Retail customers walk & search across store aisles and pick up products. The list of products in the customers’ basket depends on customers’ actual and perceived need. The perceived need could be generated and altered by the promotions and offers.
I am sure it would have happened to you. When you went a supermarket to buy couple of products, but ended up buying a lot of additional products. Whether you realize or not the additional products selections are driven by the promotions in the store.
In another scenario, a supermarket advertised discounts on a list of items if purchased together. For example, if a customer buys 5Kg floor, 5Litre Oil and 5Kg Sugar then overall cost 20% less. The customer may not require buying sugar but s/he is buying due to the offer.
The product combinations in an advertisement and the in-store promotions could be developed based on the customers’ transaction patterns and product basket analysis. But those may not necessarily align to individual customer needs.
A few and probably quite a few customers visit different aisles and SKUs, looks at the products but do not put in the basket. Hypothesis is that if a customer visit an aisle and spend time looking at the product SKUs, the customer have the need. The need is expected to be stronger than the perceived need or the identified by statistical models.
Critical and key points are tracking customer movement across store aisles and identifying the product SKUs which the customer have looked at and the amount of time spend by the customers.
When the customer completes its basket and reaches the counter, based on the above information, the customer’s basket and historical spending pattern, a real time and personalized offer could be solicited to the customer. The offer so made will be far more effective and personalized to the customers.
The processing of the above the information in real time requires sophisticated algorithms and analysis capability. The big data platform and big data analytics could be looked at as a potential solution.