A novel approach to conduct the importance-satisfaction analysis for acquiring typical user groups in business-intelligence systems

Business intelligence (BI) is a powerful tool to conduct causality analysis and corporate diagnoses since it provides a data-driven approach to link firms' strategic goals into tactical policies and operational actions. Specifically, BI consists of a series of architectures and techniques like database, data warehousing, and data mining that transform raw data into useful information to provide decision supports. In reality, typical BI user groups involve financial analysts, marketing planners, general managers, field staffs, upstream suppliers, and downstream customers. Inspired by the concept of STP (segmentation-target-positioning) and product family design, BI systems need to be customized to satisfy diverse user groups and tailored to a firm for solving complicated but specific business problems. Consequently, a novel framework is proposed to fulfill the following goals: (1) incorporating user preferences to identify key design features that best fit BI's main segments for achieving customer acquisition, (2) transforming user perceptions into quantitative degrees of satisfaction for accomplishing customer retention, and (3) conducting the importance-satisfaction analysis to generate managerial insights for developing next-generation BI systems.