Approaching the cold start problem in customer relationship management through lifetime value models

Abstract

Companies are often interested in assessing the future profitability of their customers. This is especially challenging for clients that the company recently acquired, given the small amount of information available. In this thesis, we investigate a different way of approaching this problem, using the Pareto/NBD and Gamma/Gamma models. These allow us to establish statistical descriptions of the purchase rates, the length of the relation with a client and the average value of their purchases. In the end with these models we will create a binary classifier that, given the purchase history of a group of clients acquired at a certain time will tell us which are expected to be profitable in the future, which will present extremely high specificity and acceptable sensibility.