The prudsys RDE algorithms recognize not only the customer behavior and respond to it (e.g. with individual recommendations), but they also measure the acceptance of the generated content at the same time (= reinforcement learning). When a customer accepts (i.e. clicks on) a generated recommendation, the system rewards itself. If the recommendation is not accepted, there is no reward. This way, the prudsys RDE gets to know its customers better with each move and can tap into the individual preferences of each customer in a more strategic way.
There’s more: The prudsys RDE can deviate from learned patterns by way of so-called exploit and explore mechanisms, e.g. to recommend products or content that has never been recommended or has not been recommended for a very long time. The AI ensures that its scope of learning and action remains flexible by trying out alternatives or new variants. For your customers, that means that they will always see new content and products. They are not stuck in a filter bubble. This is a great advantage, especially for quickly changing product ranges: Products from new collections or brands flow automatically and immediately into the recommendations.
An intelligent personalization solution evaluates large volumes of data in real time, recognizes patterns and performs the “right” action in order to reach the corporate goals of the retailer.