| Customer relationship management facilitates efficient management of the relationship between a firm and its customers. The notion of customer value helps quantify these relationships so that they can be measured and managed by the firm. A customer's survival probability and contribution forms two components of customer value. However, there are uncertainties associated with customer behavior that impact their worth to a firm. Specifically, these uncertainties directly affect customer retention and, therefore, degrades the customer worth, if they are unaccounted for. In this thesis, I use a Competing Risk customer valuation framework that is more robust in terms of withstanding these uncertainties.;First, I compute the survival probabilities of customers using a Random Survival Forest method that incorporates the competing risk methodology to account for uncertainties in customer churn. This nonparametric estimation technique overcomes the distributional limitations of extant traditional parametric or semi-parametric methods. As a result, I demonstrate how the underlying factors that determine a customer's survival, contribute to the competing causes of churn in terms of their importance. This study also demonstrates how the risks due to a particular competing cause evolve over time.;Next, I compute the customer's margin using a multiple regression model that incorporates customer specific information and the effect of time. The margin and survival propensities are then used to compute customer lifetime values. My contribution in this area advances existing works in customer valuation under contractual business settings in the presence of multiple causes of churn. I analyze the computed lifetime values using various data mining techniques, such as customer segmentation, and compare them to traditional non-probabilistic methods.;Finally, I address the firms decision making process during allocation of promotional offers by using the estimated lifetime values and risks from the Random Survival Forest method. The optimization framework adapts to the changing customer behavior and ensures the high value customers with high risk of churn are targeted for making a promotional offer at a particular time, while also ensuring the cost of carrying out the entire marketing campaign is minimized. |
