An Improved Customer Relationship Management Model for Business-to-Business E-commerce Using Genetic-Based Data Mining Process

Customer Relationship Management (CRM) empowers the employees and takes customers' satisfaction to a higher level. However, CRM faces some challenges in Business-to-Business (B2B) e-commerce because CRM data are rarely analyzed across market segments or customer categories and customer-firm relationship is also complex. Therefore, making appropriate decisions in CRM model is difficult. This paper presents a combined model for B2B CRM using Genetic algorithm and Data Mining Techniques to improve decision making. The model classifies the customers into Repeat and Shop-and-Go customers. A modified Data mining - C5.0 was used for customer classification and Genetic algorithm was used to optimize the rules generated by the decision tree algorithm. The results showed that the proposed model effectively allocates resources to the most profitable group of customers. The proposed model has higher accuracy and outperforms others when compared to the conversional C5.0, k-means, and Support Vector Machine (SVM) algorithm.