Matrixx0070 When to use
Use this when an aggregate metric hides what's really happening — a flat top-line can mask new cohorts getting worse while old ones prop it up. Reach for it to compare cohorts over time, isolate the effect of a launch, or answer "is the product getting better for users we acquire now?"
Not for: the specific retention-lever workflow (use gro-retention-analysis, which is a cohort analysis specialized to retention), single-step drop-off (gro-funnel-analysis), or proving causation of one change (gro-ab-test-design). This is the general mechanic; the others are lenses on it.
Method
- Choose the cohort key — usually signup period (acquisition cohort), but sometimes a behavioral cohort (users who did action X) or a source cohort. Decision point: acquisition cohorts answer "are we improving over time?"; behavioral cohorts answer "does behavior X matter?"
- Choose the metric per cell: retention %, revenue, orders, or any per-user value. Pick the time grain (day/week/month) matching product frequency.
- Build the matrix: rows = cohorts (by start period), columns = age (periods since start), cells = the metric. Every cell in a column is the same age, which is what makes cohorts comparable.
- Read it three ways: down a column (are newer cohorts better or worse at the same age? — the product-health trend), across a row (how a single cohort evolves — decay or growth), and the diagonal (calendar effects like a launch or seasonality hitting all cohorts at once).
- Decision point: if newer cohorts underperform older ones at equal age, something regressed (or you're acquiring worse users) — investigate acquisition source mix and recent product changes.
- Normalize by cohort size (use rates, not raw counts) so a big cohort doesn't dominate, and isolate the driver before recommending action.
Example
Monthly signup cohorts, cell = % still active. Reading down the month-3 column: Jan cohort 30%, Feb 28%, Mar 21%. Newer cohorts decay faster at the same age. The diagonal shows no shared calendar dip, so it's not seasonality. Cause: a Feb paid-ads push brought lower-intent users. Action: shift spend back to higher-retaining channels; confirm with the next cohort.
Pitfalls
- Comparing cohorts at different ages (a 6-month-old vs a 1-month-old cohort) — always compare down a column.
- Using raw counts so the largest cohort dominates; use per-user rates.
- Reading only the diagonal (calendar) and missing the column trend (product health), or vice versa.
- Too-small cohorts producing noisy cells you over-interpret; bucket up until each cell is stable.
Output format
Cohort key: <signup month/behavior/source> | Metric: <retention/revenue> | Grain: <week/month>
Cohort \ Age 0 1 2 3 ...
<Jan> 100% 45% 35% 30%
<Feb> 100% 43% 32% 28%
<Mar> 100% 40% 28% 21%
Column read (health): <newer better/worse at same age>
Row read (lifecycle): <decay/growth shape>
Diagonal (calendar): <launch/seasonality effect or none>
Finding: <what changed + likely driver>
Action: <recommendation> | Confirm: next cohort / gro-ab-test-design