Coveo ML for Commerce offers cloud-based AI-powered search and personalized recommendations to enhance customer engagement.
There are various commerce model types used in different applications.
- Product Recommendations (PR)
- Query Suggestions (QS)
- Automatic Relevance Tuning (ART)
- Dynamic Navigation Experience (DNE)
There are more advanced and personalized types.
- Predictive Query Suggestions (PQS)
- Session-Based Product Recommendations (SBPR)
- Intent-Aware Product Ranking (IAPR)
In this series, we will discuss about Product Recommendations (PR) model which provides relevant product suggestion (for your Coveo-powered commerce implementation) to end users. This is heavily dependent on the Coveo Usage Analytics (Coveo UA) which tracks the end user's search event, product click events, add to cart events and purchase events. With this data, model gets trained continuously and starts returning relevant results to the end users.
Coveo ML PR models offer strategies to adapt product recommendations to the evolving needs and preferences of customers throughout their shopping experience. Various strategy types for PR models are listed below.
- User recommender
- Frequently bought together
- Frequently viewed together
- Cart recommender
- Popular items (viewed)
- Popular items (bought)
In the next article, we will discuss about the prerequisites of PR models.
Happy Searching and Happy Customer! 😊
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