Retention Analysis Template for Product Analytics

It varies by product type. For B2B SaaS with daily-use workflows: D1 40%+, D7 25%+, D30 15%+. For B2B SaaS with weekly use: D7 30%+, D30 20%+, D90 10%+. These are rough benchmarks. The most important comparison is against your own prior cohorts, not against industry averages.

For multi-seat B2B products, measure both. User retention tells you whether individuals find the product valuable. Account retention tells you whether the paying entity continues to pay. An account can have declining user retention (fewer team members active) while still being "retained" (still paying). User retention is a leading indicator of account churn. The [churn rate glossary entry](/glossary/churn-rate) covers both user and revenue churn.

Define a clear inactivity window that counts as churned. For daily-use products, 14 days of inactivity. For weekly-use products, 30 days. For monthly-use products, 60 days. Users who return after the window are "resurrected," not "retained." Track resurrection separately from retention.

Monthly retention rate + monthly churn rate = 100% (approximately; they are complements). Retention rate measures who stayed. Churn rate measures who left. Use retention for cohort-level analysis (how is retention improving over time?) and churn rate for aggregate business health (what % of revenue or users are we losing per month?).

At minimum, 4-6 cohorts of at least 200 users each. With fewer cohorts, random variation dominates. With smaller cohorts, segment-level analysis becomes unreliable. If your user volume is low, use monthly cohorts instead of weekly to get larger sample sizes per cohort. ---