Overview
Most CLV calculations are simple averages: ARPU × average tenure = CLV. That number is useless. It hides the 20% of customers who drive 80% of lifetime value, and the 40% who never break even on CAC. Decisions made on average CLV systematically overspend on low-value acquisitions.
The CLV Prediction Model segments customers by predicted lifetime value using retention curves for subscription businesses and probabilistic models (BG/NBD + Gamma-Gamma) for transactional businesses — producing per-customer CLV estimates that drive CAC caps, retention investment prioritization, and product pricing decisions.
What you get: - Business model classification (contractual vs. non-contractual) - Cohort-based retention curve construction - BG/NBD model for purchase frequency (non-contractual) - Gamma-Gamma model for order value (non-contractual) - Probabilistic CLV with confidence intervals per customer - CLV segmentation (top 10% / middle 80% / bottom 10%) - CAC cap recommendations by CLV segment - Retention investment ROI calculation
Built for: SaaS finance teams, e-commerce analysts, marketplace operators, and subscription businesses who need CLV estimates that drive real decisions — not averages that obscure the distribution.