Merchandising & Space allocation
Harness all your transactional data and shopper data to take the best merchandising decisions, building on (but not blindly following) our A.I based recommendations
Identify the customer decision tree
By tracking the individual behavior of each shopper, identify the customer keys of entry & sequences of choices in the category. Based on this, build the customer decision tree, down to the consumer need unit level (group of products addressing the same consumer need)
Adapt the merchandising sequence
Based on an in-depth understanding of the customer decision tree and the cross-purchasing level between groups of products, define the optimal merchandising sequence of a category, i.e the order in which sub-categories / product families must be presented to the client
Measure the performance of new merchandising plans
Based on a multi-criteria analysis (client profiles, sales profiles, seasonality…), identify the right short list of stores to run a test , as well as the control stores, against which the performance of the test must be measured. Run the test performance analysis and draw the right conclusions along a comprehensive set of KPIs (total sales, penetration, focus on specific client groups…)
Cluster stores
Run a thorough analysis of a network of stores along a wide set of criteria (sales structure profile, shopper profile, price sensitivity, promotional sensitivity, seasonality…), measure variabilities and extract one or several angles of store clustering. Adapt category strategies (on assortment, promotions, pricing..) to each cluster.
Optimize space allocation in store
For each store, analyze the sales and profit performance of each linear meter / square meter, liaise it with the client performance and define a space reallocation plan that optimize sales and/or profit, while respecting several operational constraints and/or commercial principles.