Description

At Colcrane, we build research-driven predictive Customer Lifetime Value (CLV) models that estimate the future value each customer will generate — even several years ahead. We define CLV as the net present value of all future variable profits from a customer, minus associated costs (including acquisition), discounted to reflect risk and the company's cost of capital.

Our models are predictive, decision-oriented, and built on explainable Bayesian probability methods. Instead of relying on black-box AI or static, backward-looking averages, we use dynamic statistical models that adapt to individual customer behavior and reveal value heterogeneity rather than hiding it. By incorporating seasonality and your actual cost of capital, the models provide a financially realistic view of customer value across the entire lifecycle.

We integrate existing segmentations and KPIs, ensuring the model aligns with your operating reality. Platform-agnostic by design, outputs can be deployed directly into your CRM or CDP, and Colcrane provides ongoing monitoring, development, and iteration to keep predictions accurate and actionable over time.

By segmenting customers by predicted future value, organizations can move from personalizing everything to personalizing with purpose—allocating resources to the customers and actions that drive the strongest long-term returns, and focusing marketing, retention, and service investments where they matter most. When embedded into day-to-day operations, CLV shifts from a static metric into a reliable customer value operating lens—a decision-support system that sharpens focus, guides investment, and drives measurable performance.