基于顾客需求重要度的电商平台功能与服务优化策略研究

Research on Function and Service Optimization Strategies for E-Commerce Websites Based on the Importance of Customer Demands

摘要: 电商平台提升核心竞争力的关键是提升用户体验，而提升用户体验的关键是优化网站的功能或服务，在这一过程中精准识别顾客需求尤为重要。针对传统方法在电商场景下过于主观、忽视顾客需求关联性等问题，本文提出一种基于链路预测的综合决策方法。首先，利用犹豫模糊DEMATEL计算出顾客需求间的影响，并构建有向带权网络，然后采用加权LeaderRank算法在该网络上进行随机游走，依据顾客需求重要度的量化结果，为电商平台功能改进与服务升级提供了明确的分层策略与资源配置建议。以一个典型购物网站的15项顾客需求为例进行实证分析，结果表明：本方法不仅能有效融合专家经验与需求关联的客观数据，还能使顾客满意度高的需求获得更高的重要度，从而为电商企业在资源有限的情况下进行精准的功能改进与服务升级提供了科学的决策依据。

Abstract: The key to enhancing the core competitiveness of e-commerce platforms is to improve user experience, and the key to improving user experience is to optimize website functions or services. Accurately identifying customer needs is particularly important in this process. This paper proposes a comprehensive decision-making method based on link prediction to address the issues of traditional methods being too subjective and neglecting customer demand correlation in e-commerce scenarios. Firstly, using hesitant fuzzy DEMATEL to calculate the impact between customer needs, a directed weighted network is constructed. Then, the weighted LeaderRank algorithm is used to perform random walks on the network. Based on the quantitative results of customer demand importance, clear hierarchical strategies and resource allocation suggestions are provided for the improvement of e-commerce platform functions and service upgrades. Taking 15 customer needs of a typical shopping website as an example for empirical analysis, the results show that this method can not only effectively integrate objective data related to expert experience and needs, but also give higher importance to needs with high customer satisfaction, thus providing a scientific decision-making basis for e-commerce enterprises to make precise functional improvements and service upgrades in resource limited situations.