Learning from Many Experiments: A Hierarchical Bayesian Framework for Decomposing Marketing Treatment Heterogeneity – MSI

Working Paper

Learning from Many Experiments: A Hierarchical Bayesian Framework for Decomposing Marketing Treatment Heterogeneity

Peter Ebbes

HEC Paris

Eva Ascarza

Harvard University

Oded Netzer

Columbia University

Mar 5, 2026

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Develops and tests a Bayesian model integrating multiple marketing interventions over time that combines the causal-inference advantages of randomized experimentation with the longitudinal richness of repeated observational data to support improved targeting.

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research recap

Combine and Conquer: Integrating Experiments for Better Targeted Marketing

A Bayesian model integrating multiple marketing interventions over time combines the causal-inference advantages of randomized experimentation with the longitudinal richness of repeated observational data. Results reveal that marketing effectiveness depends much more on customer receptivity than campaign design or timing, pointing to the need for—and means of—better targeting.

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