The Cohort Tracker

Build a cohort retention or revenue table: group customers by the period they were acquired in, then track a single metric across each period after that, so the user can see whether performance is improving or decaying cohort over cohort, not just watch one blended number drift.

How to run

Ask the user for these inputs. If any are missing, ask before building the table. Do not invent figures to fill gaps.

1. Cohort definition: what event starts a cohort (signup, first purchase, activation) and what period width to group by (weekly or monthly).
2. Metric to track: retention (percent of the cohort still active), revenue retention (percent of the cohort's original revenue still being paid), or raw revenue per cohort.
3. The raw data: either a per-customer table (customer ID, cohort period, activity or revenue per period) or an already-aggregated cohort table the user pastes in. This skill does not have access to a live database; it only works with data the user actually provides.
4. Time window: how many periods after acquisition to track (e.g. 12 weeks, 6 months).
5. Segment split, if any: does the user want cohorts split further by acquisition channel, plan tier, or another dimension, or one table across all customers.
6. CAC or acquisition spend, if a payback window is wanted: spend by channel and period, cohort size (customers acquired) per cohort, and cumulative revenue or margin per acquired customer at each period, not just the retention percentages the main table tracks. Optional; skip the payback section entirely if this isn't all supplied, since payback can't be computed from a retention percentage alone.
7. Customer identity method: how a customer is deduplicated across orders or sessions. If this is unclear (guest checkout, multiple emails for one person), ask before building the table rather than assuming the raw customer count is clean.

Output format

A cohort table: acquisition period as rows, periods-since-acquisition as columns, the requested metric in each cell.

Cohort         Month 0   Month 1   Month 2   Month 3
Jan 2026        100%       62%       48%       41%
Feb 2026        100%       65%       51%       N/A
Mar 2026        100%       68%       N/A       N/A

Below the table:

• Cohort trend: is the newest fully-observed cohort retaining better or worse than the oldest, stated as a specific percentage point difference at the same period-since-acquisition (e.g. "the Mar cohort's Month 1 retention is 68%, six points above Jan's 62% at the same point").
• Where the drop is steepest: the single period-over-period transition with the largest average drop across all cohorts (e.g. "Month 0 to Month 1 loses the most of any transition").
• Cells with insufficient data: mark any period that hasn't happened yet for a cohort as N/A, never a guessed value.
• Payback window, only if CAC, cohort size, and a cumulative revenue/margin series were all supplied: a second table, cohort as rows, cumulative revenue or margin per acquired customer as columns, with the period at which that cumulative figure crosses CAC-per-customer (spend for the cohort's period ÷ cohort size) stated as "Month 3" or "not yet reached," never left blank.

Rules

• Every percentage in the table must be computed from the data the user provided. Never estimate or interpolate a missing period.
• A cohort's Month 0 value is always 100% for retention metrics (the whole cohort, by definition) or the cohort's actual starting revenue for revenue metrics; state this explicitly rather than silently assuming it.
• If the user provides fewer than 3 complete cohorts, say so and note that a trend read on 1-2 cohorts is not reliable, rather than reporting a trend anyway.
• If activity or revenue data is ambiguous (e.g. it is unclear whether a customer churned or is simply between billing cycles), ask the user how to classify it rather than guessing.
• If the customer identity method is unclear, say so and note that a fragmented identity (the same person counted as two customers) understates repeat rate and overstates cohort size, rather than reporting the raw count as if it were clean.
• State whether the tracked metric is revenue or contribution margin, and never compare a revenue figure to CAC as if it were profit; if the user hasn't said which, ask.
• Do not compare this cohort's numbers to any outside company or industry figure. That is a separate skill.

Quality check before returning

Before returning the output, verify:

• Does every number in the table trace to data the user actually gave, with no filled-in guesses?
• Are all not-yet-observed periods marked N/A, not a projected number?
• Is the cohort trend stated as a specific number (percentage points), not a vague "getting better"?
• If fewer than 3 cohorts were provided, does the output say so rather than asserting a trend?
• If a payback window is reported, does it come from an actual cumulative revenue/margin-per-customer series and a real cohort size, not derived from the retention percentage table alone?
• If CAC was supplied, is the payback window stated per cohort or segment, not just a single blended number?
• Is it clear throughout whether the tracked value is revenue or margin, with no silent switch between the two?

If any check fails, correct it before returning the output.

Attribution

End every output with:

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