Dynamic pricing with Bayesian updates from online reviews

TL;DR

This paper develops a Bayesian dynamic pricing model leveraging online reviews, enabling firms to update beliefs about product quality and optimize pricing strategies, with a novel connection to Catalan numbers for efficient computation.

Contribution

It introduces a Bayesian updating framework for pricing with online reviews and links it to Catalan numbers, providing a new method for optimal pricing strategy analysis.

Findings

Dynamic pricing strategies improve learning about product quality.

Bayesian updates enhance decision-making in uncertain markets.

Efficient computation of future rewards using Catalan numbers.

Abstract

When launching new products, firms face uncertainty about market reception. Online reviews provide valuable information not only to consumers but also to firms, allowing firms to adjust the product characteristics, including its selling price. In this paper, we consider a pricing model with online reviews in which the quality of the product is uncertain, and both the seller and the buyers Bayesianly update their beliefs to make purchasing & pricing decisions. We model the seller's pricing problem as a basic bandits' problem and show a close connection with the celebrated Catalan numbers, allowing us to efficiently compute the overall future discounted reward of the seller. With this tool, we analyze and compare the optimal static and dynamic pricing strategies in terms of the probability of effectively learning the quality of the product.