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

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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.