数据驱动的电子商务企业客户全生命周期价值挖掘与营销策略迭代研究

Study on Data-Driven Customer Lifetime Value Mining and Marketing Strategy Iteration in E-Commerce Enterprises

摘要: 本项研究专注于电子商务领域，并构建了一个以客户全生命周期价值(CLV)为中心的数据驱动的营销策略框架。通过运用机器学习建模、多维度数据评估和归因分析，对客户价值的分层度量、关键节点的数据挖掘、精准策略设计和闭环优化机制进行系统的论述。研究表明，利用基于行为序列的潜在客户识别、面向价值层次的自动旅行和动态场景策略，可以显著提高CLV的性能并优化其营销效果。得出结论，构建面向CLV升级的“数据感知–策略迭代”闭环是企业持续成长的关键所在。

Abstract: This study focuses on the e-commerce field and constructs a data-driven marketing strategy framework centered on Customer Lifetime Value (CLV). Through machine learning modeling, multi-dimensional data evaluation, and attribution analysis, the research systematically discusses the hierarchical measurement of customer value, key node data mining, precise strategy design, and closed-loop optimization mechanisms. The study shows that using behavior sequence-based latent customer identification, value-level automated journeys, and dynamic scenario strategies can significantly improve CLV performance and optimize marketing effectiveness. It is concluded that constructing a “data perception-strategy iteration” closed loop aimed at CLV upgrade is crucial for the sustainable growth of enterprises.