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Predictive Routing - Model Efficiency Dashboard

This page describes how you can use the Model Efficiency Dashboard to see detailed information about the impact on contact center efficiency of enabling Genesys Predictive Routing (GPR), and compare the effectiveness of various GPR prediction models.

Note that the term 'dashboard' is used interchangeably with the term 'dossier’. Dashboards / dossiers provide an interactive, intuitive data visualization, summarizing key business indicators (KPIs). You can change how you view the data by using interactive features such as selectors, grouping, widgets, and visualizations, and explore data using multiple paths, though text, data filtering, and layers of organization.

Video: Introducing the Model Efficiency Dashboard

This video describes how to use the Model Efficiency Dashboard.

Understanding the Model Efficiency Dashboard

Predictive Routing — Model Efficiency Dashboard

The Predictive Routing — Model Efficiency Dashboard provides a bubble-graph summary that you can use to evaluate the impact on contact center efficiency of enabling GPR, and compare the effectiveness of various GPR prediction models. The dashboard includes graphical summaries of average agent scores, average time interactions waited in queue before being scored by Predictive Routing and distributed, and the percentage of interactions that encountered an error during Predictive Routing.

To help you understand the graph:

  • The larger the bubble on the graph, the more calls were accepted.
  • The color of the bubble indicates whether GPR was on or off.
  • The higher the bubble is on the vertical axis, the higher the average agent score.
Navigating the Model Efficiency Dashboard

This design allows you to see, at a glance, how evenly calls are distributed, relative to agent score. If you find that a large number of calls are being routed to the agents with the best scores, and very few calls to other agents, you may want to adjust the routing model.

To get a better idea of what this dashboard looks like, view sample output from the report:
Sample Predictive Routing — Model Efficiency Dashboard.pdf

The following table explains the prompts you can select when you generate the Predictive Routing - Model Efficiency Dashboard:

Prompt Description
Prompts on the Predictive Routing - Model Efficiency Dashboard
Pre-set Date Filter Choose a date from the list of preset options. If this prompt is set to anything other than none, the Report Date prompt is ignored. Default: Year-to-Date.
Start Date Choose the first date on which to report. This prompt has no effect if Pre-set Date Filter is set to anything other than none.
End Date Choose the last date on which to report. This prompt has no effect if Pre-set Date Filter is set to anything other than none.
Media Type Select one or more media types for which to gather data into the report.
Predictor Select one or more predictors to include in the report.
Model Select one or more prediction models to include in the report.
Tenant Select one or more tenants to include in the report.

The following table explains the attributes used on the Predictive Routing - Model Efficiency Dashboard:

Attribute Description
Attributes on the Predictive Routing - Model Efficiency Dashboard
Day Enables the organization of data based on the day/date on which the interaction occurred.
Predictor Switch Enables the organization of data based on whether Predictive Routing is ON, OFF, or for which an error occurred.

The following table explains the metrics used on the Predictive Routing - Model Efficiency Dashboard:

Metric Description
Metrics on the Predictive Routing - Model Efficiency Dashboard
% Error Percentage of active interactions that received a Predictive Routing error score.
Accepted Total number of calls accepted.
Avg Agent Score The sum of all Agent Scores (gpmAgentScore), divided by the total number of interactions where GPR was active.
Average Accept Time The average amount of time, in seconds, it took agents to accept, answer, or pull customer interactions.
This page was last edited on May 31, 2021, at 15:37.
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