New university research finds retailers can use shoppers’ familiarity with a store’s layout to bring a more data-driven approach to in-store merchandising similar to online merchandising.
The researchers developed a product allocation model that uses data mining techniques to extract profitability and product affinity details from tens of thousands of genuine customer transactions contained in Microsoft’s Foodmart database. The model then used a three-step process to determine ideal product placement for stores that periodically rearrange their items:
