154-2008: Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry

The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue generation. Segmentation is the process of developing meaningful customer groups that are similar based on individual account characteristics and behaviors. The goal of segmentation is to know your customer better and to apply that knowledge to increase profitability, reduce operational cost, and enhance customer service. Segmentation can provide a multidimensional view of the customer for better treatment targeting. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted treatments to lift account profitability. In this paper we outline several segmentation techniques using SAS Enterprise MinerTM. INTRODUCTION Rapid advances in computer technology and an explosion of data collection systems over the last thirty years make it more critical for business to understand their customers. Companies employing data driven analytical strategies often enjoy a competitive advantage. Many organizations across several industries widely employ analytical models to gain a better understanding of their customers. They use these models to predict a wide array of events such as behavioral risk, fraud, or the likelihood of response. Regardless of the predictive variable, a single model may not perform optimally across the target population because there may be distinct segments with different characteristics inherent in the population. Segmentation may be done judgmentally based on experience, but such segmentation schema is limited to the use of only a few variables at best. True multivariate segmentation with the goal of identifying the different segments in your population is best achieved through the use of cluster analysis. Clustering and profiling of the customer base can answer the following questions: ♦ Who are my customers? ♦ How profitable are my customers? ♦ Who are my least profitable customers? ♦ Why are my customers leaving? ♦ What do my best customers look like? This paper discusses the use of SAS Enterprise Miner to segment a population of customers using cluster analysis and decision trees. Focus is placed on the methodology rather than the results to ensure the integrity and confidentiality of customer data. Other statistical strategies are presented in this paper which could be employed in the pursuit of further customer intelligence. It has been said that statistical cluster analysis is as much art as it is science because it is performed without the benefit of well established statistical criteria. Data Mining and Predictive Modeling SAS Global Forum 2008