Customer Lifetime Value, CLV, is a popular measure to understand the future profitability of customers
to allocate resources in more efficient ways to keep the company alive during difficult economic
situations. We use machine learning tools to predict the expected revenue from each customer during
one year of his/her relationship with the institution as the CLV of the customer. The approach is
implemented on two datasets from two international financial institutions. Different feature engineering techniques were applied to improve the prediction power of the model. We used two stage or three stage prediction models. In the second phase, we train a reinforcement learning algorithm based on the history of marketing activities and the CLV as the state of customers to determine the optimum marketing action for customers in each state to maximize their profitability.