Optimal pricing using online auction experiments: A P\'olya tree approach

TL;DR

This paper introduces a Bayesian Pólya tree method for retailers to estimate optimal product prices from online auction data, offering flexible, assumption-free valuation inference that improves pricing accuracy.

Contribution

It develops a novel Pólya tree Bayesian approach tailored for limited auction data, enabling robust, non-parametric valuation distribution estimation for optimal pricing.

Findings

Method successfully estimates optimal prices in real-world jewelry auctions.

Flexible inference improves pricing robustness over parametric models.

Collaborative case study demonstrates practical application and benefits.

Abstract

We show how a retailer can estimate the optimal price of a new product using observed transaction prices from online second-price auction experiments. For this purpose we propose a Bayesian P\'olya tree approach which, given the limited nature of the data, requires a specially tailored implementation. Avoiding the need for a priori parametric assumptions, the P\'olya tree approach allows for flexible inference of the valuation distribution, leading to more robust estimation of optimal price than competing parametric approaches. In collaboration with an online jewelry retailer, we illustrate how our methodology can be combined with managerial prior knowledge to estimate the profit maximizing price of a new jewelry product.