Survival analysis for customer lifetime value estimate

Traditional marketing metrics such as brand awareness, sales and share are not enough to show a return on marketing investment. Customers are important intangible assets of a company but they are not equally remunerative. Therefore, estimating customer lifetime value (CLV) is becoming increasingly important to identify prospective profitable customers. In particular, thanks to Gupta et al. (2006), using survival analysis approach it is possible to estimate the customer retention rate in term of probability. This article examines the recent literature of a number of implementable customer lifetime value and survival models and proposes a case study (telecommunication sector) about modeling customer lifetime value using survival analysis. With the increasing development of information technology, the gap between products and services becomes less and less. The wealth of customer information and increasingly sophisticated information technology and statistical modeling have led to a revolution in areas such as customer relationship management or CRM (Winer, 2001). This paper represents a useful tool that allows to help management strategy to estimate CLV and the risk of customer churn.