Generative Marketing Mix Modeling: A Causal Inference Framework Linking GEO and GEM to Business Impact
A research paper proposes Generative Marketing Mix Modeling (GMMM) to estimate causal effects of Generative Engine Optimization (GEO) and Generative Engine Marketing (GEM). The method combines repeated generated answers with question counts, shares of use across generative systems, and notice probabilities for GEO; 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 effects. Empirical performance is investigated using simulated answers to product recommendation in English and Japanese.
The paper introduces GMMM, a causal inference framework for measuring the business impact of generative AI marketing strategies. It addresses the lack of standard data on user exposure to brand mentions in generated answers by modeling GEO and GEM effects through repeated generated answers, question counts, usage shares, notice probabilities, and sponsored placement records. The framework compares expected business responses under different treatment sequences and provides identification conditions. Simulation studies in English and Japanese validate the approach.
GMMM extends marketing mix modeling to generative AI channels by explicitly modeling notice probabilities and usage shares across generative systems. It establishes sufficient conditions for causal identification under alternative treatment sequences, suggesting a potential-moutcomes or structural causal model approach. The use of simulated product recommendation data in two languages indicates robustness testing across linguistic contexts.
As generative AI becomes a primary customer acquisition channel, firms lack standardized metrics for brand visibility in AI-generated answers. This framework could enable marketers to allocate budgets between organic optimization (GEO) and paid placements (GEM) with causal evidence, similar to traditional marketing mix models but adapted for generative engines.
Provides a methodology for quantifying ROI of generative AI marketing investments, enabling data-driven budget allocation between GEO and GEM. Could reduce uncertainty in emerging AI marketing channels and support strategic decisions for brands seeking visibility in AI-generated responses.
Next observable signals include empirical applications with real-world generative engine data, development of measurement tools for notice probabilities, and adoption by marketing analytics platforms. Further research may extend the framework to multi-touch attribution and dynamic treatment regimes.