Cohort analysis
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Grouping users by when they signed up and tracking their behavior over time to identify trends and measure the impact of changes.
Cohort analysis groups users by a shared characteristic (usually signup date) and tracks their behavior over time. Instead of looking at all users in aggregate, you look at the January cohort, the February cohort, and the March cohort separately.
This matters because aggregate metrics hide trends. If your overall retention is 20%, is that good or bad? Cohort analysis reveals: the January cohort retains at 15%, February at 20%, and March at 28%. Retention is improving. Something you changed is working.
Cohort analysis is the tool for measuring the impact of product changes. You ship a new onboarding flow in March. The March cohort retains 8 points higher than February. That is evidence the change worked. Aggregate metrics would not show this because older cohorts dilute the signal. Pair cohort analysis with activation metrics to understand not just who stays, but who reaches the moment of value.
Examples
A PM uses cohort analysis to evaluate a feature launch.
A new activation flow launched on March 1. The March cohort's 7-day activation rate is 45%, up from 32% in February. The PM attributes the improvement to the new flow and continues iterating.
A team discovers a seasonal pattern through cohort analysis.
Cohorts from January and September (start of budget cycles) retain 30% better than cohorts from June and December. The team adjusts acquisition spend to focus on high-retention months.
Cohort analysis reveals a problem with a specific channel.
Users acquired through paid ads retain at 10%. Users from organic search retain at 30%. Users from referrals retain at 45%. The team realizes paid ads are attracting low-intent users and reallocates budget to content marketing.
Frequently asked questions
What is the most common type of cohort analysis?
Time-based cohorts grouped by signup month. You track metrics like retention, revenue per user, and feature adoption for each monthly cohort. This reveals trends over time and measures the impact of changes you made in specific months.
How is cohort analysis different from segmentation?
Segmentation divides users by characteristics (industry, company size, plan type). Cohort analysis divides users by time (when they signed up). Both are valuable. Segmentation tells you who behaves differently. Cohort analysis tells you when behavior changed.
Related terms
A chart showing what percentage of users continue using a product over time, revealing whether the product has lasting value.
Tools and practices for tracking how users interact with a product to inform decisions about features, onboarding, and growth.
The moment when a new user experiences the core value of a product for the first time, making them likely to return.
Metrics tracking how many unique users engage with a product daily and monthly, used to measure engagement and stickiness.
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