View features that influenced predictive routing decisions

AI-generated summary

Genesys Cloud's predictive routing system provides visibility into the top 10 features influencing queue-based routing decisions through the Performance > Workspace > Predictive Routing interface in Analytics Workspace. Features are organized into categories—agent features, customer features, other features, and miscellaneous—and classified using specific identifiers including user (agent), caller (customer), userqueue (agent-queue interaction), callerqueue (caller-queue interaction), and calculated fields. The system measures data through counts and time metrics across voice, email, and message media types, with sentiment analysis capabilities that incorporate sentiment scores and trends when enabled. Sentiment scores apply to users and userqueue categories, while both scores and trends apply to customer and customerQueue categories. Metrics are analyzed over specified time periods using mathematical aggregation functions to determine feature importance in routing predictions. Access to predictive routing analysis requires Routing > Predictor Model – View and Routing > Predictor Model Feature – View permissions. Users can navigate through Menu > Analytics > Analytics Workspace, select the Predictive Routing view under the Contact Center section, choose a queue, open the Predictive Model tab, and select the desired media type to view feature breakdowns and detailed routing decision influences. When a feature scores 10% and ranks as a top contributor, it indicates that the associated parameter—such as consult transfer volume among voice calls—represents a major factor in the routing algorithm's agent assignment decisions. Genesys Cloud provides supplementary resources including documentation on predictive routing functionality, AI model scoring mechanisms, artificial intelligence applications in routing operations, predictive routing queue detail views, and data action updates. Users can access community support for additional questions and submit feedback through structured channels to report issues such as inaccurate information, outdated content, or clarity concerns. The feedback mechanism accommodates various user roles including agents, administrators, developers/engineers, partners, supervisors, and support personnel.

PrerequisitesClick to expand

You can view the top features that contribute to the routing decisions in a specific queue. For more information about feature importance and what they mean in the prediction process, see How AI model scores agents for predictive routing.

Go to Menu > Analytics > Analytics Workspace.
In the Contact Center section, search for Predictive Routing and then click the view name to open it.
Click the queue name and open the Predictive Model tab.
From the list, choose the media type for which you must view the details.

The page classifies the top 10 features into these categories: agent features, customer features, and other features. Features that fall out of the top 10 features appear under the miscellaneous feature list. To view the breakdown of the features, select View all features.

The following is an example of a feature that influenced the routing decision.

The following table helps you interpret the feature template based on the previous example:

Callout	Description
1	Specifies if the feature is about the agent, caller, or interaction. Possible values are: user – Indicates agent caller – Indicates customer and applicable only to voice media type userqueue – Indicates agent’s interaction on the queue callerqueue – Indicates caller’s interaction on the queue calculated – Indicates that the feature is calculated from other fields such as interaction time
2	Specifies how the feature data is measured. Possible values are: n – Indicates count t – Indicates time
3	Specifies the activity that happened on the queue. Note: If your organization has sentiment analysis enabled, the feature can contain one or both of the following: Sentiment score – The sentiment score of a conversation given the overall score requests. Sentiment trend – The trend of a conversation given all its sentiments. User and userqueue use only sentimentScore; whereas customer and customerQueue use both sentimentScore and sentimentTrend.
4	Specifies the media type. Possible values are: voice email message
5	Specifies the period over which the metric was calculated.
6	Specifies the mathematical function used to derive the aggregate value.

Therefore, the previous example indicates that the data relates to an agent who handled voice calls for which there were consult transfers. The number of voice calls were calculated for 30 days. If the percentage of this feature is 10 and is ranked top, it indicates that one of the major contributors of the routing decision was the number of voice calls that had consult transfers.

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View features that influenced predictive routing decisions

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