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

由于加拿大银行业市场竞争激烈，获取和留住有利可图的客户是 比以往更具挑战性。由于客户人口统计、购买行为和需求正在迅速发展， 银行现在必须开发动态统计模型，帮助他们确定提供给客户的正确产品， 他们的客户这些挑战正在推动加拿大国家银行（NBC）了解其客户 在个人客户层面，在其所有金融产品（存款、贷款、 投资、信用卡等）。CLV可以定义为客户端预期产生的收入， 他或她与银行的关系。因此，最大的问题是发展先进的 统计方法，以准确地评估和预测CLV。CLV将帮助NBC定义， 理解，并以一种新颖的方式预测特定客户与NBC之间的关系。该模型 因此，旨在推动营销决策和客户关系管理。 这个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。在这一目标下，我们将研究现代 在CLV上下文中尚未考虑的分割技术，如自组织映射， 模糊聚类第二个目标是开发一个测量CLV的原始预测模型 通过半马尔可夫链。 该研究项目旨在通过以下方式使加拿大银行业在客户分析领域受益： 改善银行服务，更好地满足用户需求。

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.