Case Study: a major telecom provider reduces complaint call volume with SparkCognition

Predicting Complaints for Telecom

A major telecom provider, receiving 6,000 customer complaints daily, needed to reduce call volume and increase customer satisfaction. They partnered with SparkCognition to proactively predict and address customer issues before they led to complaints, focusing on a key challenge where unhappy customers rarely reported the problems that bothered them most.

Using its DeepNLP and Darwin products, SparkCognition implemented a machine learning solution to analyze customer data, predict which customers were likely to complain within two days, and diagnose the issue. This allowed for proactive resolution. As a result, SparkCognition helped the telecom provider achieve an expected 33% reduction in call volume, equating to 2,000 fewer complaints per day, while also improving customer retention and brand loyalty.