Quick Answer (TL;DR)
Week-over-Week Retention measures percentage of users retained from one week to the next. The formula is Users active this week who were active last week / Last week active users x 100. Industry benchmarks: 60-80%. Track this metric when tracking weekly retention trends.
What Is Week-over-Week Retention?
Percentage of users retained from one week to the next. 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.
Week-over-Week Retention 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 week-over-week retention 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
Users active this week who were active last week / Last week active users x 100
How to Calculate It
Suppose you measure users active this week who were active last week at 500 and last week active users at 2,000 in a given period:
Week-over-Week Retention = 500 / 2,000 x 100 = 25%
This tells you that one quarter of the base is converting or meeting the criteria.
Benchmarks
60-80%
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 Week-over-Week Retention
When tracking weekly retention trends. 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 week-over-week retention
You need a quantitative baseline before making a strategic decision
How to Improve
Optimize the numerator. Increase the number of users or events in users active this week who were active last week through better UX, clearer CTAs, and reduced friction in the conversion path.
Qualify the denominator. Ensure last week active users represents the right audience. Better targeting means a higher conversion rate.
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
Ignoring sample size. Small sample sizes produce volatile rates that do not reflect true performance. Ensure you have statistically significant data before drawing conclusions or making changes.
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
Day 30 Retention --- percentage of users active 30 days after signup
Monthly Retention Rate --- percentage of users retained month over month
Day 7 Retention --- percentage of users active 7 days after signup
Cohort Retention Curve --- retention plotted over time for each signup cohort
Product Metrics Cheat Sheet --- complete reference of 100+ metrics