Case Study: U.S. Telecom Giant Achieves 1.3% Churn Reduction and 68% Model Accuracy Improvement with Cognizant

With Data Science, the Customer’s Voice Is Loud and Clear

Cognizant worked with a U.S. telecom giant to improve customer insight for its wireline business after existing analytics and churn models were producing confusing results. The telecom’s goal was to boost customer satisfaction and retention by better identifying at‑risk customers and the root causes of dissatisfaction.

Cognizant applied advanced data‑science techniques to improve model accuracy, identified high‑risk customers, and mined text inputs from customer interactions to develop a sentiment score that highlighted trending reasons for satisfaction and displeasure. The initiative increased focus on first‑call and first‑ticket resolution, yielding a 68% improvement in churn‑model accuracy, a 1.3% reduction in customer churn, and a 35% drop in repeat repair rate.

Telecomgiant