Developing a measure of risk adjusted revenue (RAR) in credit cards market: Implications for customer relationship management

► We develop a model to account for multiple risks in customer lifetime value models. ► We estimate the model using rich data on customers of credit cards. ► We show that affinity and rewards cards programs generate customers that are high on the risk adjusted revenue metric. ► We show that customers acquired through direct mail and Internet are high on the risk adjusted revenue metric. ► We employ the DEA model, bootstrapped DEA model and the SFA model to check the robustness of our findings. Current models of customer lifetime value (CLV) consider the discounted value of profits that a customer generates over an expected lifetime of relationship with the firm. This practice can be misleading in the financial services markets because it ignores the risk posed by the customer (such as delinquency and default). Specifically, in the credit card market, the correlation between revenue and risk is positive. Therefore, firms need to adjust a customer’s profits for the associated risk before developing a measure of customer lifetime value. We propose a new measure, risk adjusted revenue (RAR), that can incorporate multiple sources of risk and demonstrate the usefulness of the proposed measure in correctly assessing the value of a customer in the credit card market. The model can be extended to compute risk adjusted lifetime value (RALTV). We use the RAR metric to understand the effectiveness of different modes of acquisition, and of retention strategies such as affinity cards and reward cards. We find that both reward- and affinity-cardholders generate higher RAR than non-reward and non-affinity cardholders respectively. The ordering of different modes of acquisition with respect to RAR (in decreasing order) is as follows: Internet, direct mail, telesales, and direct selling.