Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization

The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequential personalized interventions that maximize the adoption probability for a new product. We model consumer behavior by...

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Bibliographic Details
Published in:Production and operations management Vol. 33; no. 3; pp. 775 - 794
Main Authors: Iyengar, Garud, Singal, Raghav
Format: Journal Article
Language:English
Published: Los Angeles, CA SAGE Publications 01.03.2024
Subjects:
attribution
Markov decision process
interpretability
scalability
Sequential marketing
ISSN:1059-1478, 1937-5956
Online Access:Get full text
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Summary:The flexibility of choosing the ad action as a function of the consumer state is critical for modern-day marketing campaigns. We study the problem of identifying the optimal sequential personalized interventions that maximize the adoption probability for a new product. We model consumer behavior by a conversion funnel that captures the state of each consumer (e.g., interaction history with the firm) and allows the consumer behavior to vary as a function of both her state and firm’s sequential interventions. We show our model captures consumer behavior with very high accuracy (out-of-sample area under the curve of over 0.95) in a real-world email marketing dataset. However, it results in a very large-scale learning problem, where the firm must learn the state-specific effects of various interventions from consumer interactions. We propose a novel attribution-based decision-making algorithm for this problem that we call model-free approximate Bayesian learning. Our algorithm inherits the interpretability and scalability of Thompson sampling for bandits and maintains an approximate belief over the value of each state-specific intervention. The belief is updated as the algorithm interacts with the consumers. Despite being an approximation to the Bayes update, we prove the asymptotic optimality of our algorithm and analyze its convergence rate. We show that our algorithm significantly outperforms traditional approaches on extensive simulations calibrated to a real-world email marketing dataset.
ISSN:1059-1478
1937-5956
DOI:10.1177/10591478241231857