The purchasing behaviour of consumers is highly fragmented. It is no longer enough to target your selection and offering to certain customer segments or slavishly follow the principles of the traditional chain concept or mass marketing. Instead, customers need to be understood as individuals. In addition to skilled staff, the key factors for the competitiveness of a store include the selection, optimisation of shelf space, pricing, and implementation of promotions. These have to be considered against the current clientele and competition situation.
AIS (Assortment In Space) is a tool that utilises artificial intelligence and enables a store’s various databases to be synchronised in order to optimise the selection and use of shelf space. Optimisation is based on predicted demand, which also allows emphasis on the sales and the profit, and consideration of the various special needs of individual stores. Sales and profit scenarios allow you to see the sales and financial impact of various solutions on the end result. The model can use unstructured data, such as feedback from customer service and panel results, which means that customer satisfaction can also be included in the analysis, not only by way of market baskets but also through perceptions.
Once you have your optimised selection, the optimisation of shelf space can be started. This requires precise data on the available shelf space and the products. The optimisation of databases makes it possible to extend the space division of shelf modules to the product level. The resulting product selection and shelf-space recommendation is the optimal solution for customer satisfaction, sales, and business profit. Demand forecasts allow you to manage stock value and enhance turnover rate and subsequently profitability.
So, how should products be priced these days, when reduction of prices is the main selling point? Artificial intelligence has a good solution for this, too. The key is demand response, i.e. how the price affects the demand of an individual product and its optimal price considering the total sales and profit. However, examining individual products does not always offer the best overall picture, as relations between different products may also need to be considered. Does the sale of smoked fish increase the sale of lemons or the sale of cake bases boost sales of cream and fresh berries? How much does a well-selected offer increase the total volume of a shopping basket?
Marketing automation is probably the best-known artificial intelligence application designed for the retail sector. It enables optimisation of the product selection so that it is most attractive for the customer at the right time and through the right channel. This method of implementing marketing to meet customer needs is based on a customer’s purchase history, and also on the modelling of mass data. If other households that base their choices on heathy eating have added a new muesli, for example, to their shopping basket, the algorithm can suggest the same product to customers with a similar profile who have not yet purchased that product. Why keep on targeting random offers to customers when you can turn marketing into a service that makes their everyday life easier?
Very few customers go to the food store just to hang out, as the aim is to get the necessary shopping over and done with as painlessly as possible. Artificial intelligence can be used to attract customers to the store using suitable offers. The right product selection, efficient shelving, and optimised pricing are the best ways of ensuring that customers are as satisfied as possible with their visit to the store and, at best, will recommend the store to their neighbours.
If this subject is of interest and relevant to you, Houston Analytics is the leader in artificial intelligence applications in the retail sector.