AI Case Study
France Telecom's Telekomunikacja Polksa realised that certain customers have a greater or lesser influences on networks of mobile phones users. If highly connected networkers churn then this is likely to cause a large ripple effect. To improve customer churn prediction and identification of who to retain they developed social graphs and analysis based on the transaction history and network connections of customers. This allowed them to improve prediction by 47%.
France Telecom Polksa improves customer churn prediction and identification of who to retain by 47% through analysis of customers and their social influence. The company realises
Industry
Telecommunications
Mobile Telecommunications Services
Project Overview
They built a ''social graph' from the call data records of millions of phone calls transiting through its network every month—looking, in particular, at patterns of who calls whom and at what frequency. The tool divides communities into roles such as 'networkers,' 'bridges,' 'leaders,' and 'followers.' For example, it detects the networkers, who link people together, and the leaders, who have a much greater impact on the network of people around them. That set of relational data gives the telecom-service provider much richer insight into who matters among those who might drop its service and, therefore, how hard to try to keep its most valuable customers. "
Reported Results
"...the accuracy of churn-prediction model accuracy has improved 47% allowing focus on how hard to retain influential customers."
Technology
Function
Strategy
Analytics
Background
"There’s tremendous value in accurately predicting churn at a customer-by-customer level at telecom companies. If a company offers discounts to people who would have stayed anyway, it has wasted its money. A lack of appropriate targeting can also make it overlook people who might leave for a competitor."
"...Telekomunikacja Polska, part of France Telecom-Orange Group and the largest fixed-line provider for voice and broadband services in Poland. The company wanted to quickly find ways to predict and address churn among its customers more effective than the traditional methods, including analysis of declining rates of use and calculations of lifetime customer value based on how long customers stayed with the service and how much they spent.
Benefits
Data
"...call data records of millions of phone calls transiting through its network every month—looking, in particular, at patterns of who calls whom and at what frequency."