Calculating Customer Lifetime Value (CLV) is a key measure in Customer Relationship Management (CRM) studies. Using this measurement, marketing budget can allocate perfectly. An important issue in this area is to increase the accuracy of calculations and considering the possibility of changing customers’ behavior. In this paper, we tried to show possibility of changing the behavior of customers. For this purpose, we used Isaco (Iran Khodro Spare parts and After Sales Service Corporation) four years transaction dataset. The customer lifetime value is calculated using Markove chain approach. Results show that considering all time period data to estimate only one transition matrix with fixed arrays don’t reflect the real world situation properly. In addition, aggregating all data to estimate the transition matrix will prevent detecting and studying possible changes of customers’ behavior and its potential reasons.


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