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:
- Identify a store’s most profitable products to be placed in highly visible locations.
- 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.
- Employ “past-aisle impulse” to take advantage of customers’ familiarity with where products used to be to determine future store layouts.
“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. “Every
rearrangement can then be used as the basis for the next. To the best of our knowledge, no previous research has considered this effect.”
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.
Jeff Sward, founding partner, Merchandising Metrics, and a BrainTrust member, told RetailWire he liked how the research zeroed in on identifying a store's most profitable products and which items tend to be purchased together. He said, “It's all about building average basket size or average transaction value as profitably as possible.”
“Our allocation method could ultimately be something that store managers could install and use with a little training,” said Washington State University professor and co-author Chuck Munson. “When you take into consideration the fact that 80 percent of shoppers don’t make a list before visiting a brick-and-mortar retailer, it is easy to see how important something like this could be to maximizing profits and helping physical stores compete with online retailers.”
