Product recommendation example #1: Use machine learning technology
Machine learning allows predictive software to use the data it gathers to provide AI-driven product recommendations. As more information is incorporated into the algorithm, the accuracy of the recommendations improves and evolves.
Most product recommendation engines run on predictive analytics, which make sense of disparate customer data inputs to predict future behaviors. Once this data is analyzed and “trains” the software’s algorithm, it can begin matching customers to the products they’re most likely to buy.
Product recommendation example #2: Automate your product recommendations system
Automation is a key feature of product recommendation engines. It allows brands to easily scale their personalized recommendation efforts. Effective product recommendation engines automate nearly the entire process by collecting data, analyzing it, and delivering recommendations directly to customers. The less often a marketer or merchandiser has to manually step in, the more businesses save time, effort, and money.