Customer Lifetime Value (CLV)

Basic CLV = (Average Purchase Value × Purchase Frequency) × Average Customer Lifespan. For predictive CLV, use machine learning models that analyze purchase patterns, engagement signals, and demographic factors to forecast future value. E-commerce CLV typically ranges $100-$300, while SaaS CLV can be thousands based on subscription duration.

A healthy CLV:CAC ratio is 3:1 or higher, meaning customers generate 3x what you spend to acquire them. Below 1:1 means you're losing money on acquisition. VCs typically look for 3:1+ for SaaS and 4:1+ for e-commerce. Higher isn't always better—it might mean you're under-investing in growth.

AI analyzes hundreds of signals—purchase patterns, browsing behavior, engagement metrics, support interactions—to predict future value. Machine learning identifies which early behaviors predict high CLV, enabling proactive intervention. AI-powered CLV predictions are typically 30-50% more accurate than historical averages.

Focus on: 1) Improving retention through better onboarding and customer success, 2) Increasing purchase frequency with loyalty programs and personalized recommendations, 3) Raising average order value through upselling and bundling, 4) Extending customer lifespan by reducing churn triggers, 5) Creating referral programs that acquire similar high-CLV customers.

CLV and LTV (Lifetime Value) are used interchangeably by most marketers. Some distinguish LTV as the gross revenue figure and CLV as the profit-adjusted version. For clarity, always specify whether you're calculating revenue-based or profit-based lifetime value.

Recalculate CLV quarterly at minimum. For fast-moving businesses or those implementing new retention programs, monthly updates are better. Predictive CLV models should be retrained quarterly and recalculate individual customer scores in real-time or daily.