Some key technologies of actualizing customer relationship management(CRM)systems are researched.The customer analysis mining prototype systems on the basis of on-line analytical mining(OLAM)is designed.After transact...Some key technologies of actualizing customer relationship management(CRM)systems are researched.The customer analysis mining prototype systems on the basis of on-line analytical mining(OLAM)is designed.After transaction analysis,the data warehouse of CRM is constructed.The CLV/CL/CC customer division model based on customer lifetime value,customer loyalty and customer credit is emphatically researched.Three parameters of customer division—customer lifetime value,customer loyalty and customer credit—are calculated by corresponding algorithms,which can realize customer divisions effectively and improve the accuracy of distinguishing among customers.The data of product sales are analyzed by the sequence association rules algorithm,the potential rules of the products relevance are discovered,which can provide evidence for supporting decisions such as promotion strategies.The transaction data such as product sales volumes and order lists are analyzed on-line through multi-dimensional and multi-level up-drills,down-drills,and horizontal/longitudinal sections.The customer property factors are analyzed as well.The theory and practice of OLAM and its visualization are further explored.展开更多
基金The National Key Technology R&D Program of China during the 11th Five-Year Plan Period(No.2006BAH02A06)
文摘Some key technologies of actualizing customer relationship management(CRM)systems are researched.The customer analysis mining prototype systems on the basis of on-line analytical mining(OLAM)is designed.After transaction analysis,the data warehouse of CRM is constructed.The CLV/CL/CC customer division model based on customer lifetime value,customer loyalty and customer credit is emphatically researched.Three parameters of customer division—customer lifetime value,customer loyalty and customer credit—are calculated by corresponding algorithms,which can realize customer divisions effectively and improve the accuracy of distinguishing among customers.The data of product sales are analyzed by the sequence association rules algorithm,the potential rules of the products relevance are discovered,which can provide evidence for supporting decisions such as promotion strategies.The transaction data such as product sales volumes and order lists are analyzed on-line through multi-dimensional and multi-level up-drills,down-drills,and horizontal/longitudinal sections.The customer property factors are analyzed as well.The theory and practice of OLAM and its visualization are further explored.
