Macy’s: A Model Driven Approach to Store Selling Space Optimization
Store Locations sales performance by merchandise business was until now being compared against benchmarks formulated by averages of similar scale. A new approach has been developed integrating Exploratory Analytics (Co-clustering) and Prescriptive Analytics (Non‐Linear Spline Regression Optimization Model and Seasonal (Random Walk) Autoregressive Integrated Moving Average (SARIMA) Model). We have developed a new workflow to integrate all three models in recommending optimal store layouts and merchandize mix for new store locations and major remodels of existing ones. In this three-tiered process, the analyst first identifies the statistical cluster membership of the under analysis location and formulates a plan based on that benchmark. Then he/she invokes the optimization model that provides the space adjustment recommendations that maximize its sales potential based on existing cross‐sectional data (for remodel stores). In the final step, the forecasting model is used to validate whether the recommendations made based on cross‐sectional (historical) data hold true in the time‐frame where these changes (projected store opening or remodel completion) are expected to take place.
Macy’s is competing as a 2018 Innovative Applications in Analytics Award Finalist, to see other finalists, click here.
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