Cohort Retention Calculator
A starting cohort and how many are still active give retention and churn for that group — the truest read on whether what you ship keeps people. Your own cohort numbers.
How much of a cohort is still around
The formula for cohort retention is: Retention Rate = (Number of Active Users from Cohort at End of Period / Total Number of Users in Cohort at Start) × 100%. Variables: - 'Number of Active Users from Cohort at End of Period' (also called 'retained count'): The count of users from the initial cohort who remain active (e.g., logged in, made a purchase, or performed a key action) during the specific time period (e.g., Week 4, Month 3). - 'Total Number of Users in Cohort at Start' (also called 'cohort size'): The number of users who entered the cohort during the initial time window (e.g., all users who signed up in January 2024). Why it's calculated this way: This ratio directly measures the proportion of an original group that continues to engage. It isolates the effect of your product or service on user behavior because it tracks the same set of users over time, unaffected by new user acquisition. The retention rate is computed per time period (e.g., weekly or monthly) to reveal how stickiness decays. Churn rate is simply 100% minus the retention rate for that period. This method is mathematically sound because it uses a fixed denominator (the initial cohort size) and a numerator that counts only those still active, avoiding double-counting or dilution from later signups.
SaaS Startup Monthly Retention
A project management software company signs up 500 users in March 2024. They define 'active' as logging in at least once in a calendar month. For Month 1 (April 2024), they find 350 of those 500 users logged in. Retention rate = 350 / 500 × 100% = 70%. For Month 2 (May 2024), only 200 of the original 500 logged in. Retention rate = 200 / 500 × 100% = 40%. Churn from Month 1 to Month 2 is 100% - 40% = 60% (or alternatively, the drop from 70% to 40% shows a 30 percentage point loss).
E-commerce Weekly Purchase Retention
An online clothing store runs a promotion in Week 1 of June, gaining 1,200 new customers. They define 'active' as making a purchase in a given week. In Week 2, 180 of those original 1,200 make a purchase. Retention = 180 / 1,200 × 100% = 15%. In Week 3, only 60 purchase. Retention = 60 / 1,200 × 100% = 5%. The steep drop indicates low repeat purchase rate after the initial promotion. Churn rate from Week 2 to Week 3 is 100% - 5% = 95% (or relative to the 15% retention, a 10 percentage point loss).
Mobile App Daily Active Retention
A meditation app gets 3,000 new downloads on August 1. They define 'active' as opening the app on a given day. On Day 1 (the same day), 2,400 users open it. Retention = 2,400 / 3,000 × 100% = 80%. On Day 2, 900 users open it. Retention = 900 / 3,000 × 100% = 30%. By Day 7, only 150 users open it. Retention = 150 / 3,000 × 100% = 5%. This shows rapid drop-off typical of daily active metrics. Churn from Day 1 to Day 7 is 100% - 5% = 95%.
A 'good' cohort retention rate varies dramatically by industry, business model, and the specific time period measured. There are no universal benchmarks. For SaaS, a Monthly Retention Rate above 90% is excellent (implying churn below 10%), but many B2B SaaS tools see 80-90% retention in early months. For e-commerce, weekly purchase retention of 10-20% can be strong, while daily active retention for mobile apps often drops to 5-15% by Day 7. The key is to track your own trends: a retention rate that is stable or improving over time indicates product-market fit and effective user engagement. A declining retention curve that flattens (e.g., stabilizes at 30% after Month 3) is healthier than one that keeps dropping to zero. Compare retention across cohorts (e.g., users who signed up in different months) to see if changes you've shipped improve stickiness. Avoid comparing raw percentages across different business types without adjusting for the definition of 'active' and the time period. A high retention rate on a very short time frame (e.g., 90% Day 1 retention) is common and not necessarily indicative of long-term success. Focus on longer-term periods (e.g., Month 6, Month 12) for a truer read on sustained engagement.
Common mistakes include: (1) Using a denominator that changes over time, such as dividing by the number of users who were active in a previous period instead of the original cohort size, which inflates retention. (2) Counting users as retained if they were active at any point in the period, but not ensuring they were truly from the original cohort—this can happen if user IDs are reused or if the data pipeline mislabels. (3) Defining 'active' too loosely (e.g., any page view) versus a meaningful action (e.g., completing a core task), which can make retention appear artificially high. (4) Not accounting for seasonality: a cohort starting during a holiday may have different retention than one starting mid-year. (5) Ignoring users who churn and return: if someone is inactive for months but then becomes active again, they should be counted as active in that period, but some tools incorrectly exclude them. (6) Using too short a time window for the period (e.g., hourly retention) when the product is not used that frequently. (7) Failing to segment cohorts by acquisition channel or user type, which can mask poor retention in specific segments.
- Cohort
- A group of users who share a common characteristic, typically the time period they first started using a product or service.
- Retention Rate
- The percentage of users from a cohort who remain active during a specific time period, calculated as retained users divided by the original cohort size.
- Churn Rate
- The percentage of users from a cohort who stop being active during a given time period, calculated as 100% minus the retention rate for that period.
- Active User
- A user who performs a predefined meaningful action (e.g., login, purchase, session) within a given time period, as defined by the business.
- Retention Curve
- A graph plotting retention rates over successive time periods for a single cohort, showing how user engagement decays over time.
How do I define a cohort for this calculator?
A cohort is typically defined by a time window (e.g., all users who signed up in a specific month or week). You can also define it by acquisition source or behavior, but the time-based cohort is most common for retention analysis.
What is the difference between retention rate and churn rate?
Retention rate is the percentage of users still active; churn rate is the percentage who have stopped being active. They always add up to 100% for the same period and cohort.
How do I handle users who were inactive for a period but then return?
Count them as active in any period they perform the defined action. This is the standard approach and reflects true engagement. Do not exclude them just because they were inactive earlier.
What size should my cohort be for reliable results?
Larger cohorts (hundreds or thousands) give more stable and reliable retention rates. For very small cohorts (e.g., 10 users), a single user's activity can swing the percentage dramatically, so interpret with caution.
Can I compare retention rates across different time periods?
Yes, but only if the time periods are the same length (e.g., monthly retention for Month 1 vs. Month 1 of a different cohort). Comparing Day 1 retention to Month 1 retention is not meaningful.
What if I have a subscription business with cancellations?
Define 'active' as having an active paid subscription at any point during the period. If a user cancels but still has access for the rest of the billing period, decide whether to count them based on your definition of 'active'.
How often should I calculate cohort retention?
It depends on your product usage cycle. For daily active products, calculate daily or weekly. For monthly active products, calculate monthly. Track at least 3-6 periods to see the retention curve.
What does it mean if my retention rate goes up in a later period?
This is unusual but can happen due to seasonality, a win-back campaign, or data errors. Check if your definition of 'active' changed or if the cohort size was incorrectly counted. Typically, retention decreases over time.
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