Using Data Mining in telecommunications to prevent customer churn
The telecommunications industry generates and stores a tremendous amount of data. These data include call detail data, network data and customer data. The amount of data is so great that manual analysis of the data is difficult, if not impossible. The need to handle such large volumes of data led to the development of knowledge-based expert systems. These automated systems performed important functions such as identifying fraudulent phone calls, identifying network faults and preventing of customer churn. A serious issue with telecommunication companies is customer churn. Customer churn involves a customer leaving one telecommunication company for another. Customer churn is a significant problem because of the associated loss of revenue and the high cost of attracting new customers. Some of the worst cases of customer churn occurred several years ago when competing long distance companies offered special incentives, typically $50 or $100, for signing up with their company—a practice which led to customers repeatedly switching carriers in order to earn the incentives.
Data mining techniques which we have, now permit companies the ability to mine historical data in order to predict when a customer is likely to leave. These techniques typically utilize billing data, call detail data, subscription information (calling plan, features, contract expiration data) and customer information. Based on the induced model, the company can then take action, if desired. For example, a wireless company might offer a customer a free phone for extending their contract. One such effort utilized a neural network to estimate the probability of cancellation at a given time in the future. The telecommunications industry has been one of the earliest adopters of data mining technology, largely because of the amount and quality of the data that it collects. This has resulted in many successful data mining applications. Given the fierce competition in the telecommunications industry, one can only expect the use of data mining to accelerate, as companies strive to operate more efficiently and gain a competitive advantage.