With the advent of big data era, instead of using demographic variables, such as gender, age, area, to analyze customers’ behavior, customers’ transaction history are used most frequently. Nowadays, many companies have huge amount of customers’ transaction data. In order to understand the behavior of customers, RFM (recency, frequency, and monetary) model is the most common method to use. RFM model is able to simply describe the mode of consuming behavior and find the high-valued customers so that the companies can make the right decisions. However, recent years more and more scholars proposed different method to modify the RFM model. For example, there is a new idea, called RFMC model, using “Clumpiness” to research the customers’ behavior. Therefore, the research in this paper used the RFMC model and a new idea to calculate the “Clumpiness”. Moreover, there is a comparison between this new idea and the other method in calculating “Clumpiness”. Finally, the concept of “Run” is cited to predict the next behavior of the customers.