Application of Customer Lifetime Value Models in Banking

How banks can use the customer lifetime value model to identify the profit contributions of individual customers.

Gary Class
Gary Class
10 juillet 2024 3 min de lecture

Application of Customer Lifetime Value Models in Banking 

While models are abstractions of reality, they’re nonetheless useful tools to measure the financial impact of a bank’s strategy to influence customer behavior. The customer lifetime value model provides an important framework to identify the profit contributions of individual customers in current and future periods.  

In “The Customer Centricity Playbook: Implement a Winning Strategy Driven by Customer Lifetime Value,” coauthors Pete Fader and Sarah Toms relate how the development of a customer lifetime value model should begin with basic questions such as “Can we project how long the customer is going to stay?” and “How much is the customer going to spend over the time horizon?” 

In banking, customer attrition—or inversely, relationship retention—is driven by the breadth of product holdings and the depth of product usage as dimensioned by payment intensity and channel usage. Fortunately, Teradata ClearScape Analytics™ enables the development of a customer attrition model leveraging econometric methods, such as logistic regression, or tree-based methods like gradient-boosted decision trees. 

Customer profitability is the revenue generated by the customer’s current product holdings less the direct cost of product usage. In banking, revenue is largely driven by the net interest margin charged to loans or credited to deposits plus any direct fees collected.  

Direct cost is primarily driven by channel usage, which can be allocated either by activity, as in the duration of channel interaction times the marginal cost per second, or by access, as in the allocation of branch expenses to customers who are the primary patrons of the branch.  

Retail banking is a 90/10 business, where 10% of customers account for 90% of the profits. When most customers generate close to zero profit, subtle differences in behavior, notably the breadth and depth of digital channel usage, materially impact customer profitability.  

Customer lifetime value is a discounted cashflow analysis of customer profitability cast over the customers’ expected future life discounted at the bank’s target return on equity. Robust measures of customer lifetime value carefully assess the likelihood of product expansion in future periods as well as potential increases in the usage of current products. Calculation of customer lifetime value estimates can be validated by analyzing small bank acquisitions, divestitures, and branch acquisitions.  

Banks spend most of their advertising and marketing dollars on acquiring new customers—and little on customer retention. Traditionally, they’ve focused on “product push” marketing, i.e., delivering targeted offers to a prequalified audience. One example of this is the airline miles bonus credit card offer. A careful consideration of the impact of attrition on customer lifetime value motivates banks to promote “services pull” marketing, or driving customers to services that disincentivize defection, such as the direct deposit of paychecks and online bill payments. 

A less obvious application of the customer lifetime value model is to identify the economic value created by a bank strategy, such as a digital migration strategy, to change its customers’ behavior. Banks aggressively promote the migration of activity from physical channels (such as branches) to remote channels (such as digital banking), which can have an immediate positive impact on operating expenses.  

However, some customers may react negatively to the promotion of remote banking, reducing deposit balances, dropping accounts, or even leaving the bank altogether. How can a bank weigh the opportunity for lower operating expenses today against the risk of reduced revenue tomorrow? Ultimately, it needs to measure how individual customers respond to the bank’s digital migration strategy. The bank can identify the customers impacted by the strategy and use its customer lifetime value model as a metric to enumerate the strategy’s financial impact on current and future periods. As the profitability of the customer base is highly skewed, it’s critical to assess the customer lifetime value of the customer impacted by the bank’s strategy.  

To learn how banks are moving from the limited dimensions of the traditional customer profile to a holistic view, check out the white paper, “Customer Banking Relationship in 5 Dimensions.


À propos de Gary Class

Gary is an accomplished industry strategist with extensive experience in financial services, where he has made significant contributions to advanced analytics and AI. Gary spent over three decades at Wells Fargo Bank as the Director of Advanced Analytics at the forefront of innovation during the transformational era of “anytime, anywhere” banking. His visionary leadership has shaped the landscape of financial services through innovation, data-driven insights, and strategic thinking.

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