Optimized Feature Extraction and Actionable Knowledge Discovery for Customer Relationship Management

In today's dynamic marketplace, telecommunication organizations, both private and public, are increasingly leaving antiquated marketing philosophies and strategies to the adoption of more customer-driven initiatives that seek to understand, attract, retain and build intimate long term relationship with profitable customers. This paradigm shift has undauntedly led to the growing interest in Customer Relationship Management(CRM) initiatives that aim at ensuring customer identification and interactions. The urgent market requirement is to identify automated methods that can assist businesses in the complex task of predicting customer churning. The immediate requirement of the market is to have systems that can perform accurate (i) identification of loyal customers (so that companies can offer more services to retain them) (ii) prediction of churners to ensure that only the customers who are planning to switch their service providers are being targeted for retention