Determining the appropriate amount of data for classifying consumers for direct marketing purposes

This article examines the impact of using incremental amounts of purchasing data on the ability to classify consumers in consumer packaged goods categories for direct marketing purposes. Building on the work of Rossi, McCulloch, and Allenby (1996), who focused on the impact of three information sets—(a) demographics only, (b) demographics and one purchase made by a consumer, and (c) demographics plus an entire purchasing history of a consumer—we examine the impact of each additional purchase, starting with no purchasing information (i.e., demographics only) through 20 purchases. Using two different classification models, a Multinomial Logit model and an Artificial Neural Network model, we examine the sensitivity of classification accuracy to each additional purchase. We use these results in a profitability analysis of a hypothetical direct marketing campaign to determine the optimal number of purchases to use for classification in the category studied. The findings suggest an optimal number of purchasing observations exists for classification and targeting purposes and this optimal number falls between one purchase and a “history” of purchases as studied by Rossi et al. Our findings illustrate the importance of conducting a sensitivity analysis to identify the optimal amount of purchasing data to use when classifying consumers for the purpose of a direct marketing campaign.