Modeling Repeat Purchases in the Internet when RFM Captures Past Influence of Marketing

Predicting online customer repeat purchase behavior by accounting for the marketing-mix plays an important role in a variety of empirical studies regarding individual customer relationship management. A number of sophisticated models have been developed for different forecasting purposes based on a – mostly linear – combination of purchase history, so called Recency-Frequency-Monetary Value (RFM)-variables and marketing variables. However, these studies focus on a high predictive validity rather than ensuring that their proposed models capture the original effects of marketing activities. Thus, they ignore an explicit relationship between the purchase history and marketing which leads to biased estimates in case these variables are correlated. This study develops a modeling framework for the prediction of repeat purchases that adequately combines purchase history data and marketing-mix information in order to determine the original impact of marketing. More specifically, we postulate that RFM already captures the effects of past marketing activities and the original marketing impact is represented by temporal changes from the purchase process. Our analysis highlights and confirms the importance of adequately modeling the relationship between RFM and marketing. In addition, the results show superiority of the proposed model compared to a model with a linear combination of RFM and marketing variables.