Markdown Pricing For a Large Scale Retailer

Abstract

A large European-based international discount store chain retailer, operating in more than 1000 stores across countries, wanted to make a transition from traditional cost-based markdown pricing to an optimal markdown pricing that maximizes the overall revenue as well as clears the inventory. Their regular pricing strategy was to price lowest among all their competitors since they are a discount store chain. Their markdown pricing problem is both critical and complex because there are many requirements to be satisfied, albeit simultaneously. The overall revenue had to be maximized as well as the given percentage of inventory was to be cleared by the end of the markdown period. Also, inventory had to remain in the stores till the end of the season so that the footfalls do not decrease and the markdown pricing was to be at the item group level for interrelated items. We developed a novel markdown solution by utilizing the retailers’ historical markdown performance data to come up with the markdown price estimates. Our approach was to obtain initial markdown price estimates for each item, apply ML/DL algorithms to forecast sales, compute price elasticities and build a nonlinear markdown price optimization system to recommend optimal prices. In this paper, we give the details and results for one category - the Clothing category. We did back testing for multiple periods to compare our sales forecasting model outputs using initial price, with their actuals. We have employed a distributed computing and parallel execution framework in cloud to obtain optimal markdown prices for products in the clearance season. Our final recommended price was greater by around 20% than the actual markdown prices for most items and this led to around 6% decrease in sales units while satisfying their inventory constraints. Our optimal pricing solution resulted in 10% increase in revenue for Clothing as compared to their actuals. Our markdown solution was later scaled to other categories as well as to stores in other countries.