The CRM handbook: a business guide to customer relationship management
The CRM handbook: a business guide to customer relationship management
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Managing customers as investments the strategic value of customers in the long run
Managing customers as investments the strategic value of customers in the long run
A customer satisfaction inventory model for supply chain integration
Expert Systems with Applications: An International Journal
A dynamic decision support system to predict the value of customer for new product development
Decision Support Systems
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Customer retention as part of customer relationship management (CRM) is increasingly important in today's dynamics competitive environment, as customer retention cost is lower than acquisition cost. In CRM, it is beneficial for a firm to estimate expected customer lifetime value (CLV) at the individual level rather than taking an average profit from all customers. This helps a firm to determine its future efforts for remarketing its customers in a more accurate manner. The Markov Chains Model (MCM) as a probabilistic model is appropriate for modeling customer relationships, and estimating CLV. Based on the theory of the Markov Chains Model (MCM) for CRM, we build up a repurchasing model for modeling the customer retention situation in CRM using a system dynamics approach, which results in the probability outcomes for estimating CLV in MCM afterwards. The aim of this paper is to introduce the modeling of the repurchasing Markov Chains model, and use its simulation results to calculate CLV for better CRM solutions.