Instant Customer Base Analysis: Managerial Heuristics Often “Get It Right”

Abstract

Recently, academics have shown interest and enthusiasm in the development and implementation of stochastic customer base analysis models, such as the Pareto/NBD model and the BG/NBD model. Using the information these models provide, customer managers should be able to (1) distinguish active customers from inactive customers, (2) generate transaction forecasts for individual customers and determine future best customers, and (3) predict the purchase volume of the entire customer base. However, there is also a growing frustration among academics insofar as these models have not found their way into wide managerial application. To present arguments in favor of or against the use of these models in practice, the chapter compares the quality of these models when applied to managerial decision making with the simple heuristics that firms typically use. The chapter finds that the simple heuristics perform at least as well as the stochastic models with regard to all managerially relevant areas, except for predictions regarding future purchases at the overall customer base level. The chapter concludes that in their current state, stochastic customer base analysis models should be implemented in managerial practice with much care. Furthermore, it identify areas for improvement to make these models managerially more useful.