基于智能网联汽车大数据的用户画像构建与精准营销策略研究

Research on User Profile Construction and Precision Marketing Strategy Based on Big Data from Intelligent Connected Vehicles

摘要: 智能网联汽车生成的多模态数据为电子商务精准营销提供了全新范式。本研究旨在构建融合车联网数据与商业逻辑的动态用户画像体系，并设计可落地、可评估的精准营销策略框架，以解决汽车后市场营销粗放化问题，挖掘数据资产价值。本系统构建了包含车辆状态、驾驶行为、座舱交互、V2X及出行场景的多维度数据体系，提出了包含基础属性、动态行为、兴趣偏好、消费潜力的四层精细化标签体系，并明确了基于规则与机器学习(K-means、XGBoost等)的标签生成方法。进而设计了“数据–画像–场景–策略–反馈”的闭环营销应用框架，并基于用户生命周期、出行场景、车辆类型三维度细化策略。结果表明，智能网联汽车大数据是驱动场景电商革命的核心资产，通过系统性数据治理、前沿算法与闭环业务设计，可实现营销效率质的飞跃。

Abstract: The multimodal data generated by intelligent connected vehicles provides a new paradigm for precision marketing in e-commerce. This study aims to construct a dynamic user profiling system that integrates vehicle networking data with business logic, and to design an implementable and evaluable precision marketing strategy framework. This addresses the issue of extensive marketing in the automotive aftermarket and unlocks the value of data assets. The system establishes a multi-dimensional data architecture encompassing vehicle status, driving behavior, in-cabin interaction, V2X, and travel scenarios. It proposes a four-tier refined tagging system comprising basic attributes, dynamic behaviors, interest preferences, and consumption potential, and defines tagging generation methods based on both rules and machine learning (e.g., K-means, XGBoost). Furthermore, a closed-loop marketing application framework of “Data-Profiles-Scenarios-Strategies-Feedback” is designed, with strategies refined across three dimensions: user lifecycle, travel scenarios, and vehicle type. The results indicate that intelligent connected vehicle big data serves as a core asset driving the revolution in scenario-based e-commerce. Through systematic data governance, advanced algorithms, and closed-loop business design, a qualitative leap in marketing efficiency can be achieved.