Definition
Time to value (TTV) is how long it takes a new customer to reach a meaningful outcome. Shorter TTV tends to improve retention.
How to use it
- Define 'value' as the earliest outcome that predicts retention or expansion.
- Track TTV by segment to improve onboarding and product activation.
Why this matters
This term matters because small changes compound in SaaS metrics. Use consistent definitions by cohort and segment so you can diagnose retention, payback, and growth quality.
Practical checklist
- Write a 1-line definition for "Time to Value (TTV)" that your team will use consistently.
- Keep the time window consistent (weekly/monthly/quarterly) when comparing trends.
- Segment results (channel/plan/cohort) before drawing big conclusions from blended averages.
- Sanity-check with a related calculator from the same category on MetricKit.
- Read the related guide (e.g., PLG metrics hub: activation, trial conversion, stickiness, and adoption) for context and common pitfalls.
Where to use this on MetricKit
Calculators
- LTV Sensitivity Calculator: See how gross profit LTV changes as churn and gross margin vary (simple 3x3 sensitivity).
- LTV:CAC Calculator: Compute LTV:CAC ratio and CAC payback using ARPA, gross margin, churn, and CAC.
- CAC Payback Period Calculator: Estimate how many months it takes to recover CAC (months to recover CAC) using gross profit.
- CAC Payback Sensitivity Calculator: See how CAC payback months change as ARPA and gross margin vary (simple 3x3 sensitivity).
- Churn Rate Calculator: Calculate customer churn rate for a period and compare retention across segments or cohorts.
Guides
- PLG metrics hub: activation, trial conversion, stickiness, and adoption: A practical hub for product-led growth metrics: activation rate, trial-to-paid, DAU/MAU and WAU/MAU stickiness, feature adoption, and PQL-to-paid conversion.
- Retention & churn hub: cohorts, GRR/NRR, and retention curves: A practical hub for retention measurement: churn rate, GRR/NRR, cohort retention curves, and how to set retention targets without getting misled by noise.