What are tools called that make purchasing recommendations based on user behavior?

Collaborative filtering tools are designed to analyze user behavior and make purchasing recommendations by identifying patterns across different users. This technique involves collecting and processing data from users' past interactions, such as past purchases, browsing history, and preferences. By comparing the behaviors of similar users, these tools can predict and suggest products that those users might enjoy based on the shared characteristics or interests.

For example, if User A and User B have a history of purchasing similar items, and User A buys a new product that User B has not yet viewed, the collaborative filtering tool can recommend that product to User B. This method leverages the collective knowledge and purchasing trends of a broader user base, making it highly effective for personalized marketing and tailored shopping experiences.

Other options may relate to the concept of tracking user behavior but do not specifically engage in making recommendations based on the behavior of similar users. Hence, while clickstream tracking tools and customer tracking tools focus on collecting data, they stop short of analyzing this data in the manner that collaborative filtering does for recommendations.