Chances for the purchase of a particular product are very strongly linked to the content of the customer’s shopping basket

The sales data analysis usually indicates a very strong link between purchased categories/products. The shopping basket of a person who enters the shop reflects the set of their current needs and provides potential for purchasing subsequent products that answer these needs.


Shopping basket analysis gives an opportunity for building reliable prediction models which increase chances for the sale of the product. Basket analysis can be used to indicate the location for the product display that increases the likelihood of purchase or – due to direct knowledge about shopping baskets – to prepare appropriate targeting actions both as a part of a shopping trip and path-to-purchase.


A universally known manufacturer of soft drinks conducted a test in which customers leaving the cash register were offered a new product in a promotional offer. On average 7 % of the customers accepted the offer. The shopping basket analysis revealed that, for instance, the percentage of customers who decided to purchase the new product was two times higher (14%) among the buyers of energy and isotonic drinks. The new product matched the shopping mission and needs of customers who tend to buy such products. At the same time it became clear, in vicinity of which category the producer should locate their product.


Using basket analysis can lead to increasing the likelihood of the product purchase even by several hundred percent in comparison to a randomly selected location in the store.


  • Ask yourself: What is the involvement in the category? Does its character indicate people would be likely to devote time to asses alternatives on the shelf/display?
  • Observe customers’ behaviour in the store and compare sales data with the shelf layout – is there anything clearly indicating the priority rule effect?