Mining Customer Behavior Knowledge to Develop Analytical Expert System for Beverage Marketing

Consumer relationship management (CRM) requires detailed information and business knowledge for successful adoption. Data mining techniques are widely used in business administration, the financial industry, and marketing. Mining techniques provide decision administration reference for enterprises by integrating useful information and discovering new information from different perspectives. In this study, we applied data mining technique and statistics and utilized questionnaires in CRM to analyze customer behavior. The Chinese tea market is famous worldwide, customizing the tea service is a special trend in chain stores, and customer behavior analysis is essential for the tea market. This study aims to develop a customer behavior analysis expert system (CBAES) in which a decision tree is used to identify relevant knowledge and personalize merchandise based on association rule framework of consumer behavior analysis in chain store beverage marketing. Identifying consumers’ preferences and providing optimal purchase strategy using this approach is a helpful characteristic of customers and facilitates marketing strategy development. Keywords—Consumer relationship management, expert system, decision tree algorithm, marketing