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Customer analytics can reveal important insights about a company’s consumer base. But when the analytical models confuse more than they clarify, changes need to be made.
For one U.S. telecom giant, improving customer insight was a key strategy to increase customer satisfaction, and thus retention, for its wireline business. The company looked to Cognizant’s data science services to help transform its approach to analyzing customer data.
Cognizant improved both the accuracy of the telecom’s existing models using advanced methods and its identification of high-risk customers. We also helped the company uncover other important customer service enhancements by analyzing the text data customers enter during their interactions. This information also revealed ways the telecom could increase both first call and first ticket resolutions.
We developed a sentiment score to identify trending reasons for customer satisfaction and displeasure. Based on customer surveys, this analysis helps the telecom focus on real impacts to customer satisfaction.
Addressing high-risk customers is just one way data can improve customer satisfaction. Cognizant’s multifaceted approach helps the telecom address issues proactively, reducing customer churn and repeated contact for common issues.
reduction in customer churn rate
improvement in accuracy of customer churn model
reduction in repeat repair rate
