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We present our work with a global online fashion retailer, Zalando, as an example of how a global retailer can utilize massive amount of data to optimize price discount decisions over a large number of products in multiple countries on a weekly basis. Given demand forecasts under a collection of discrete prices, Zalando’s objective is to set discount levels to maximize total profit over the entire selling horizon while taking into account both local and global business constraints. Local constraints refer to single product level requirements, where Zalando needs to balance sales across different countries and over different weeks while adhering to a first-come-first serve policy. That is, as long as product inventory exists, a customer is served independent of the customer’s origin country or time of arrival. Global constraints refer to specific targets set by management for different product categories and each country. We address these challenges by applying a three-step process. In the first step, we cluster products into groups that behave similarly and solve the aggregated problem in a way that allows us to decouple the problem into a problem for each product category. Each product category includes thousands of individual products (SKUs) and the various markets where products are sold, each of which with its own target sales and margins. In the second step, we decompose this problem using Lagrangian relaxation into a problem for each product (SKU) and provide an efficient way to identify the Lagrange multipliers. Finally, in the last step, we optimize decisions for individual products and also address local business constraints. For this new approach, which was implemented as part of Zalando's price discount decision process, we provide results from offline tests and field experiments to demonstrate its benefit.