Bayesian Nonparametric Sequential Search

Abstract

Sequential search models are popular in marketing for studying consumer search behavior. Current search models use parametric assumptions regarding different aspects of the models, such as random shocks, search costs, and consumer preferences. These assumptions can be restrictive. The authors develop a novel Bayesian nonparametric framework for sequential search to flexibly model unknown distributions. They also develop Markov chain Monte Carlo methods for inferring the model unknowns. The authors conduct simulation studies to demonstrate that currently popular parametric search models can yield incorrect estimates and inferences when the data-generating process deviates from their assumptions. In contrast, the proposed model accurately recovers these quantities for various data-generating processes. The methodology is then applied to online search and purchase data from a Japanese retailer. The results show that several heterogeneity distributions have complex patterns, such as multimodality and skewness, that are not captured by a parametric benchmark model. The authors also estimate the monetary value of search costs, the price elasticities, and a counterfactual profit gain under a personalized couponing strategy and find substantial differences in the results from the two models.