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论文编号：	15009
作者编号：	2320224197
上传时间：	2024/12/9 15:05:00
中文题目：	A 银行 B 分行零售定期存款的精准营销研究
英文题目：	The precise marketing study of retail time deposits at Branch B of Bank A
指导老师：	安利平
中文关键字：	零售存款；银行业务；精准营销
英文关键字：	Retail Deposits; Banking Operations; Precision Marketing
中文摘要：	当前社会正在经历数字革命技术的迅猛发展，大数据的广泛运用已经成为银行业竞争发展的关键转折点。在大数据的背景下，数字化转型成为银行业的重要议题。新型金融服务模式不仅给商业银行带来了巨大挑战，也为银行业的发展创新提供了前所未有的机遇。随着数字化时代的兴起，精准营销在各个领域得到了广泛应用。银行业利用各类数字化信息全面了解客户，基于数据集成，建立精准营销模型，探索客户画像，深入洞察客户的消费需求，产品偏好和资金管理需求，以此为客户提供个性化的产品或服务。这种做法不仅节省了大量资源，也显著提高了营销效率。 本文以A银行B分行零售定期存款业务中实际存在的问题作为研究对象，探究在大数据环境下，通过客户的数据信息构建精准营销模型，使用模型帮助银行筛选出存款概率更大的客户以及找到目标客户特征，提高营销效率及成功率，从而扩大A银行B分行零售定期存款规模。基于此，首先对客户画像、精准营销进行深入了解，探究这些理论和技术的基本原理，同时了解其在国内外银行业领域中的应用背景、范围和现状，为解决A银行B分行零售定期存款中出现的问题提供理论支撑和依据。其次，结合A银行B分行零售定期存款开展的背景及发展现状，分析该行零售定期存款业务内部营销困境成因及外部面临挑战，提出利用银行内外部客户信息及数据挖掘技术构建精准营销模型的思路去解决问题，并对精准营销模型在银行业的策略优化以及应用保障进行分析。以银行内部客户信息数据为主体，通过对集成的数据进行核查、清洗、筛选操作，通过Python程序语言调用逻辑回归模块及CART决策树模块进行模型训练，形成A银行B分行零售定期存款精准营销模型。最后，以7P理论为基础，对A银行B分行零售定期存款精准营销提出策略优化，同时针对精准营销的应用提出保障措施。 为保障模型拟合优度，采用ROC曲线对模型区分效果进行检验，检验AUC值分别为0.81和0.88，在合理值范围，故该模型可在A银行B分行零售定期存款业务开展过程中使用，以帮助从业人员合理利用资源、提高营销效率及扩大业务规模。
英文摘要：	The current society is undergoing rapid development in digital revolution technology, and the extensive use of big data has become a crucial turning point for the competitive development of the banking industry. In the context of big data, digital transformation has become an important issue in the banking industry. The new financial services model has not only brought significant challenges to commercial banks but also unprecedented opportunities for innovative development in the banking industry. With the rise of the digital age, precise marketing has been widely applied in various fields. The banking industry utilizes various forms of digital information to comprehensively understand customers. Based on data integration, precise marketing models are established to explore customer profiles, gain in-depth insight into customer consumption needs, product preferences, and fund management requirements, thereby providing personalized products or services for customers. This approach not only saves a significant amount of resources but also significantly enhances marketing efficiency. This paper takes the actual problems existing in the retail time deposit business of Branch B of Bank A as the research object, exploring the construction of a precision marketing model through customer data in the context of big data. The model is used to assist the bank in identifying customers with a higher probability of depositing and to identify the characteristics of target customers, enabling relationship managers to concentrate effective resources on "core customers" for targeted marketing, thereby improving marketing efficiency and success rates, and ultimately expanding the scale of retail time deposits at Branch B of Bank A. Based on this, a comprehensive understanding of data analysis, customer profiling, and precision marketing is initially conducted to explore the fundamental principles of these theories and technologies. Simultaneously, the application background, scope, and current situation of these theories and technologies in the domestic and international banking industry are examined to provide theoretical support and a basis for addressing the issues present in the retail time deposit business at Branch B of Bank A. Subsequently, in conjunction with the background and current status of the retail time deposit business at Branch B of Bank A, the internal marketing difficulties and external challenges faced by the business are analyzed. A proposal is put forward to utilize both internal and external customer information and data mining technology to construct a precision marketing model to address these issues. Furthermore, an analysis of the strategic optimization and application security of the precision marketing model in the banking industry is provided. With a focus on internal customer information data, a usable customer information database is formed through the verification, cleaning, and selection of integrated data. This database serves as the basis for training the precision marketing model for retail time deposits at Branch B of Bank A, achieved through the use of the Python programming language to invoke the logistic regression module and the CART decision tree module. Finally, based on the 7P theory, strategies for optimizing precision marketing for retail time deposits at Branch B of Bank A are proposed, along with measures to ensure the application of precision marketing. To ensure the goodness of fit of the model, the ROC curve was used to test the model's discriminatory effect, with AUC values of 0.81 and 0.88, falling within a 1reasonable range. Therefore, this model can be used in the process of conducting retail time deposit business at Branch B of Bank A to assist practitioners in the rational utilization of resources, improving marketing efficiency, and expanding business scale.
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