Genesys predictive routing uses artificial intelligence to route interactions to the best agent available for the KPI that you set. The agent score derived by predictive routing is based on various drivers such as level of data available, skill level of agents, availability of agents, timeout period.

What data does Genesys AI use to make routing decisions?

Genesys data models rely heavily on data that is both populated and calculated/derived from other data in the system. The availability of all the required data ensures that the models function at their optimum levels. The following are examples of the features and data sources that predictive routing uses for agent scoring: 

  • Agent profile data such as skills, tenure, department, certificates, employee type. For more information, see Data requirements for predictive routing.
  • Agent performance data such as historic average handle time for a queue.
  • Customer history data such as the number of times they called the contact center in the last 30 days.

Currently, the primary sources of data are agent directory (for agents) and analytics (for customer and interaction data).

How are data models created and maintained?

To keep up with changing levels of agent proficiency and customer interaction contexts, the data models continually retrain and learn from the latest features. Genesys Cloud updates the features used for agent scoring with daily data and retrains the data models weekly. No data is retained in the models for more than 90 days. The retraining ensures that the latest operational data is taken into account for forthcoming routing decisions. 

As newer data models emerge with updated features, Genesys retires the older data models that are no longer relevant. Where models are unused (for example, where predictive routing is deactivated on a queue), they are deleted from the system automatically.

Genesys Cloud platform creates and maintains the data models used in predictive routing. For more information about how data is used in agent identification, see How AI model scores agents for predictive routing.

How do the features and data model work?

Predictive routing uses white-box models that allow gaining insights into how the features contribute to a prediction. Genesys helps you deduce the prediction by presenting a global interpretation that describes the average behavior of a model. Each input feature is given a percentage/score that represents its importance. A high value means that the feature will have a larger effect on the model’s predictions and ranking of agents. While a small value is given to unimportant features whose contribution is mostly ignored for the model’s predictions. For more information about features specific to a queue, see How AI model scores agents for predictive routing. 

How does Genesys AI protect personally identifiable information (PII)?

Genesys does not use PII for the agent scoring process. Genesys Cloud only uses transaction conversation data to train machine learning models. The agent profile and the performance data that are used in the scoring process do not contain any agent PII. 

How does Genesys ensure that no discrimination is introduced in the agent scoring process?

Genesys does create data models that require data such as gender and nationality that have the potential to introduce discrimination. To train a data model, predictive routing generates features only using transactional conversation data that does not contain PII. The absence of PII ensures that there is no scope for discrimination in the scoring process.

It must be noted that in order to keep the predictions as accurate as possible, predictive routing uses the most recent historical conversations, which may introduce what is known as a temporal bias.