Intradiem, a provider of contact center automation solutions, has been awarded a U.S. patent for its Machine Learning (ML) Burnout and Attrition Indicator solution, which predicts attrition among contact center agents.
The new patent grants protection for Intradiem's machine learning model, which leverages a variety of customer data flowing through Intradiem's platform to identify agent burnout. The models used include linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and survival analysis.
Intradiem supports both agent engagement and well-being through this solution, which harnesses ML's predictive power to identify patterns of decreased productivity, stress, and burnout that typically lead to agent attrition. Drawing and connecting insights from a broad range of data, the solution assigns agents to a burnout risk category (low, moderate, high, or critical).
When the technology identifies an agent at risk of burnout, it can automatically trigger actions, such as scheduling breaks, recommending shift adjustments, or initiating wellness checks. These interventions can be tailored to individual agents according to specific patterns identified through the system's analytics.
"This patent highlights our ongoing commitment to finding novel ways to solve long-standing problems in our industry," said Intradiem Co-CEO Matt McConnell in a statement. "Early users are already sharing success stories of supervisors being alerted to agent burnout risks and taking actions to head off attrition."