Summary of customer information.

In the competitive retail omnichannel market, customer loyalty is essential for maintaining market share and reducing the cost of acquiring new customers. Previous research has primarily focused on factors influencing customer loyalty, often in isolation, but this study goes beyond traditional approaches. The aim of this research is to fill significant gaps in current studies by integrating a more comprehensive set of variables that reflect the complex and dynamic nature of customer loyalty in a flexible omnichannel environment. The main innovation of this study lies in the use of new and comprehensive omnichannel data, which includes sales data across various platforms, socio-economic conditions, shopping cart behaviors, and customer sentiments. The proposed model utilizes a hybrid approach, incorporating BERT for sentiment analysis, reinforcement learning for behavior analysis, and fine-tuning for improved predictions. Additionally, graph-based models (GCN) and adaptive learning are employed to analyze trends and predict loyalty at both individual and neighborhood levels. This research provides an intelligent analytical framework for predicting customer loyalty in omnichannel retail environments, enhancing Customer Relationship Management (CRM) subsystems within Enterprise Information Systems (EIS). By optimizing decisions in areas such as pricing, inventory management, and personalized advertising, this study ultimately leads to improved customer retention and increased market competitiveness.

在竞争激烈的全渠道零售市场中，客户忠诚度（Customer Loyalty）对于维持市场份额、降低新客户获取成本至关重要。既往研究多聚焦于单一维度的客户忠诚度影响因素，本研究则突破了传统研究范式。本研究旨在整合一套更全面的变量集，以刻画灵活全渠道环境下客户忠诚度的复杂动态特性，填补当前研究中的显著空白。本研究的核心创新在于采用了全新且全面的全渠道数据集，涵盖多平台销售数据、社会经济状况、购物车行为与客户情感数据。所提出的模型采用混合架构：将BERT用于情感分析、强化学习用于行为分析，并通过微调优化预测精度。此外，本研究还引入基于图的模型（GCN）与自适应学习方法，分别从个体与社区层面开展趋势分析与忠诚度预测。本研究为全渠道零售环境下的客户忠诚度预测提供了智能分析框架，可优化企业信息系统（Enterprise Information Systems，EIS）内的客户关系管理（Customer Relationship Management，CRM）子系统。通过优化定价、库存管理与个性化广告等领域的决策，本研究最终可提升客户留存率与市场综合竞争力。