The present study attempts to establish a new framework to speculate customer lifetime value by a stochastic approach. In this research the customer lifetime value is considered as combination of customer’ s present and future value. At first step of our desired model, it is essential to define customer groups based on their behavior similarities, and in second step a mechanism to count current value, and at the end estimate the future value of customers. Having a structure in modeling customer churn is also important to have complete customer lifetime value computation. Clustering as one of data mining techniques is practiced to help us analyze the different groups of customers, and extract mathematical model to count the customers value. Thereafter by using markov chain model as stochastic approach, we predict future behavior of the customer and as a result, estimate future value of different customers. The proposed model is demonstrated by the customer demographic data and historical transaction data in a composite manufacturing company in Iran.