Historically, the hierarchical positioning of the different categories has often been based on their industrial characteristics, purchasing logic or exposure constraints. For example, for the shopper, there are no such categories as “frozen” or “chilled”, but pizzas or fish that can be bought in one or the other form. In the same way, “sliced cold cuts” is not a category: ham can be bought whole, cut, or packaged and pre-sliced, but what the customer is looking for is above all ham and not sausage.
While some categories such as kitchen paper or water are relatively simple in their offers, in complex categories, such as beauty or hair care, it is important to understand the buyer’s entry key into the category to facilitate:
- The planned act of purchase (“I can easily find the reference of the Pantene shampoo for dry hair that I usually use”)
- The understanding of the category and the choice of the appropriate product for shoppers who make their decision in front of shelves sometimes packed with several hundred SKUs divided into dozens of subfamilies.
A categorization based on shopper insights and sell-out data per store has a fundamental impact not only on the organization of the shelf (or planogram) and the definition of an effective assortment, but also in the very definition of categories and store organization.
Creating a universe dedicated to babies gathering everything that may be useful for new parents or implanting SKUs of certain categories in the shelves of complementary products (“cross merchandising” of alcoholic beverages and snacks, for example) are now common applications of this type of learnings.