Using Merchandise Optimization, retail executives are able to implement optimized store-by-store space and merchandise assortment plans based on analysis of historical differences in diminishing returns across categories throughout the network. APT Merchandise Optimization uses parameters such as historical store-by-store space, productivity, and inventory to calculate the elasticity of sales with respect to space. This elasticity is then combined with performance data for individual retail stores to develop customized store-by-store plans. APT’s algorithms allow users to optimize space trade-offs at all levels of the store, from determining how much space to allocate to each department to determining how much space to allocate to each planogram.
APT Merchandise Optimization helps answer core strategic questions, including:
How can I improve my merchandising assortment to maximize profits, both within and across departments?
APT’s proprietary algorithms calculate the elasticity of space for a department or section of the store based on the understanding that there are diminishing returns from allocating incremental space. By comparing sales to space, inventory, or other controllable driver data, Merchandise Optimization determines how much space to allocate to each department or planogram. The algorithms use custom constraints such as minimum department size or fixture length to create recommendations that can be immediately applied.
How does a merchandising change in one area drive sales, margin, and transaction changes in another?
Because APT’s solution simultaneously analyzes many categories, the assignment of each unit of space takes into account the productivity, rate of diminishing returns, and constraints of the other categories.
Which types of stores have the highest potential gains from merchandise changes?
APT’s analysis tools reveal the store, demographic, and competitive factors that are most important in optimizing merchandise allocation.
How successful was my last merchandising change?
APT’s Merchandise Optimization provides recommendations for new business actions that can be tested in the marketplace, evaluated for effectiveness, and fine-tuned for optimal impact using Test & Learn™.