Gro Funnel Analysis

When to use

Use this when you have an ordered sequence of steps users must complete (visit → signup → activate → pay) and you want to know where you lose the most people and what to do about it. Reach for it before any redesign, when a metric drops, or when leadership asks "where should we invest?"

Not for: unordered behavior exploration (use gro-cohort-analysis), long-run repeat usage (gro-retention-analysis), or deciding whether a change worked (gro-ab-test-design). Funnel analysis finds where; those find why over time and whether.

Method

1. Define the funnel as ordered, mutually exclusive steps with an explicit denominator and time window (e.g. "of users who signed up this week, % reaching each step within 7 days"). Decision point: use unique users per step, not events, unless you specifically study repeat actions.
2. Pull counts per step. Compute step-to-step conversion (step N+1 / step N) AND overall conversion (step N / step 1).
3. Rank steps by absolute users lost (stepN − stepN+1), not by percentage. A 10% drop on a huge step often beats a 60% drop on a tiny one.
4. For the top loss step, segment it: device, source, new vs returning, geo, plan. Decision point: if one segment drives most of the loss, fix that segment; if loss is uniform, the step itself is broken.
5. Diagnose root cause with session data, error logs, or a quick user watch. Classify as friction (too hard), motivation (unclear value), or timing (asked too early).
6. Prescribe ONE fix mapped to the cause, estimate uplift (recovered users × downstream conversion × value), and hand to gro-ab-test-design if the change is risky.

Example

Signup 10,000 → Verified email 6,000 (−4,000) → Activated 5,400 → Paid 540. Biggest raw loss is email verification (4,000 users). Segmenting: mobile verification converts 45%, desktop 78%. Root cause: the verification link opens in a different browser on mobile, dropping the session. Fix: 6-digit code instead of a link. Estimated recovery: ~2,000 mobile users × 9% paid = ~180 new paying users.

Pitfalls

• Ranking by percentage instead of absolute loss, so you optimize a step that barely matters.
• Mixing time windows across steps (lifetime signups vs 7-day activation) — conversions become meaningless.
• Treating the funnel as strictly linear when users skip or loop; validate the real path first.
• Proposing five fixes at once so you can never attribute the uplift.

Output format

Funnel: <name> | Window: <N days> | Denominator: <cohort>
Step            Users    Step CR   Overall CR
1 <step>        10000    —         100%
2 <step>         6000    60%        60%
...
Biggest drop:   <step> → <step> (−<X> users, <Y>%)
Top segment:    <segment> (<CR> vs <baseline CR>)
Root cause:     <friction | motivation | timing> — <detail>
Recommended fix: <one change>
Est. impact:    <recovered users> → <downstream value>
Next: <ship | A/B test via gro-ab-test-design>