个性化推荐算法驱动电商平台营销效益增长的价值共创机制研究

Research on the Value Co-Creation Mechanism of Personalized Recommendation Algorithms Driving E-Commerce Platform Marketing Performance Growth

摘要: 在电子商务平台的运营实践中，个性化推荐算法已从传统的“需求匹配”工具跃升为驱动平台营销效益增长的核心基础设施。文章基于价值共创理论，从电商平台运营逻辑与信息经济学视角出发，打破将算法仅视为流量分发枢纽的传统认知，提出了“算法价值共创模型”，系统阐释了算法通过“价值赋能–价值评估–价值转化”路径，在与消费者协同重构商品经济价值感知的过程中，实现平台营销效益增长的作用机制。具体而言，算法通过多维标签体系重构商品价值属性，拓展溢价空间(价值赋能)；消费者则通过比价、交叉验证等信息交互行为进行价值博弈与评估(价值评估)；最终平台通过“行为反馈–算法迭代–交易达成”的循环机制实现转化率、客单价及用户生命周期价值的全面提升(价值转化)。研究为电商平台突破流量瓶颈、优化商业化变现路径、实现长期经济效益增长提供了理论依据与实践策略。

Abstract: In e-commerce platform operations, personalized recommendation algorithms have evolved from traditional demand-matching tools into core infrastructure driving platform marketing performance growth. Drawing on value co-creation theory, e-commerce platform operational logic, and information economics, this study challenges the traditional view of algorithms as mere traffic distributors. It proposes an “Algorithmic Value Co-Creation Model” and systematically explains how algorithms drive growth in platform marketing performance through a three-stage pathway—value enablement, value evaluation, and value transformation—by collaborating with consumers to reconstruct consumers’ perceived economic value of products. Specifically, algorithms reconstruct product value attributes through a multi-dimensional labeling system to expand premium pricing potential (value enablement). Consumers then engage in value bargaining and assessment through price comparison and cross-validation (value evaluation). Finally, the platform achieves comprehensive improvements in conversion rate, average transaction value, and customer lifetime value through a circular mechanism of “behavioral feedback - algorithmic iteration - transaction completion” (value transformation). This study provides both theoretical foundations and practical strategies for e-commerce platforms to overcome traffic bottlenecks, optimize monetization pathways, and achieve sustainable long-term economic growth.