Online Dynamic Pricing of Complementary Products

TL;DR

This paper develops an online learning algorithm for dynamic pricing of complementary products, leveraging demand interactions to improve revenue over traditional independent pricing methods.

Contribution

It introduces a novel algorithm that models demand interdependencies among products and optimizes prices using multi-armed bandit techniques with Gaussian processes.

Findings

Improves revenue compared to algorithms ignoring product interactions.

Effectively identifies complementary relationships through transaction data.

Demonstrates success in simulated environments.

Abstract

Traditional pricing paradigms, once dominated by static models and rule-based heuristics, are increasingly being replaced by dynamic, data-driven approaches powered by machine learning algorithms. Despite their growing sophistication, most dynamic pricing algorithms focus on optimizing the price of each product independently, disregarding potential interactions among items. By neglecting these interdependencies in consumer demand across related goods, sellers may fail to capture the full potential of coordinated pricing strategies. In this paper, we address this problem by exploring dynamic pricing mechanisms designed explicitly for complementary products, aiming to exploit their joint demand structure to maximize overall revenue. We present an online learning algorithm considering both positive and negative interactions between products' demands. The algorithm utilizes transaction data to identify advantageous complementary relationships through an integer programming problem between different items, and then optimizes pricing strategies using data-driven and computationally efficient multi-armed bandit solutions based on heteroscedastic Gaussian processes. We validate our solution in a simulated environment, and we demonstrate that our solution improves the revenue w.r.t. a comparable learning algorithm ignoring such interactions.