Generative AI methods measure marketing impact on customer choice
Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
Artificial IntelligenceMachine Learning
Summary
Marketing today uses AI that creates answers for people, but it’s hard to know how often customers actually see a company's name in those answers. The authors created a new method called Generative Marketing Mix Modeling to figure out how much two specific AI marketing approaches influence business results. One approach looks at how often AI-generated answers mention a product, and the other checks sponsored placements in those answers. They tested their method on made-up product recommendations in English and Japanese to see how well it works. This helps businesses better understand how AI-driven advertising affects what customers buy.
Generative artificial intelligenceMarketing mix modelingCausal inferenceGenerative Engine OptimizationGenerative Engine MarketingSponsored placementsNotice probabilitySimulated answersBusiness impactProduct recommendation
Authors
Masahiro Kato, Daiki Honma, Taka Kato
Abstract
Generative artificial intelligence changes how firms reach customers, but standard marketing data do not record how often users see and notice a firm's name in generated answers. We develop Generative Marketing Mix Modeling (GMMM) to estimate the causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). For GEO, GMMM combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities. For GEM, it combines records of sponsored placements with notice probabilities. GMMM compares expected business responses under alternative treatment sequences and establishes sufficient conditions for identifying the resulting effects. We investigate the empirical performance of the proposed method using simulated answers to product recommendation in English and Japanese.