Data mining for customer service support
Information and Management
Knowledge management and data mining for marketing
Decision Support Systems - Knowledge management support of decision making
Knowledge refinement based on the discovery of unexpected patterns in data mining
Decision Support Systems - Special issue: Formal modeling and electronic commerce
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
The new k-windows algorithm for improving the k-means clustering algorithm
Journal of Complexity
Using information retrieval techniques for supporting data mining
Data & Knowledge Engineering
Mining product maps for new product development
Expert Systems with Applications: An International Journal
Mining customer knowledge for tourism new product development and customer relationship management
Expert Systems with Applications: An International Journal
Backcalculation of pavement layer moduli and Poisson's ratio using data mining
Expert Systems with Applications: An International Journal
Mining customer knowledge to implement online shopping and home delivery for hypermarkets
Expert Systems with Applications: An International Journal
Mining customer knowledge for direct selling and marketing
Expert Systems with Applications: An International Journal
Mining customer knowledge for exploring online group buying behavior
Expert Systems with Applications: An International Journal
Mining shopping behavior in the Taiwan luxury products market
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Demand chain management (DCM) can be defined as ''extending the view of operations from a single business unit or a company to the whole chain. Essentially, demand chain management focuses not only on generating drawing power from customers to purchase merchandises on the supply chain; but also on exploring satisfaction, participation, and involvement from customers in order for enterprises to understand customer needs and wants. Thus, customers have changed their position in the demand chain to assume a leading role in bringing more benefit for enterprises. This article investigates what functionalities best fit the consumers' needs and wants for life insurance products by extracting specific knowledge patterns and rules from consumers and their demand chain. By doing so, this paper uses the a priori algorithm and clustering analysis as methodologies for data mining. Knowledge extraction from data mining results is illustrated as market segments and demand chain analysis on life insurance market in Taiwan in order to propose suggestions and solutions to the insurance firms for new product development and marketing.