Abstract: Past sales data for a product sold at stores in a chain of retail stores is received by a computing device. For each segment, the stores in the chain are grouped into one or more clusters. The average sales of the product in each cluster and the average sales of the product in all of the stores are calculated based on the past sales data. A cluster variation and a total variation may be determined for each of the stores based on the past sales data. A correlation indicative of an effectiveness of the segmentation strategy to reduce sales variation between stores in each of the plurality of clusters may also be determined based on the at least cluster variation.

Abstract: Sales transaction data of products sold at a store of a retailer is employed to identify opportunities to reallocate product inventory among the products based on characteristics of sales of the products. The sales transaction data is employed to determine characteristics of product sales, including, e.g., sales volume and sell-through. The number of units of products inventoried in a store can be reallocated based on sales volume and sell-through of the products indicated by the sales transaction data. After reallocation to determine new inventory levels for the products, an estimate of sales performance for the products can be determined at the new inventory levels and the estimated sales can be compared to past actual sales measurements to determine the potential effect of changing the number of units of products allocated to inventory in a store in a retail chain of a retailer.