Modeling and segmentation of the customer lifetime value in the banking industry

银行业客户终身价值的建模和细分

• 批准号: 492137-2015
• 负责人: Augustyniak, Maciej
• 金额: $ 1.79万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Engage Grants Program
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587192
• 关键词: Modeling; segmentation; customer; lifetime; value

Due to the strong competition in the Canadian banking market, acquiring and retaining profitable customers is more challenging than ever. Because customer demographics, buying behavior and needs are evolving rapidly, banks must now develop dynamic statistical models that will help them identify the right products to offer to their clients. These challenges are pushing the National Bank of Canada (NBC) to understand its Customer Lifetime Value (CLV) at an individual customer level, across all their financial products (deposits, loans, investments, credit card, etc...). The CLV can be defined as the revenue that a client is expected to generate for the bank throughout his or her relationship with the bank. The broad problem is therefore to develop advanced statistical methods to precisely evaluate and project the CLV. The CLV will then help the NBC to define, understand, and predict in a novel way the relationship between a given client and the NBC. The model is therefore intended to drive marketing decisions and customer relationship management. This research project has two objectives. The first objective is the identification and segmentation of the variables that are relevant for measuring the CLV. Within this objective, we will investigate modern segmentation techniques that have yet to be considered in the CLV context, such as self-organizing maps and fuzzy clustering. The second objective is the development of an original predictive model that measures CLV by way of semi-Markov chains. This research project is intended to benefit the Canadian banking industry in the customer analytics field by improving banking services to better fit users.

由于加拿大银行业市场上的竞争激烈，获取和保留有利可图的客户是 比以往任何时候都更具挑战性。因为客户人口统计，购买行为和需求正在迅速发展，所以 银行现在必须开发动态的统计模型，以帮助他们确定合适的产品以提供 他们的客户。这些挑战促使加拿大国家银行（NBC）了解其客户 终身价值（CLV）在各个客户级别，其所有金融产品（存款，贷款， 投资，信用卡等...）。 CLV可以定义为客户预期为客户产生的收入 该银行整个与银行的关系。因此，一个广泛的问题是发展高级 精确评估和投射CLV的统计方法。然后，CLV将帮助NBC定义， 理解并以新颖的方式预测给定客户与NBC之间的关系。该模型是 因此旨在推动营销决策和客户关系管理。 该研究项目有两个目标。第一个目标是识别和分割 与测量CLV相关的变量。在这个目标中，我们将调查现代 在CLV环境中尚未考虑的细分技术，例如自组织地图和 模糊聚类。第二个目标是开发原始预测模型，该模型测量CLV 通过半马尔可夫连锁店。 该研究项目旨在使客户分析领域的加拿大银行业受益 改善银行服务以更好地适合用户。