Spatial multicriteria customer satisfaction analysis: the case of the single-store retailer

Abstract

Customer Satisfaction (CS) may be modelled as a multicriteria decision problem consisting of singular events leading to an overall outcome. Measuring CS contributes to better understanding customer preferences and further identify potential market opportunities. The MUlticriteria Satisfaction Analysis (MUSA) method is the optimal ordinal regression-based approach for assessing a set of collective satisfaction functions in a way that is consistent with the global satisfaction criterion and aligned with customers judgements. The implementation of the MUSA method provides estimations of the global customer demandingness, action and improvement diagrams and indices. Due to their capabilities in handling spatially referenced data Geographic Information Systems are a primary component in the direction of geomarketing approaches implementation in real world case studies. In that manner spatially related datasets, spatial analysis tools and marketing analysis methods integration, assist local estimations of CS dimension metrics. The paper presents a framework that enables local implementation of multicriteria-based CS analysis which allows deeper interpretation of results based on the local characteristics of the examined area extending the results interpretation capabilities of non-spatial techniques. It extends MUSA to the spatial context with the clear aim of estimating local satisfaction mapping, which allows for informed managerial decisions. The significant advantage is that it accepts as input answers from a simple questionnaire and generates map layers of CS dimensions. Finally, a case study is illustrated to investigate customer behavior in a spatial manner.