融合DEA与聚类分析的保险电商绩效分类与优化策略研究

Study on Performance Classification and Optimization Strategy of Insurance E-Commerce Integrating DEA and Cluster Analysis

摘要: 保险电商作为保险业信息化的产物，对资源配置、保险服务模式及经营效率具有新的管理要求，提升公司综合绩效已被证明是增强公司竞争力的重要手段。本文引入基于数据包络分析(DEA)和聚类分析的绩效评估方法，构建一套“评估–分类–策略”的一体化分析框架。利用DEA方法分别计算每个决策单元的相对效率值，利用聚类分析方法将企业分入相应的类别，即形成“评–分–策”的综合体系。在此基础上，本文提出了根据不同的业绩水平制定相应的激励方案，包括构建智能化数据分析系统、重置业务模式与完善动态反馈机制等。研究表明，该方法不仅提升了绩效识别的准确性与分层能力，更有利于制定针对性的管理策略，为保险电商行业实现高质量发展提供了理论参考和技术支撑。

Abstract: As a product of the informatization of the insurance industry, e-commerce in insurance has new management requirements for resource allocation, insurance service models, and operational efficiency. Enhancing overall company performance has been proven to be an important means of boosting competitiveness. This paper introduces a performance evaluation method based on Data Envelopment Analysis (DEA) and Cluster Analysis, constructing an integrated analysis framework of “Evaluation-Classification-Strategy”. Using the DEA method, the relative efficiency values of each decision-making unit are calculated separately, and cluster analysis is used to categorize companies into corresponding classes, forming a comprehensive system of “Evaluation-Classification-Strategy”. On this basis, the paper proposes formulating incentive schemes according to different performance levels, including building intelligent data analysis systems, resetting business models, and improving dynamic feedback mechanisms. The study shows that this method not only enhances the accuracy and stratification capabilities of performance identification but also facilitates the formulation of targeted management strategies, providing theoretical references and technical support for the high-quality development of the e-commerce in insurance industry.