Abstract
Consumer satisfaction (CS) analysis has been a major issue in business organizations over the years. It may be modeled as a multicriteria decision problem, although different alternative measurement approaches have been developed. The MUSA + method extends the typical MUSA method which is an ordinal regression method that analyzes CS. It allows managers to conduct a benchmarking analysis among different stores and provides action diagrams to assist in gap analysis. The MUSA + method to the spatial context provides geolocated presentation of the derived results and allows their interpretation based on the local characteristics of the market areas. Geographic Information Systems are well known tools that facilitate capabilities for obtaining, processing, and visualizing spatial related data through maps. In that manner, the term ‘geomarketing’ reflects the combination of the multicriteria consumers analysis and GIS. This paper presents a framework that generates maps in relation to the multi-store CS analysis dimensions. Thus, the analysis is extended to the spatial context aiming to expand the MUSA + analysis capabilities with the power of results mapping generation. The proposed framework is supported by a tool developed in GIS environment to assist its computational part. Initially the process estimates the service areas for every store to establish the spatial boundaries for each one of them. Then the MUSA + method is implemented separately for each consumer group located in a specific service area. The computational tool developed in GIS environment generates mappings of the MUSA + method diagrams. The proposed framework combines MUSA + capabilities to generate CS related indices and GIS capabilities to assist spatial analysis allowing customization of the marketing strategy for multi-store enterprises. In conclusion a case study is illustrated to present the prementioned framework into real world data.
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Saridou, A.S., Vavatsikos, A.P. & Grigoroudis, E. Multi-store consumer satisfaction benchmarking using spatial multiple criteria decision analysis. Oper Res Int J 24, 30 (2024). https://doi.org/10.1007/s12351-024-00818-9
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DOI: https://doi.org/10.1007/s12351-024-00818-9