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.

Purpose This study aims to identify a new model of relative customer satisfaction translated into share of purchases (SOP) with the best-related metrics. Design/methodology/approach This study uses an online customer satisfaction survey to clients of a firm and with a comparative valuation with current competitors by customer. The model builds a weighting through a multiple regression analysis, obtaining β for each variable by relating the variables to the SOP, presenting the relative effect of the variables and the best global explanation of the model. Findings This new model has good prediction accuracy and shows a clear impact of different relative satisfaction indicators and, to a minor degree, business and relationship characteristics. Research limitations/implications The main limitation of this model is that it is based on data from only one company, but it should have value in other sectors and provide full insight through its transversal application. Originality/value The involved advantages demonstrated better predictability and usefulness to decision-makers and determined how the improvements in customer satisfaction translate into business growth. The study shows that the relative evaluation of satisfaction carries different meanings for customers, while all of them are better than absolute satisfaction. It includes a more understandable indicator than other prior relative indicators, the difference in satisfaction and is more effective. Additionally, it guides how to take advantage of the knowledge of relative customer satisfaction before competitors and demonstrates the courses of action with the potential best results.