Development of Modified Ripper Algorithm to Predict Customer Churn

Abstract

Technologies such as data warehousing, data mining, and campaign management software have made Customer
Relationship Management (CRM) a new area where firms can gain a competitive advantage. Particularly through data mining
a process of extracting hidden predictive information from large databases, organisations can identify their valuable
customers, predict future behaviors, and enable firms to make proactive, knowledge-driven decisions. Data Mining along with
Customer Relationship Management plays a vital role in today’s business environment. Customer churn, a process of
retaining customer is a major issue. Prevention of customer churn is a major problem because acquiring new customer is
more expensive than holding existing customers. In order to prevent churn several data mining techniques have been
proposed. One among such method is solving class imbalance which has not received much attention in the context of data
mining. This paper describes Customer Relationship Management (CRM), customer churn and class imbalance and proposes
a methodology for preventing customer churn through class imbalance.