Space Optimization
A leading retailer used APT to optimize its space allocation and drive millions in annual profits
The Company
A large fashion retail chain, operating over 1,000 outlets
The Challenge
The client had been investigating means for improving their current merchandise allocation plan for years. After numerous consulting engagements, they had settled on using a clustering scheme based on market demographics to divide their layout, but management remained unconvinced that this was the optimal method. Many senior decision makers within the organization felt there were opportunities to improve this approach and that doing so would have significant financial and brand benefits. However, the client lacked internal consensus about how to improve the clusters and this had stymied their ability to move forward.
The client looked to the APT Suite to help them better allocate their merchandise.
The Solution
With the help of APT's consultants and the APT Suite, the retailer's team of analysts began by classifying their products into categories of similar and substitute products. This allowed them to measure the current profit and space allocation for each category at each store to establish a baseline for Test & Learn comparison. They also used the software to measure how profits generated by each category changed as the space allocated to that category changed. They used the software to build on this understanding and calculate the optimal category mix, by store.
Using the modeling capability provided by the APT suite, the client built optimized space plans for each store in the network. By clustering on these resulting plans, the client was able to provide grouped stores with the same recommended allocation plan, allowing for customization to drive profits, but keeping a level of standardization across the network.
After developing the clusters based on similar optimal mix, the client then profiled each cluster to understand the common demographic, competitive and psychographic attributes of each cluster. This allowed them to communicate a picture of the "typical" customer of each cluster to their merchants, media buyers, and other key internal constituents to allow them to more effectively optimize their plans for each group.
Results
By optimizing their merchandise allocation by cluster and by better targeting and buying for the customers in each cluster, the retailer is expected to realize ~$6-7 MM in incremental annual profits.
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