The present study attempts to establish a framework for computing customer lifetime values for a company in the auto repair and maintenance industry. The customer lifetime value defined in this study consists of the current and future values of a customer, which involve an estimation of lifetime length, future purchasing behavior and the profit associated with each behavior of the customer. The proposed framework contains three groups of techniques to obtain these estimates from historical customer transactions. The first group includes a logistic regression model and a decision tree model to estimate the churn probability of a customer and to, further, predict the lifetime length of the customer. The second group comprises a regression analysis to identify the critical variables that affect a customer’s purchasing behavior, and a Markov chain to model the transition probabilities of behavior change. Finally, the third group contains two neural networks to predict the profits contributed by a customer under various purchasing behaviors. The proposed framework is demonstrated with the historical customer transactions of an auto repair and maintenance company in Taiwan.