RFM stands for Recency, Frequency and Monetary value. RFM analysis is a marketing technique used for analyzing customer behavior such as how recently a customer has purchased (recency), how often the customer purchases (frequency), and how much the customer spends (monetary). It is a useful method to improve customer segmentation by dividing customers into various groups for future personalization services and to identify customers who are more likely to respond to promotions. In recent years, data mining applications based on RFM concepts have also been proposed for different areas such as for the computer security (Kim et al., 2010), for automobile industry (Chan, 2008) and for the electronics industry (Chiu et al., 2009). Research cases of data mining with RFM variables include different data mining techniques such as neural network and decision tree (Olson et al., 2009), rough set theory (Cheng & Chen, 2009), self organizing map (Li et al., 2008), CHAID (McCarty and Hastak, 2007), genetic algorithm (Chan, 2008) and sequential pattern mining (Chen et al., 2009; Liu et al., 2009). Integration of RFM analysis and data mining techniques provides useful information for current and new customers. Clustering based on RFM attributes provides more behavioral knowledge of customers’ actual marketing levels than other cluster analyses. Classification rules discovered from customer demographic variables and RFM variables provides useful knowledge for managers to predict future customer behavior such as how recently the customer will probably purchase, how often the customer will purchase, and what will the value of his/her purchases. Association rule mining based on RFM measures analyzes the relationships of product properties and customers’ contributions / loyalties to provide a better recommendation to satisfy customers’ needs. This chapter presents incorporating RFM analysis into data mining techniques to provide market intelligence. It proposes a new three-step approach which uses RFM analysis in data mining tasks, including clustering, classification and association rule mining, to provide market intelligence and to assist market managers in developing better marketing strategies. In our model, (i) once clustering task is used to find customer segments with similar RFM values, (ii) then, using customer segments and customer demographic variables, classification rules are discovered to predict future customer behaviors, (iii) finally; association rule mining is carried out for product recommendation. The proposed model depends on the sentence "the best predictor of future customer behavior is past customer behavior". (Swearingen, 2009) The purpose of this study is to provide better product recommendations than simple recommendations, by considering several parameters together: customer’s segment, the