Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost

TL;DR

This paper introduces a cascade multinomial logit model for assortment optimization that accounts for repeated product exposures and consumer patience costs, providing a new approach for online retail scenarios.

Contribution

It develops a novel cascade multinomial logit model incorporating repeated exposures and product-dependent patience costs, with an approximation solution for optimization.

Findings

Model captures repeated exposures and patience costs effectively.

Proposed approximation improves optimization in complex consumer behavior scenarios.

Applicable to online retail platforms for better assortment strategies.

Abstract

In this paper, we study the assortment optimization problem faced by many online retailers such as Amazon. We develop a \emph{cascade multinomial logit model}, based on the classic multinomial logit model, to capture the consumers' purchasing behavior across multiple stages. Different from existing studies, our model allows for repeated exposures of a product, i.e., the same product can be displayed multiple times across different stages. In addition, each consumer has a \emph{patience budget} that is sampled from a known distribution and each product is associated with a \emph{patience cost}, which captures the cognitive efforts spent on browsing that product. Given an assortment of products, a consumer sequentially browses them stage by stage. After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform. Otherwise, if the patience cost of all products browsed up to that point is no larger than her patience budget, she continues to view the next stage. We propose an approximation solution to this problem.