Lifetime Value (LTV)
LTV (also written as CLV — Customer Lifetime Value) is the total revenue a business can expect from a single customer account over the entire relationship. It's the foundational metric for determining how much you can spend to acquire a customer (CAC) and whether your unit economics are healthy. For SaaS, LTV is primarily driven by ARPU and churn rate.
Note: For example: $100 ARPU ÷ 2% monthly churn = $5,000 LTV. Also expressed as: Average Contract Value × (1 ÷ Annual Churn Rate).
LTV:CAC ratio > 3:1 is the standard SaaS benchmark; > 5:1 indicates strong unit economics
LTV:CAC < 1:1 means you're spending more to acquire customers than they're worth
Benchmarks by segment
How to improve LTV
Reduce churn — a 1% improvement in monthly churn has a larger LTV impact than a 10% revenue increase at most churn levels
Increase ARPU through upsells, expansions, and add-ons from existing customers
Identify your highest-LTV customer segments and focus acquisition on those ICP profiles
Build switching costs (integrations, data lock-in, workflows) that extend the natural lifetime
Common measurement mistakes
Tools for measuring LTV
Best-in-class behavioral analytics with powerful event segmentation, funnel analysis, and retention charts that go far deeper than Google Analytics
Best-in-class event-based analytics with intuitive funnel, retention, and flow reports that surface actionable insights quickly
All-in-one product analytics platform combining analytics, session replay, feature flags, A/B testing, surveys, and a data warehouse — replacing multiple point solutions
Autocapture eliminates the need for manual event instrumentation — every click, pageview, and form interaction is tracked automatically from day one
All-in-one platform combining feature flags, A/B testing, product analytics, session replay, and web analytics — eliminating the need for separate tools
Best-in-class no-code editor for creating in-app walkthroughs, tooltips, and interactive guides without developer involvement
Frequently Asked Questions
Historical LTV is reliable but backward-looking. Predictive LTV uses ML to estimate future value based on early signals (activation depth, usage patterns). For product decisions, historical cohort LTV is usually sufficient. Predictive LTV is more useful for marketing bid optimisation.
CAC payback period < 12 months is the benchmark for healthy SaaS growth. < 6 months is excellent. > 18 months creates cash flow risk and slows reinvestment.