Development of an intelligent customer relationship decision support system to achieve total customer satisfaction

Abstract

In the fierce business environment, customer demands continue to change rapidly. In order to stay ahead of competitors, business processes and the related supply chain structure have to be adapted quickly and responsively to the changes. Manufacturers face the challenge of offering customers high quality products and shorter delivery times while remaining cost-effective. Thus, modern enterprises need to measure how resources are utilised when achieving the highest customer satisfaction in order to determine the effectiveness of their customer relationship strategies. One of the ways to do this is to make use of the operational data in existing application systems, such as enterprise resources planning. However, various functional departments have their own information systems. The focus of those systems is on transaction processing. They fail to deliver anticipated benefits in managing customer relationships. Therefore, attention should be paid to capturing distributed relevant data to support decision making when formulating customer relationship strategies. In this research project, a generic system called the intelligent customer relationship decision support system (ICRS) is proposed. The proposed system captures, integrates and analysis relevant customer data from different locations within an enterprise to provide users with decision support functionality. This system integrates the data warehouse concept with online analytical processing (OLAP) and artificial neural networks to form an OLAP-neural engine to predict customer satisfaction. Then, the customised segmentation engine and the decision support platform are used to aid in formulating customer relationship strategies. The proposed system aims to establish a cost-effective strategic customer relationship management solution for achieving total customer satisfaction. To validate the feasibility of the proposed system, ICRS was applied in a local die-casting manufacturer. It was found that the overall performance in customer service efficiency and cost reduction improved. Customer satisfaction increased significantly. The major contribution of this research is to develop a generic decision support system infrastructure in the customer relationship management area in relating disperse customer data from raw records to the ERP systems. Integration of customer information and transaction data facilitates fast and appropriate decision making on customer relationship strategies, which is an important element of overall business strategies. The deliverables of this research increase awareness of the benefits of incorporating different types of emerging technologies in any future marketing intelligence system.