![]() ![]() Numerical simulations found the researcher’s model significantly outperformed allocation methods that rely solely on visual rearrangement and other modeling techniques that use data association. To the best of our knowledge, no previous research has considered this effect.” “Every rearrangement can then be used as the basis for the next. “This last step is designed so that people looking in a familiar place for, say, potato chips will notice something new that our data tells us will interest them,” said Gihan Edirisinghe, the study’s lead author and a Western Kentucky University professor, in a press release. Employ “past-aisle impulse” to take advantage of customers’ familiarity with where products used to be to determine future store layouts.Determine which items tend to be purchased together so they can be placed in a way that customers will notice something interesting next to a planned purchase.Identify a store’s most profitable products to be placed in highly visible locations.The model then used a three-step process to determine ideal product placement for stores that periodically rearrange their items: 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. 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.
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