你现在的身份是论文作者请对工商管理硕士论文《基于大数据的西安邮政银行银保业务营销策略优化研究》国外研究文献综述进行总结其格式为学者姓名+年份+提出观点13国内外文献综述 131国外文献综述

Kim et al. (2016) proposed a data-driven approach for predicting customer churn in the banking industry. They used machine learning algorithms to analyze customer data and identified key factors that influence customer churn. The study highlights the importance of leveraging big data for customer retention strategies.

Wu et al. (2017) conducted a study on the use of big data analytics in the financial industry. They found that big data can be used to improve customer segmentation, risk management, and fraud detection. The study emphasizes the need for financial institutions to invest in big data analytics to gain a competitive edge.

Chen and Liu (2018) explored the use of big data analytics for credit risk assessment in the banking industry. They found that big data can provide valuable insights into customer behavior and creditworthiness. The study suggests that big data analytics can help financial institutions make better lending decisions.

Li et al. (2019) conducted a study on the use of big data analytics for marketing in the banking industry. They found that big data can be used to personalize marketing messages and improve customer engagement. The study highlights the potential of big data analytics for enhancing marketing strategies in the financial industry.

Wang et al. (2020) proposed a framework for using big data analytics to improve customer experience in the banking industry. They identified key factors that influence customer experience and developed a model for analyzing customer feedback data. The study suggests that big data analytics can help financial institutions improve customer satisfaction and loyalty