社交电商时代网络营销策略的精准化与用户行为演化研究——基于大数据与数据库营销的整合视角

Study on the Precision of Online Marketing Strategies and the Evolution of User Behavior in the Era of Social E-Commerce—From the Integrated Perspective of Big Data and Database Marketing

摘要: 社交电商崛起重塑商业范式，营销核心从“交易”转向“关系”与“信任”。传统精准营销遭遇“数据孤岛”困境——企业内部结构化CRM数据与外部非结构化社交大数据割裂，无法触达用户决策核心。本文提出，社交电商时代“精准化”需建立在大数据与数据库营销深度整合之上，研究基于S-O-R (刺激–有机体–反应)理论框架展开。研究发现，数据整合通过AI与机器学习，实现从静态分群到动态个体画像的飞跃，催生“关键意见领袖精准匹配(KOL)”“个性化内容”等新型精准策略(S)。这些策略通过重塑用户“感知信任”(基于KOL专业性)与“社会交换动机”(基于社区反馈)，改变用户内部心理状态(O)，进而推动用户行为(R)演化，表现为冲动性购买增加、信息分享常态化。更重要的是，用户行为响应(R)作为新数据源和社交刺激(S')纳入整合数据库，形成S-O-R-S'动态闭环。最后，研究反思“精准化”背后的操纵、算法偏见等伦理边界，展望未来趋势，为理解社交电商营销与消费行为提供动态协同演化模型。

Abstract: The rise of social e-commerce has reshaped the business paradigm, shifting the core of marketing from “transactions” to “relationships” and “trust”. Traditional precision marketing faces the dilemma of “data silos”—structured Customer Relationship Management (CRM) data within enterprises is isolated from unstructured external social big data, failing to reach the core of users’ decision-making. This study argues that “precision” in the era of social e-commerce must be built on the in-depth integration of big data and database marketing, and the research is conducted based on the Stimulus-Organism-Response (S-O-R) theoretical framework. The findings show that through artificial intelligence (AI) and machine learning, data integration enables a leap forward from static user segmentation to dynamic individual profiling, spawning new precision strategies (S) such as “precision KOL (Key Opinion Leader) matching” and “hyper-personalized content”. These strategies reshape users’ “perceived trust” (based on KOL expertise) and “social exchange motivation” (based on community feedback), altering users’ internal psychological states (O) and thereby driving the evolution of user behavior (R)—manifested in increased impulsive purchasing and the normalization of information sharing. More importantly, users’ behavioral responses (R) are incorporated into the integrated database as new data sources and social stimuli (S’), forming a dynamic S-O-R-S’ closed loop. Finally, the study reflects on the ethical boundaries behind “precision”, such as manipulation and algorithmic bias, looks ahead to future trends, and provides a dynamic co-evolution model for understanding social e-commerce marketing and consumer behavior.