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

Jiakui Zhao, Wei Yang, Wei Zhao, Jian Liu, Yaozong Lu, Yuxi Liu, Hongwang Fang, Hong Ouyang
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引用次数: 1

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.
基于视觉聚类算法的电力客服人员高效特征描述
有效、高效地描述不同员工的特征是实现客服中心科学、细致管理的关键问题,因为这些特征是制定调度策略、安置策略、绩效评价标准等决策工作所必需的。本文通过引入视觉聚类算法,根据电力客服人员的工作量、工作质量和工作效率,将电力客服人员分为质量型、效率型、中等型和问题型四种类型。聚类结果有助于客观地制定调度策略、布局策略和绩效评价标准。目前已定期应用视觉聚类算法对电力客服人员进行分组,以便及时调整相关策略和标准。本文方法在国家电网客户服务中心的成功应用表明了该方法的有效性和高效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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