Customer Lifetime Value Modeling & Revenue

By: Dennis Driscoll (
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CLV - Customer Lifetime Value | Anchor Computer

Customer Lifetime Value (CLV) is the “discounted value of future profits generated by a customer.”[1] Companies are constantly trying to gauge the value of a customers through the course of their interactions. Profits are composed of revenue and cost components, and most organizations are keenly attune to what their particular cost structure is at any moment in time. Revenue is much harder to ascertain, and is thereby much more important to assign to customers.

Revenue and value can be generated in a variety of ways, and all contribute to CLV. When a customer shares a company’s content on LinkedIn (Providing indirect marketing in the process) that is providing value. The main gauge of customer value is through direct purchases. How often and how much does a client purchase, and for how long. Customer’s direct purchases are often analyzed in two ways historical and predictive customer modeling.

Historical CLV

Historical CLV modeling looks at a customer’s past purchases, and extrapolates future results based on the data. The models begin by determining whether a customer is active or inactive. Certain customers might be active for several years, and with life changes might immediately become inactive. While formerly inactive customer’s might suddenly become highly active direct purchasers. The variations can be factored into the modeling process to produce the most accurate result.

Predictive CLV

Predictive CLV modeling provides a different type of probabilistic analysis. The model focuses on the purchasing behavior of customers to infer what future purchases will be made[2]. There are several categories of non-contractual purchases that must be included in this type of modeling.

Continuous Purchases: Purchases without contracts where customers can purchase at any time and leave anytime. Think grocery purchases & purchases.

Discrete Purchases:  Purchases that occur at fixed intervals, like drug prescription refills.

Many other aspects go into predictive CLV modeling including customer’s purchase count, monetary value, lifespan, and other parameters.

There are multiple CLV models that all serve the same purpose to better gauge the revenue contribution of individual customer’s. The more companies can engage customers that plan to purchase soon & often, the more revenue will result. Companies are always driving at increasing revenue, and CLV data modeling provides the tools to identify the customers to do just that.  Please don’t hesitate to contact us if we can help.

[1] Jean-Rene Guathier,

[2] Jean-Rene Guathier Datascience

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