Price Optimization in Fashion E-commerce

TL;DR

This paper presents a machine learning and optimization framework for setting optimal product prices in fashion e-commerce, improving revenue and margin through demand prediction and linear programming, validated by live AB tests.

Contribution

It introduces a novel integrated approach combining demand forecasting, price elasticity, and linear programming for real-time price optimization in fashion e-commerce.

Findings

Revenue increased by 1% in live tests

Gross margin improved by 0.81%

Method effective in large-scale product environments

Abstract

With the rapid growth in the fashion e-commerce industry, it is becoming extremely challenging for the E-tailers to set an optimal price point for all the products on the platform. By establishing an optimal price point, they can maximize overall revenue and profit for the platform. In this paper, we propose a novel machine learning and optimization technique to find the optimal price point at an individual product level. It comprises three major components. Firstly, we use a demand prediction model to predict the next day demand for each product at a certain discount percentage. Next step, we use the concept of price elasticity of demand to get the multiple demand values by varying the discount percentage. Thus we obtain multiple price demand pairs for each product and we have to choose one of them for the live platform. Typically fashion e-commerce has millions of products, so there can be many permutations. Each permutation will assign a unique price point for all the products, which will sum up to a unique revenue number. To choose the best permutation which gives maximum revenue, a linear programming optimization technique is used. We have deployed the above methods in the live production environment and conducted several AB tests. According to the AB test result, our model is improving the revenue by 1 percent and gross margin by 0.81 percent.