About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
INFORMS 2020
Talk
Data-driven stochastic markdown optimization for fashion retail
Abstract
An effective price markdown strategy is important for any retailer to profitably liquidate seasonal fashion products with finite life-time. While conventional markdown approaches are largely rule-based or parametric, we propose an approach with two novel components: i) A data-driven price elasticity model that estimates future sales as function of offered discounts and other product and merchandizing attributes. ii) A dynamic programming based optimizer that recommends an optimal discount policy to be followed in the entire planning horizon, that maximizes the expected revenue and also allows for a pre-specified markdown budget. The proposed approach was piloted with a leading fashion retailer and yielded encouraging results.