IBM > Case Studies > Predictive Analytics Boosts Customer Satisfaction and Reduces Churn for Cablevisión

Predictive Analytics Boosts Customer Satisfaction and Reduces Churn for Cablevisión

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Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Application Infrastructure & Middleware - Middleware, SDKs & Libraries
Applicable Industries
  • Healthcare & Hospitals
  • Telecommunications
Applicable Functions
  • Quality Assurance
  • Sales & Marketing
Use Cases
  • Inventory Management
  • Visual Quality Detection
Services
  • Testing & Certification
The Challenge
Cablevisión, one of Argentina’s leading media and communications companies, was facing a challenge in boosting customer loyalty and minimizing churn. The company wanted to enhance service quality and customer satisfaction but lacked an effective method to identify dissatisfied customers and address their issues. Regular satisfaction surveys did not provide a comprehensive picture as many subscribers chose not to participate. Moreover, if customers did not report their problems to the helpdesk, the company often had no feedback from them at all. The company realized that it needed to diagnose and resolve connectivity problems early to retain customers. The challenge was to predict which internet subscribers were dissatisfied, regardless of whether they complained or not, by gathering data about network status from customers’ modems and comparing it with the results of satisfaction surveys.
About The Customer
Cablevisión is a leading media and communications company in Argentina. Founded in 1981, the company provides cable TV services to 3.5 million subscribers and internet services to 1.8 million more. Employing more than 10,000 people, Cablevisión is committed to delivering high-quality services to its customers. The company's primary goal is to maintain a reliable revenue stream by keeping customers satisfied and minimizing churn. One of the most important factors in customer satisfaction for Cablevisión is the quality of service that it delivers. The company understands that service quality has a far greater impact on customer satisfaction than any other factor, including the price of the service.
The Solution
To address this challenge, Cablevisión collaborated with IBM Business Partner BeSmart and the IBM® Global Business Services® Business Analytics & Strategy team to deploy IBM SPSS® Modeler, a predictive analytics platform. This platform enabled the company to analyze large volumes of service quality data generated by the modems of its 1.8 million internet subscribers. The company first used CSAT models to analyze over 25,000 satisfaction surveys and then used 42 million daily network indicators to examine the health of those customers’ modems in the 15 days preceding the survey. Using regression tree techniques, Cablevisión mapped out various combinations of connectivity problems and scored each combination based on how likely it was to cause dissatisfaction. This insight allowed the company to predict which customers were most likely to be dissatisfied with the service quality.
Operational Impact
  • The implementation of the predictive analytics platform had significant operational results for Cablevisión. The company was able to proactively identify and resolve service quality issues, which led to an increase in customer satisfaction and loyalty. The proactive approach was appreciated by customers, leading to reputational benefits as happy customers were more likely to recommend Cablevisión's services to friends and family. Furthermore, the proactive identification of service issues resulted in fewer customers needing to call the contact center, leading to cost savings on managing complaints. The company has now incorporated proactive network troubleshooting into its regular operations and placed customer satisfaction management at the heart of its strategy. The management committee analyzes the evolution of the client experience each month and discusses ideas for further improvements.
Quantitative Benefit
  • Boosted customer satisfaction by predicting and resolving service quality issues
  • Reduced the cost of customer service by decreasing complaints and technical support requests
  • Identified that the group of customers most likely to be dissatisfied by service quality made up just one percent of the total subscriber base

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