Effective and Efficient Feature Description of the Electric Customer Service Staffs Based on the Visual Clustering Algorithm

Abstract

Effective and efficient description of the features of different staffs is the key problem to achieve scientific and meticulous management in customer service centers, because the features are essential for decision-making works, e.g., the formulation of the scheduling strategy, the placement strategy and the performance evaluation standard. In this paper, by introducing the visual clustering algorithm, the electric customer service staffs are divided into four types, i.e., the quality type, the efficiency type, the medium type and the problem type, on the basis of the workload, work quality and work efficiency of the staffs. The clustering results are helpful for the formulation of the scheduling strategy, the placement strategy and the performance evaluation standard objectively. The visual clustering algorithm is now regularly applied to divide electric customer service staffs into different groups so that related strategies and standards may be tuned timely and properly. The successful application of our proposed method in State Grid Customer Service Center shows the effectiveness and the efficiency of the proposed visual clustering algorithm.