User profile

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About the user profile
User Profile dimensions

A user profile contains behavioral information about a given user gathered from their actions history. This information is leveraged internally by Coveo services, such as the Coveo for Salesforce User Actions component or Coveo Machine Learning (Coveo ML) models to provide more personalized and task-oriented recommendations.

To do so, Coveo uses all users' actions recorded by Coveo Usage Analytics (Coveo UA) and applies statistical and machine learning techniques to analyze behavioral data to better understand user interests, preferences, and intents.

About the user profile

A user profile is divided into multiple user profile dimensions, each one of them representing a user’s activity, characteristic, or interest (for example, last items viewed, preferred brands, topics of interest).

These dimensions can either be calculated and updated in real time, or according to a specific usage analytics data exportation period.

Some of the user profile dimensions are automatically built-in Coveo functionalities, while others must be enabled to take advantage of a user profile in Coveo services. Contact us to enable these dimensions.

Current capabilities

User profiles are used by the following services:

Coveo ML Query Suggestions

For all Coveo ML Query Suggestion (QS) and Predictive Query Suggestion (PQS) models, the user topics dimension is automatically activated, which means that a given QS or PQS model adapts its recommendations according to the user’s main topic of interest.

Coveo ML Product Recommendations

Currently, some Coveo ML Product Recommendation (PR) strategies, such as the User recommender and Session recommendations strategies can use the history of actions dimension to constantly adapt the recommendations according to the user’s navigation history and product preferences.

To use the history of actions dimension in PR model strategies, the dimension must be enabled. Contact us to enable this dimension.

Coveo ML Content Recommendations

Coveo ML Content Recommendation (CR) models can leverage the history of actions dimension to personalize the recommendations.

By default, Coveo ML CR models use the information of the actionsHistory query parameter, which is fed by a browser cookie that stores the user’s session navigation history.

When leveraging the history of actions dimension, Coveo ML CR models don’t rely on the user’s actionsHistory to obtain their session’s navigation history, removing the need for users to both store this information in a browser cookie and send it to the model.

Coveo ML CR only uses the user’s client ID to query the user profile database which stores their session navigation history.

To use the history of actions dimension in a CR model, the dimension must be enabled. Contact us to enable this dimension.

The Coveo for Salesforce User Actions component uses the history of actions dimension to record the history of actions performed by a given user.

This allows support agents to see, in the Salesforce Lightning console, up to the last 2,000 actions performed by an end user across any Coveo page or component, which gives support agents crucial insights on the end user’s task.

To use the history of actions dimension in the User Actions component, the dimension must be enabled in the Coveo Administration Console. See Enabling User Action History for instructions.

User Profile dimensions

User profiles contain information learned from the following dimensions.

History of actions

A user profile is constantly being updated according to the actions a user performed.

This dimension records the last 2,000 actions performed by the user, including searches, clicks, and consulted items.

Coveo services such as the Coveo for Salesforce User Actions Lightning Component, Product Recommendations, and Content Recommendations ML models use this information to personalize the user experience in real time according to the actions performed by the user in their current session.

Example

During a shopping session on a Coveo-powered pet supplies commerce interface, an unauthenticated user performs the following actions:

Performs the dog collar query.
Selects the red value of the Color facet to refine the query.
Clicks one of the collars that appears on the results list.
Adds the collar to the shopping cart.

By comparing the actions performed in the above session with those of other users who performed similar actions, a Coveo ML PR model can deduce that the user is currently looking for dog products.

Therefore, the model can recommend products that have been purchased or viewed by other similar users who also purchased the same dog collar the current user added to their cart.

User topics

Query Suggestions ML models adapt their recommendations according to the distribution of topic clusters learned for a given user.

Example

In a search interface in which a Coveo ML QS model is integrated, the query completion suggestions will differ depending on the user’s profile. This level of personalization provides your users with a more intuitive experience as the model’s suggestions are adapted to their preferences and search intents.