Customer churn may be a critical issue for ban ks. The extant literature on statistical and machine lea rn ing fo r customer churn focuses on the problem of correctly predicting that a customer is about to switch bank, while very rarely consid- ers the problem of generating personalized actions to improve the customer retention rate. However, these decisions are at least a s critical as the correct identification o f customers at risk. The decision o f what actions to d eliver to what customers is normally left to managers who can only rely upon their knowledge. By looking at the scientific literature on CRM and personalization, this research proposes a number of models which can be used to generate marketing ac- tions, and shows how to integrate them into a model embracing both the analytical prediction of customer churn and the genera tion o f reten tion actio ns. The b enefits and risks asso ciated with each a ppro ach a re discu ssed. Th e pap er also describes a case of application of a predictive model of customer churn in a retail bank where the analysts have also generated a set of personalized actions to retain customers by using one of the approaches presented in the paper, namely by adapting a recommender system approach to the retention problem