Transaction Attributes and Customer Valuation

Abstract

Dynamic customer targeting is a common task for marketers actively managing customer relationships. Such efforts can be guided by insight into the return on investment from marketing interventions, which can be derived as the increase in the present value of a customer's expected future transactions. Using the popular latent attrition framework, one could estimate this value by manipulating the levels of a set of nonstationary covariates. The authors propose such a model that incorporates transaction-specific attributes and maintains standard assumptions of unobserved heterogeneity. They demonstrate how firms can approximate an upper bound on the appropriate amount to invest in retaining a customer and demonstrate that this amount depends on customers’ past purchase activity—namely, the recency and frequency of past customer purchases. Using data from a business-to-business service provider as their empirical application, the authors apply the model to estimate the revenue the service provider loses when it fails to deliver a customer's requested level of service. They also show that the lost revenue is larger than the corresponding expected gain that would result from exceeding a customer's requested level of service. The authors discuss the implications of their findings for marketers in terms of managing customer relationships.