Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Data envelopment analysis‐discriminant analysis (DEA‐DA) has been used for predicting cluster membership of decision‐making units (DMUs). One of the possible applications of DEA‐DA is in the marketing research area. This paper uses cluster analysis to cluster customers into two clusters: Gold and Lead. Then, to predict cluster membership of new customers, DEA‐DA is applied. In DEA‐DA, an arbitrary parameter imposing a small gap between two clusters (η) is incorporated. It is shown that different η leads to different prediction accuracy levels since an unsuitable value for η leads to an incorrect classification of DMUs. We show that even the data set with no overlap between two clusters can be misclassified. This paper proposes a new DEA‐DA model to tackle this issue. The aim of this paper is to illustrate some computational difficulties in previous DEA‐DA approaches and then to propose a new DEA‐DA model to overcome the difficulties. A case study demonstrates the efficacy of the proposed model.