Customer Retention Predictive Modeling in HealthCare Insurance Industry

Current economic volatility has increased the need in industries with limited resources to identify new opportunities to retain its current customer base. Customer retention is one of the key factors to sustain business growth. Developing retention models assist the company by identifying key drivers for increasing retention and focusing the communication to the "right" customers. This paper addressed the difficulties facing the healthcare industry in constructing a time-series data mart and developed a retention predictive model that can derive retention scores for each customer. Top factors affecting retention were identified through the modeling process; and some of their applications were discussed in this paper. INTRODUCTION The health insurance industry faces more competition than ever. The heated competition provides consumers with more choices, i.e., the acquisition cost increased and customer loyalty decreased. It costs at least 6-10 times more to acquire a new customer than it does to retain an existing one (Pogol, 2007), and acquisition costs are a large portion of an insurance company's administrative cost. Therefore, customer retention becomes one of the key factors in sustaining business growth. One of the benefits of a retention study is a better understanding of the customer, which can lead to improved customer service. The knowledge gained in this effort can therefore be applied in many areas of the company, and deserves the company's full attention. Given today's economic pressure, companies have limited resources to devote to every customer from every perspective. This requires the company to focus on what matters most in order to maximize its return on investment. The company is challenged to identify key factors associated with decreased retention and also identify the "right" customers with which to communicate. Fortunately, customer retention predictive modeling can serve this purpose. Much research has been completed on using data mining techniques to classify or predict a customer' likelihood to churn (Ngai, et al., 2009). Many data mining techniques are studied, for example, clustering (Bose et al., 2009), decision tree (Lemmens, 2006), neural network, random forest (Xie, et al, 2009), survival analysis (Lu, 2002), generic algorithm (Pendharkar, 2009), and SVM (Archaux, et al, 2004). However, many of them are for the purpose of research, and provided little guidance on "real-world" application. This paper serves the needs for such requests. This study collected comprehensive customer information from four sources, and built a one-row-per-member-permonth data mart containing 643 attributes. Customer segmentation through supervised clustering was performed in the first stage, and classified the total study population into two clusters. Logistic regression models were then developed for each of the clusters using SAS/STAT and SAS/Enterprise Miner 5.3. Several questions were answered in this paper, such as, how to construct data mart that meets data mining requirement in spite of the data complexity and integrity problems; how to identify key drivers to retention, what's their relative importance, and how to apply the predictive model to your business. DATA STRUCTURE AND DESCRIPTION Before any data mining effort can begin, data must be extracted and prepared in a way to maximize the value of the available data and improve the results of complex statistical analysis. The goal is to bring together all of the disparate data in an organization and transform it into meaningful, useful information. Although data construction can consume more than 50 percent of the data mining work effort, little has been published on how best to approach this task. Many of today’s corporations have built vast data warehouses with hundreds of highly normalized tables that can make a simple task of extracting actionable information difficult and, often, cost-prohibitive. The effect of distorted or missing data can have an adverse effect on performance and introduce bias into the investigation. Utilizing the data that is collected to maintain day-to-day business in a way that helps us learn more about our customers is the best tool we have to increase sales, decrease attrition, improve internal processes, reduce call volumes, and to better meet our customer’s ever-changing needs. An enhanced database structure that moves away from the traditional view of collecting data to a more focused view of accumulating facts can vastly improve data mining results. These two tasks are very different. In the healthcare