: Building content that matches actual human thinking by integrating lifecycle, content and product marketing

Marketing frameworks often fail because they ignore how humans actually behave. People don’t follow neat, linear paths; they explore, double back, and leap ahead based on genuine interests. Drawing from her diverse experience across corporate communications and lifecycle leadership, Ashley exposes how artificial walls between marketing functions create dysfunction while offering a solution. More

153: Sundar Swaminathan: How Uber measures the ROI of marketing according to their former Growth Marketing Data Science Lead

After leading Uber’s Marketing Data Science teams, Sundar shares insights that work for both tech giants and startups. Beyond uncovering that Meta ads generated zero incremental value (saving $30 million annually), they mastered measuring brand impact through geo testing and predicting LTV through first-week behaviors. Small companies can adapt these methods through strategic A/B testing and simplified attribution models, even with limited sample sizes. More