{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Humans of Martech","title":"212: The Causal AI revolution and the boomerang effect in marketing decision science with Tobias Konitzer","html":"","width":"100%","height":180,"duration":3872,"description":"Summary: 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. It also fuels his skepticism of unleashing agentic AI on historical data without a causal layer. If you want to change outcomes instead of forecast them, your systems need to understand levers and log decisions you can actually audit.About TobiasTobias Konitzer, PhD is VP of AI at GrowthLoop, where he’s chasing closed-loop marketing powered by reinforcement learning, causality, and agentic systems. He’s spent the past decade focused on one core problem: moving beyond prediction to actually influencing outcomes.Previously, Tobi was Chief Innovation Officer at Fenix Commerce, helping major eCommerce brands modernize checkout and delivery with machine learning. He also founded Ocurate, a venture-backed startup that predicted customer lifetime value to optimize ad bidding in real time, raising $5.5M and scaling to $500K+ ARR before its acquisition. Earlier, he co-founded PredictWise, building psychographic and behavioral targeting models that drove over $2M in revenue.Tobi earned his PhD in Computational Social Science from Stanford and worked at Facebook Research on large-scale ML and bias correction. Originally from Germany and based in the Bay Area since 2013, he writes frequently about causal thinking, machine decisioning, and the future of marketing.Why Predictive Models Fail Without Causal InferencePrediction dominates most marketing roadmaps. Teams invest months refining churn models, tightening confidence intervals, and debating which threshold deserves a campaign. Tobi built an entire company on that logic. His team produced...","thumbnail_url":"https://img.transistorcdn.com/4eBcAi2MlxuVPdaWgcnwbeXVYH5naGhZe-qIuSYOdjU/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hZGE5/NTRiZjYwMDI0NWM5/MmE4MDQ1NWJlODA3/MjUxYy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}