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Importance-Performance and Segmentation:
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1996
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Marketing AnalyticsCustomer SatisfactionEngineeringBusiness AnalyticsUser SegmentationImage ClassificationImage AnalysisData ScienceData MiningPattern RecognitionManagementMarket SegmentationQuantitative ManagementMachine VisionService DesignUser DisplacementComputer ScienceMedical Image ComputingMarketingComputer VisionUser SegmentsClient SegmentationInteractive MarketingBusinessDecision ScienceImage Segmentation
This paper examines how optimizing resource allocation is influenced by the existence of user segments with different attitudes. In particular, it concentrates on how the attributes of a service or destination area, their importance to clients, and the clientsÆ perceptions of performance can be used to incrementally guide decisions toward more optimal resource allocation. Results are based on simulating behavior at a hypothetical day use area by four user groups. Findings highlight the need for client segmentation prior to importance-performance (IP) analysis. IP analyses without segmentation are likely to result in user displacement of some segments giving a false impression of valid decisions. It is argued that even with improved analysis methods, the ad hoc model behind IP makes its use risky. As continuing rapid development of decision modeling allows, IP should be replaced with decision models that allow a more solid foundation for decisions in economic, social and statistical theory.