Lifetime value modelling

Abstract

Given the increase in popularity of Lifetime Value (LTV), the argument is that the topic will assume an increasingly central role in research and marketing. As such, the decision to assess the state of the field in Lifetime Value Modelling, and outline challenges unique to choice researchers in customer relationship management (CRM). As the research has argued, there are an excess of issues and analytical challenges that remain unresolved. The researcher hopes that this thesis inspires new answers and new approaches to resolve LTV. The scope of this project covers the building of a LTV model through multiple regression. This thesis is exclusively focused on modelling tenure. In this regard, there are a variety of benchmark statistical techniques arising from survival analysis, which could be applied, to tenure modelling. Tenure prediction will be looked at using survival analysis and compared with "crossbreed" data mining techniques that use multiple regression in concurrence with statistical techniques. It will be demonstrated how data mining tools complement the statistical models, and show that their mutual usage overcomes many of the shortcomings of each singular tool set, resulting in LTV models that are both accurate and comprehensible. Bank XYZ is used as an example and is based on a real scenario of one of the Banks of South Africa.