Personalization engines need to be sensitive to fluctuations in browsing and spending patterns due to life events. This boils down to understanding the true intent of a given shopper.
Computer vision-based dynamic retail personalization uncovers shopper intent using a variety of signals. Each click on a product allows the algorithms to clarify a shopper’s specific attribute preferences as well. When this is combined with historical behavioral patterns logged by the engine, it becomes possible for the engine to accurately map to shopper intent, highlighting the most relevant products using real-time personalization.
Collaborative and content-based filtering often provide irrelevant recommendations as they fail to look at shopper behavior in context. Dynamic personalization uses real-time and historical data, to deliver the right product, to the right shopper at the right time, even when a shopper is looking to make an ‘unusual’ purchase, for a gift or a special occasion.