An Application of Fuzzy Clustering to Customer Portfolio Analysis in Automotive Industry

Having achieved an optimized customer portfolio has been of significant importance for companies. The literature provides several portfolio models and vast majority of them are in matrix form where several descriptors are used as dimensions of the matrix. These dimensions are characterized in ambiguity and require specific methods to tackle with it. The aim of this paper is to utilize fuzzy clustering in customer portfolio analysis to reduce this uncertainty and to make a comparison with a traditional customer portfolio model. A dataset of 130 customers of an automotive supplier in Turkey is used to perform the analyses and the results are compared with a conventional customer portfolio matrix. By making use of substantiality and balance of portfolio parameters, a qualitative and quantitative assessment of categorization generated by both approaches are evaluated. The use of fuzzy clustering gives more substantial clusters and a more balanced customer portfolio compared to the traditional matrix form of portfolio. Marketing managers can understand their overall customer portfolio better and reduce the effect of descriptive indicators via benefiting the fuzzy clustering results.