
Predictive CLV Modeling

Description
Predictive Customer Lifetime Value (CLV) modeling estimates the total value a customer is expected to generate over the entire course of their relationship with your business. CLV is defined as the net present value of all variable profits and costs associated with the customer—including acquisition costs—until the relationship ends. To ensure financial accuracy and strategic alignment, the model incorporates a discount rate that reflects the relevant cost of capital for the business unit into which the customer was acquired.
While most companies rely on static, backward-looking CLV calculations, we take a forward-thinking approach. Our models predict each customer's future value based on behavioral signals, timing, and engagement—not just historical transactions. Built on the latest research, they adapt dynamically to real-time interactions and shifting behavioral patterns. This forward-looking perspective enables segmentation by predicted value — revealing which actions truly drive profitability. The result: smarter campaign targeting, sharper strategic decisions, and measurable impact on future growth.
By delivering precision at every level—from cohorts and microsegments to individual customers—our models help you answer both tactical and strategic questions through the lens of lifetime value. You'll gain clear insight into how seasonality, acquisition quality, and engagement affect your bottom line—guiding where to focus, how to refine your strategy, and how to fuel sustainable growth. This is where CLV evolves from a static metric into a strategic lens: a dynamic decision support system that drives business performance.
Value
Revenue forecasting: Offers a forward looking estimate of a customer's total value, enabling accurate revenue forecasting.
Resource Optimization: Powerful tool to guide businesses in efficiently allocating resources towards acquiring and retaining high-value customers.
Tailored marketing: By understanding the potential value of different customer cohorts, businesses can design more personal marketing strategies.
Churn Predictions: Predictive insights allow companies to identify and engage customers at risk of churn, enhancing customer retention.
Applied in
• Non-contractual business models (B2C & B2B)
• Subscription business models (B2C & B2B)
Often used iteratively alongside our Behavioral Experimentation and Segmentation services to identify, test, and act on high-value customer behaviors.
Example Ouputs


Predictive vs. Traditional Non-predictive Model
Predictive Model

Traditional Model

By not accounting for individual differences in the customer base the true value of the customer base can be underestimated by 25-50% (Fader & Hardie 2010).
Implementation Process
