Product Recommendations

The Coveo Machine Learning (Coveo ML) Product Recommendations (PR) feature takes advantage of Coveo Usage Analytics (Coveo UA) to suggest relevant products to end users based on their past and present interactions with your Coveo-powered commerce implementation.

Coveo ML PR enhances your customers' shopping experience by offering them products that suit their profile, context, and buying behaviors. In order to provide relevant suggestions, the model continuously learns from your end users' feedback by scoping their buyer profile and analyzing their positive and negative interactions with different products. Thanks to its multiple algorithms, Coveo ML PR can easily adapt its approach to your digital commerce strategy.

Depending on your context, you can leverage one or more of the available PR strategies:

Members with the required privileges can create, manage, and deploy Coveo ML PR models.

User Recommender

When leveraging the User recommender strategy, the model suggests products to the current user based on their general interests. To achieve this, the model learns from users' previous actions, and uses this information to find other customers that share similar browsing patterns. The model then suggests products that have been previously browsed by customers who share similar interests with the current user.

Frequently Bought Together

When leveraging the Frequently bought together strategy, the model suggests products frequently bought with the current product based on the shopping cart of other users. In other words, the model analyzes the items customers often buy together with the product that the user is currently looking at.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing a gaming computer. Based on other customers' purchase history, the model suggests gaming mice and keyboards that were bought together with the computer you are currently viewing.

Frequently Viewed Together

When leveraging the Frequently viewed together strategy, the model suggests items related to each of the current products based on other users` interactions with your commerce implementation in a single visit. Therefore, this strategy analyzes users` navigation behavior to provide the user with a selection of similar products that are often seen together in a single shopping session.

EXAMPLE

As a user of an electronic retailer commerce website, you are currently viewing the ABC gaming computer. Based on the interactions made by other users that have browsed the same computer in their shopping session, the model suggests products that have frequently been seen together with that computer in the same shopping session.

Since other users have also browsed the 123 and DEF computers along with the ABC gaming computer that you are currently viewing, the model suggests these computers to you.

Cart Recommender

When leveraging the Cart recommender strategy, the model analyzes frequent buying patterns by grouping sets of products that are frequently purchased together in the same shopping cart.

Given a cart, it will recommend products that were frequently bought together in previous similar carts. This strategy is ideal for cross-selling items on a shopping cart interface.

EXAMPLE

As a user of an electronics retailer commerce website, you are currently viewing your cart where you have a TV and a TV stand. Based on other customers’ purchase history, the model suggests TV accessories, such as HDMI cables, that were bought together with the same TV and TV stand you have in your cart.

When leveraging the Popular items (viewed) strategy, the model recommends the most viewed products. This strategy is useful when you want to display recommendations on a landing page and also used as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

When leveraging the Popular items (bought) strategy, the model recommends the most purchased products. This strategy is useful when you want to display recommendations on a landing page and also used as a backup solution for other strategies when they didn’t gather enough data to provide relevant recommendations.

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