Personalized product recommendations are generated with user data taken from your website. Ideally, you’ll only show your customers products that they actually would like to buy. The more personalized the experience, the better.
When done right, personalized product recommendations can save you money and increase your customer retention. A 2018 report shows that returning customers who engaged with a suggested product (by clicking on it and reading the description, for example) were 55% more likely to make a purchase during that shopping session. For new customers, that figure was a cool 70%.
Recommendation engines, which are programs that analyze data on both products and users, drive features like this. Not only do retailers employ these engines, video streaming websites use them to generate lists of recommended clips for their users to watch.
There are 3 main types of recommendation engines:
Collaborative filtering systems
Content-based filtering systems
Hybrid recommendation systems