Predicting customer behavior with Activation Loyalty per Period. From RFM to RFMAP

Abstract

Objective:Identify a new model of predicting customer behavior based on new variables that can be used by marketing management and adapted to their business planning. Methodology: New model has been used, with the definition of new calculation systems of the traditional variables R, Recency, F, Frequency, and M, monetary value, (RFM), related to the business periods. Besides, activation in each period P becomes a key variable for constructing the purchase cohorts of customers and identifying their potential. A new variable, Activation Loyalty, is recognized as a good proxy of the likelihood of future customer purchases. The model builds a weighting through a multiple regression analysis obtaining β for each variable, including the periods of activation, presenting the relative effect of the variables, and the best global explanation of the model. Results: This new model, RFMAP, which includes Activation Periods and Activation Loyalty, presents a higher prediction accuracy and improvements over traditional models with a clear impact, useful and manageable lines of segmentation, and prioritization for marketing management in CRM systems. Limitations: The main limitation of this model consists that it is based on data of only one company, and it should show the value in other sectors and give a full insight through its transversal application. Practical implications: The involved advantages demonstrated better predictability and usefulness to decision-makers, not only to determine the best customers but also with lapsed ones. It gives a meaningful explanation of differences in customer behavior, which are present in the data and are being reflected in the model. Also, it provides a prescriptive prioritization of variables to be managed in the marketing plan and how to be implemented.