Quick Answer (TL;DR)
Time to Churn measures average duration before a customer churns. The formula is Median days from signup to churn. Industry benchmarks: Varies widely. Track this metric when identifying churn windows for intervention.
What Is Time to Churn?
Average duration before a customer churns. This is one of the core metrics in the retention metrics category and is essential for any product team serious about data-driven decision making.
Time to Churn is a direct measure of whether your product continues to deliver value over time. Retention is the single most important category for long-term product success because it compounds: small improvements today create massive differences over months and years.
Understanding time to churn in context --- alongside related metrics --- gives you a more complete picture than tracking it in isolation. Use it as part of a balanced metrics dashboard.
The Formula
Median days from signup to churn
How to Calculate It
Apply the formula Median days from signup to churn using data from a consistent time period. Pull the values from your analytics platform or data warehouse, compute the result, and compare against the benchmarks below.
Benchmarks
Varies widely
Benchmarks vary significantly by industry, company stage, business model, and customer segment. Use these ranges as starting points and calibrate to your own historical data over 2-3 quarters. Your trend matters more than any absolute number --- consistent improvement is the goal.
When to Track Time to Churn
When identifying churn windows for intervention. Specifically, prioritize this metric when:
You are building or reviewing your metrics dashboard and need retention indicators
Leadership or investors ask about retention performance
You suspect a change in product, pricing, or go-to-market strategy has affected this area
You are running experiments that could impact time to churn
You need a quantitative baseline before making a strategic decision
How to Improve
Invest in proactive customer success. Do not wait for users to complain or churn. Use leading indicators (declining usage, support tickets, low NPS) to intervene early with at-risk accounts.
Continuously deliver value. Retention requires ongoing value delivery, not just an initial aha moment. Ship improvements, communicate them, and ensure users see the product evolving to meet their needs.
Run cohort analysis regularly. Compare retention curves across signup cohorts to determine whether product changes are improving or hurting long-term retention.
Common Pitfalls
Using averages instead of medians. Time-based metrics are often skewed by outliers. A few extremely slow cases can inflate the average and mask the typical experience. Use medians for a more accurate picture.
Looking only at aggregate retention. Blended retention hides critical differences between customer segments, cohorts, and plan tiers. Always segment your retention analysis.
Measuring without acting. Tracking this metric is only valuable if you have a process for reviewing it regularly and a playbook for responding when it moves outside acceptable ranges.
Related Metrics
Expansion Rate --- percentage of revenue gained from upsells/cross-sells
Retention by Cohort --- retention segmented by signup date
Contraction Rate --- percentage of revenue lost to downgrades
Reactivation Rate --- percentage of dormant users who become active again
Product Metrics Cheat Sheet --- complete reference of 100+ metrics