It is no longer possible to manually manage a customized approach to each individual customer via every channel with the “perfect” offer that corresponds to the customer’s current shopping behavior, is in stock or is available in the customer’s desired store. This is where artificial intelligence (AI) in the form of self-learning algorithms comes into play.
Our personalization solution learns personalization rules (keyword: adaptive learning) automatically and in real time – e.g. on the basis of clicks, shopping carts, purchases, external and internal search queries, clicked categories and banners or even selected events. The AI-based software collects all of this data independently and continuously. In addition, the algorithms measure the acceptance of the generated content (keyword: reinforcement learning). If a generated recommendation is accepted by the customer, the system rewards itself. If the recommendation is not accepted, there is no reward. This is how the recommendation engine learns gradually and can better cater to each individual user in a more targeted way with each interaction.
Translated to retail, this means that if you use an algorithm that works according to the principle of reinforcement learning, you will be able to maximize the benefit of your “whole system” over the long term – in other words, the crucial variables such as sales, turnover and profit.
If you would like to learn more about personalization, we recommend our page entitled What is personalization.