In marketing, customer lifetime value (LTV, sometimes called CLV or CLTV) is a prediction of the net cash flow attributed to the entire future relationship with a customer. The prediction model can have varying levels of sophistication and accuracy, ranging from heuristic to the use of complex machine learning techniques. LTV in a non-contractual setting is widely accepted to be more difficult than in a contractual setting, in which the churn rate can be simplified as a constant. - As the world's largest concert promoter, Ticketmaster is focused on connecting live events to millions of our fans in a non-contractual and discrete setting. We adopted a paradigm called RFM (Recency, Frequency, Monetary) to make predictions of fans' two-year lifetime value to help make decisions including, for example - new product feature launch, SEM bidding automation, overall budgeting and essentially implement a winning strategy driven by customer lifetime value.- In this talk we will discuss RFM and other probabilistic models we used and how the results of the analysis helped drive business decisions. We will provide an overview of: (1) Customer lifetime value using RFM (2) Probabilistic Models: Bayesian Models, Beta-Geometric/Beta-Binomial Model (BG/BB) (3) A case study: how to use LTV for a new feature launch