TMT

Predicting call volumes and reducing costs by $6 - $10M annually

Challenges
A leading telecom operator wanted to predict call volumes for planned or unplanned events, and reduce the number of calls to the call center by identifying customers likely to call and the reason for calling. Enhance customer experience with proactive outreach and faster first-call resolution.
Solutions
Developed call reason group logic through multiple rounds of model refinement, including multi-level models for different group of reasons granularity. Included a framework of ensemble models, clustering, and reinforcement learning to determine customer treatment and channel. Implemented a real-time model scoring to predict call reasons and engage before a customer calls. Proven framework and models for call classification and operations optimization.
Outcomes
14%
billing calls and 6% service calls reduced in a test environment
$6-$10M
savings annually
Predict call volumes and take operational decisions faster
14%
billing calls and 6% service calls reduced in a test environment
$6-$10M
savings annually
Predict call volumes and take operational decisions faster