摘 要
随着信息技术的迅猛发展，大数据为深入理解消费者行为提供了前所未有的机遇。本研究旨在利用大数据技术全面剖析消费者行为特征及其影响因素，以期为企业精准营销提供理论支持。研究基于海量多源异构数据，包括社交媒体、电商平台交易记录等，运用数据挖掘、机器学习算法对消费者购买决策过程进行建模分析。通过对比传统方法，创新性地引入情感分析和社交网络分析，从个体与群体两个层面揭示了消费者行为模式的变化规律。结果表明，消费者在做出购买决策时受多种因素共同作用，其中社交互动影响力显著增强，且不同消费群体呈现出差异化的行为特征。此外，研究发现新兴技术的应用能够有效提升企业预测消费者需求的准确性。本研究的主要贡献在于构建了一个融合多维度数据的大数据分析框架，不仅丰富了消费者行为理论体系，还为企业制定个性化营销策略提供了科学依据，对于推动商业智能化转型具有重要实践价值。
关键词：大数据分析 消费者行为 精准营销
Abstract
With the rapid development of information technology, big data offers unprecedented opportunities for gaining deeper insights into consumer behavior. This study aims to comprehensively analyze the characteristics and influencing factors of consumer behavior using big data technologies, thereby providing theoretical support for precision marketing in enterprises. Based on large-scale multi-source heterogeneous data, including social media and e-commerce platform transaction records, this research employs data mining and machine learning algorithms to model and analyze the consumer purchase decision-making process. By comparing traditional methods, this study innovatively incorporates sentiment analysis and social network analysis, revealing the changing patterns of consumer behavior at both individual and group levels. The results indicate that multiple factors collectively influence consumers' purchase decisions, with the impact of social interactions significantly increasing. Moreover, different consumer groups exhibit distinct behavioral features. Additionally, the application of emerging technologies has been found to effectively enhance the accuracy of predicting consumer demand. The primary contribution of this study lies in constructing a big data analytics fr amework that integrates multidimensional data, which not only enriches the theoretical system of consumer behavior but also provides a scientific basis for enterprises to develop personalized marketing strategies. This research holds significant practical value for promoting the intelligent transformation of businesses.
Keyword:Big Data Analysis Consumer Behavior Precision Marketing
目 录
引言 1
1大数据与消费者行为分析基础 1
1.1大数据技术概述 1
1.2消费者行为理论框架 2
1.3数据驱动的分析方法 2
2数据获取与处理技术 3
2.1多源数据采集方法 3
2.2数据清洗与预处理 3
2.3数据存储与管理策略 4
3消费者行为模式识别 4
3.1行为特征提取方法 4
3.2购买决策路径分析 5
3.3消费偏好动态建模 5
4应用场景与商业价值 6
4.1精准营销实践案例 6
4.2个性化推荐系统 7
4.3商业决策支持体系 7
结论 8
参考文献 9
致谢 9
