Transaction Attributes and Customer Valuation

• Activity Number: 228
• Type: Invited
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Marketing
• Author(s): Michael Braun* and David Schweidel and Eli Stein
• Companies: Southern Methodist University and Emory University and Harvard University
• Keywords: customer retention ; probability models ; marketing ROI ; survival models ; empirical Bayes ; probability

Abstract

The return on investment from a marketing intervention is the resulting increase in the present value of expected future transactions. Under a latent attrition framework, one could estimate this value by manipulating the levels of a set of nonstationary covariates. We propose such a model that incorporates transaction-specific attributes, maintains standard assumptions of unobserved heterogeneity, and allows for fast and easy implementation. We demonstrate how firms can approximate an upper bound on the appropriate amount to invest in retaining a customer, and find that this amount depends on both the recency and frequency of past customer purchases. Using data from a B2B service provider as our empirical application, we apply our model to estimate the revenue lost by the service provider when it fails to deliver a customer's requested level of service. We also show that the lost revenue is larger than the corresponding expected gain from exceeding a customer's requested level of service.