Conjugating Variational Inference for Large Mixed Multinomial Logit Models and Consumer Choice

TL;DR

This paper introduces a scalable variational inference method for large mixed multinomial logit models, enabling efficient estimation of consumer choice heterogeneity in big datasets, with improved accuracy over existing methods.

Contribution

A novel variational inference approach that efficiently updates Gaussian approximations for large mixed logit models, improving scalability and estimation accuracy.

Findings

Consumer heterogeneity varies significantly across stores and products.

Store size, premium status, and geography influence price elasticities.

Extension to bundle choice models enhances predictive accuracy.

Abstract

Heterogeneity in multinomial choice data is often accounted for using logit models with random coefficients. Such models are called "mixed", but they can be difficult to estimate for large datasets. We review current Bayesian variational inference (VI) methods that can do so, and propose a new VI method that scales more effectively. The key innovation is a step that updates efficiently a Gaussian approximation to the conditional posterior of the random coefficients, addressing a bottleneck within the variational optimization. The approach is used to estimate three types of mixed logit models: standard, nested and bundle variants. We first demonstrate the improvement of our new approach over existing VI methods using simulations. Our method is then applied to a large scanner panel dataset of pasta choice. We find consumer response to price and promotion variables exhibits substantial heterogeneity at the grocery store and product levels. Store size, premium and geography are found to be drivers of store level estimates of price elasticities. Extension to bundle choice with pasta sauce improves model accuracy further. Predictions from the mixed models are more accurate than those from fixed coefficients equivalents, and our VI method provides insights in circumstances which other methods find challenging.