Dynamic Pricing with Volume Discounts in Online Settings

TL;DR

This paper introduces PVD-B, an online learning algorithm for dynamic pricing with volume discounts in e-commerce, demonstrating significant revenue improvements through real-world testing and outperforming human pricing strategies.

Contribution

The paper presents a novel two-phase online learning algorithm for demand estimation and price optimization with volume discounts, tailored for limited data scenarios in e-commerce.

Findings

PVD-B achieved a 55% increase in turnover in real-world testing.

The algorithm outperformed human pricing specialists in a 4-month experiment.

The company adopted the algorithm for over 1,200 products since January 2022.

Abstract

According to the main international reports, more pervasive industrial and business-process automation, thanks to machine learning and advanced analytic tools, will unlock more than 14 trillion USD worldwide annually by 2030. In the specific case of pricing problems-which constitute the class of problems we investigate in this paper-, the estimated unlocked value will be about 0.5 trillion USD per year. In particular, this paper focuses on pricing in e-commerce when the objective function is profit maximization and only transaction data are available. This setting is one of the most common in real-world applications. Our work aims to find a pricing strategy that allows defining optimal prices at different volume thresholds to serve different classes of users. Furthermore, we face the major challenge, common in real-world settings, of dealing with limited data available. We design a two-phase online learning algorithm, namely PVD-B, capable of exploiting the data incrementally in an online fashion. The algorithm first estimates the demand curve and retrieves the optimal average price, and subsequently it offers discounts to differentiate the prices for each volume threshold. We ran a real-world 4-month-long A/B testing experiment in collaboration with an Italian e-commerce company, in which our algorithm PVD-B-corresponding to A configuration-has been compared with human pricing specialists-corresponding to B configuration. At the end of the experiment, our algorithm produced a total turnover of about 300 KEuros, outperforming the B configuration performance by about 55%. The Italian company we collaborated with decided to adopt our algorithm for more than 1,200 products since January 2022.