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Customer behavior analysis is essential for any business. The business leaders require characterizing the behavior of customers to build long-term profitable customers. It can be done by grouping or segmenting the customers according to their characteristics. The customers grouped together are described by rules to portray their behavior. These rules can be used by the marketing managers to predict the behavior of new customer by comparing with the characteristics of existing customer and to personalize the service. This paper focuses the rough set approach for customer segmentation and rule generation for customer behavior analysis. Real customer data have been collected from four different enterprises. The experimental results prove that our proposed clustering and rule induction algorithm is more efficient for customer behavior analysis.