Product Sequencing and Pricing under Cascade Browse Model

TL;DR

This paper introduces a cascade browse model for online consumer behavior, proposing approximation algorithms for joint product sequencing and pricing to maximize expected revenue under constraints.

Contribution

It develops a novel cascade browse model, provides the first FPTAS for assortment optimization with capacity and cardinality constraints, and offers approximate solutions for joint sequencing and pricing.

Findings

Proposed a cascade browse model capturing sequential consumer browsing behavior.

Developed a constant approximation algorithm for product sequencing to maximize revenue.

Created the first FPTAS for assortment optimization with capacity and cardinality constraints.

Abstract

In this paper, we study the joint product sequencing and pricing problem faced by many online retailers such as Amazon. We assume that consumers' purchase behavior can be explained by a ``consider-then-choose'' model: they first form a consideration set by screening a subset of products sequentially, and then decide which product to purchase from their consideration set. We propose a \emph{cascade browse model} to capture the consumers' browsing behavior, and use the Multinomial Logit (MNL) model as our choice model. We study two problems in this paper: in the first problem, we assume that each product has a fixed revenue and preference weight, the goal is to identify the best sequencing of products to offer so as to maximize the expected revenue subject to a cardinality constraint. We propose a constant approximate solution to this problem. As a byproduct, we propose the first fully polynomial-time approximation scheme (FPTAS) for the classic assortment optimization problem subject to one capacity constraint and one cardinality constraint. In the second problem, we treat the price of each product as a decision variable and our objective is to jointly decide a sequence of product and their prices to maximize the expected revenue. We propose a constant approximate solution to this problem.