Knowing customers’ product preferences has been the day-to-day business of the local shop owner. Making sure that the store has the right merchandise, at the right price, and the right volume is the core of what retailing is. In the digital world, it is getting easier to predict what products customers are interested in.
Using predictive analytics, web merchants can better forecast what products are best first sale items, cross-sell items, upsell items, and next sell items on an individual level. Amazon was the trailblazer in personalizing the shopping experience by providing product recommendations based on browsing and buying behavior. With the advances in marketing technology, any retailer can now afford to make their ecommerce site a personalized shopping experience.
There are two methods used to predict what products a shopper might be interested in. The first method is product-to-product recommendations. It is the standard “People who bought this, bought these!” type of recommendations. It completely focuses on the product and uses historical data on what products are typically bought together. This is a general recommendation and is normally used for new visitors that are anonymous.
The second method used to predict what products a shopper might be interested in is the product-to-user method. This method is used when the retailer has knowledge about the shopper. The shopper is a returning visitor or better yet, a returning customer. Using machine-learning predictive technology, the site can provide recommendations based on the visitor’s past shopping behaviors, web journey, and real-time site activity. These recommendations are served, as the visitor browses the site, to drive discovery, upsell/cross-sell, and average order value.
With technology now available to do product-to-user recommendations, any retailer can deliver a more personalized experience. Consumers, in turn, will reward those retailers that are delivering personalized experiences by returning and purchasing again.
