摘要：随着跨境电子商务的快速发展，客户满意度成为了企业竞争的重要指标。本文基于大数据技术，构建了B2C跨境电子商务客户满意度预测模型，并应用于实际案例中。首先，通过数据挖掘技术对客户满意度相关数据进行分析和处理，提取出关键特征。然后，采用机器学习算法构建预测模型，并对模型进行优化和验证。最后，将模型应用于实际案例中，预测客户满意度并提出改进建议。实验结果表明，该模型具有较高的预测精度和实用性，可为企业提供有效的决策支持。
关键词：大数据；B2C跨境电子商务；客户满意度；预测模型
Abstract: With the rapid development of cross-border e-commerce, customer satisfaction has become an important indicator of enterprise competition. Based on big data technology, this paper constructs a B2C cross-border e-commerce customer satisfaction prediction model and applies it to practical cases. Firstly, customer satisfaction related data is analyzed and processed by data mining technology to extract key features. Then, machine learning algorithms are used to construct prediction models, and the models are optimized and verified. Finally, the model is applied to practical cases to predict customer satisfaction and propose improvement suggestions. The experimental results show that the model has high prediction accuracy and practicality, and can provide effective decision support for enterprises.
Keywords: big data; B2C cross-border e-commerce; customer satisfaction; prediction model
基于大数据技术的B2C跨境电子商务客户满意度预测模型构建及应用
