A Data Mining Technique to Find Optimal Customers for Beneficial Customer Relationship Management

Problem statement: Modern companies and organizations are efficiently implementing a CRM strategy for managing a company interactions and relationships with customers. CRM systems have been developed and designed to support the areas of marketing, service process and sales. Many literature studies are available to preserve the customer relationship but small drawbacks occur in the existing methods. One method to maintain the customer relationship is frequency based method i.e., the company will give declination to the customer based on the historical data that is the customers how many times come to that company. These methods are not effective. Because the customers give revenue to that company is less. So the company revenue is affected. Approach: In this study, we propose a data mining and artificial technique to maintain the customer relationship between company and customers. Accomplishing this process, we maintain a historical database and then we use data mining ARM technique to mine the customer information from this database. We then present an artificial intelligence PSO technique to provide an offer to the selected customers. This offer does not affect the company revenues as well as satisfies the customers. This process will make a best relationship between the customers and organization and to satisfy the customers forever with company's rules. Results: he simulation results have shown the performance of apriori algorithm for diverse combination of profit lengths of each customer. The performance of selected customer has been analyzed for five years. Finally the comparison results shows the selected customers are optimal comparing to other customers. Conclusion: The proposed method optimally selects the customers and also avoids the problems in the earliest methods. So, the relationship between customer and company is maintained successfully by using the proposed method.