Cohort Retention Analysis

Read a cohort table properly: where retention flattens, which cohorts differ and why, and what the curve says about the business.

Cohort data:
"""
[PASTE THE COHORT TABLE: ROWS AS SIGNUP COHORTS BY MONTH OR WEEK, COLUMNS AS PERIODS SINCE SIGNUP, VALUES AS RETAINED USERS OR REVENUE. INCLUDE COHORT SIZES.]
"""

Analyze the cohort data above.

Business and what a retained user does: [THE PRODUCT + THE ACTION THAT COUNTS AS RETAINED]
What changed and when: [PRICING, ONBOARDING, ACQUISITION CHANNELS, PRODUCT LAUNCHES, WITH DATES]
The decision this analysis informs: [WHAT WE WOULD DO DIFFERENTLY]

Produce:

1. **Read the curve.** For the most recent complete cohorts: retention at each period, where the steepest drop happens, and whether the curve flattens. A flattening curve means a stable core exists; a curve still declining at the last period means it does not, and that distinction matters more than any single retention number.

2. **Cohort comparison.** Which cohorts perform better or worse, by how much, and at which period the difference first appears. Where the timing lines up with something I listed, say so as a candidate explanation and name what would confirm it. Correlation with a launch date is a hypothesis, not a cause.

3. **Immature cohorts.** Which cohorts are too young to judge, and which cells are incomplete. Excluding them or flagging them is required; a recent cohort with two periods of data will always look better or worse than it will turn out to be.

4. **Where the losses concentrate.** Absolute users lost per period, not just percentages. The largest percentage drop and the largest user loss are often in different places, and only one of them is worth a project.

5. **Segment questions.** The three cuts most likely to explain the differences (channel, plan, first action taken, company size), and what data I would need to run them.

6. **What this implies.** If current retention holds, what it means for payback, growth, and what has to be true for the business to compound. Then the single most valuable retention experiment given where the curve actually bends.

Rules: use only the numbers I pasted, and show your arithmetic. Flag any cell where the cohort size is small enough that the percentage is noise. If the table cannot support a conclusion I am asking for, say so.

How to use

Whether the curve flattens is the one thing to get from this. A retention curve that flattens means the product has a real core to grow from, and one that keeps declining means growth is a leaky bucket regardless of what acquisition does. The immature-cohort rule prevents the most common misread: the newest cohort is the one everyone looks at first and the one with the least data behind it, and it is usually the basis for a premature celebration.

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