This paper studies strategies to access customer lifetime value (CLV). Traditionally, heuristics based on recency, frequency and monetary value variables (RFM) are used to determine the best customers. Here, some forms of directly exploring these parameters to predict CLV are compared to an approach based on fitting a stochastic model. The model employed is a composition of a model for the number of transactions along the residual lifetime and a model for the value spent. New evidence is raised on the effect of aggregating transactions monthly. The data analyzed refer to two years of purchases of a group of customers of the same entrance cohort of a fidelity program cadastre of a supermarkets network in Rio de Janeiro. Using the first year to calibrate and the second year to validate the models, good fit of both models to the series of individual data and coherent CLV predictions are obtained.
customer lifetime value; knowledge management; marketing; retail; modeling
