Customer Relationship Management: from Conversion to Churn to Winback

Abstract

With the grant of a big CRM dataset from a large media company, this dissertation examines four different categories of factors that could impact three stages of customer relationship management, namely customer acquisition, retention, and winback of lost customers. Specifically, with the aid of machine learning method of random forests and text mining technique, this study identify among the factors of customer heterogeneity (e.g. in usage of self-care service channels, duration of service, responsiveness to marketing actions), firm's marketing initiatives (e.g. the volume of the marketing communications, the depth of the promotion, the different communication channels they use, and the marketing penetration in different geographical areas), customer self-reported deactivation reasons, as well as the call centers notes in text form, which factors play bigger roles than others during each of the three stages of CRM. Furthermore, the authors also examine how these factors evolve throughout these three stages of CRM in terms of their effects on shaping customers' decision making of whether to convert to paid customer, to churn, or to reactivate their service with the company. The findings help managers better allocate their resources in the processes of acquiring, retaining and winning back customers.