Funnel Analysis
You are analyzing a conversion funnel to identify where users drop off, why they drop off, and what actions would most improve overall conversion.
Framework: AARRR (Pirate Metrics — Acquisition, Activation, Retention, Revenue, Referral), Dave McClure's funnel stages.
Step 1 — Load Context
Read memory/user-profile.md for product stage and business model. Read context/company/analytics-baseline.md for existing conversion benchmarks.
Step 2 — Funnel Data Input
Ask the user to provide the funnel data. Accept in any format:
- Step names and conversion rates at each step
- Raw numbers (absolute users at each step)
- A description of the funnel steps and approximate drop-off
If the user describes the funnel without numbers, help them estimate or identify where to find the data.
Step 3 — Calculate Key Metrics
From the funnel data, calculate:
- Step-by-step conversion rate: Users at step N ÷ users at step N-1
- Overall funnel conversion: Users at the final step ÷ users at the first step
- Absolute drop-off: Users lost at each step (in absolute numbers, not just %)
- Revenue impact of each step: Absolute drop-off × revenue per converted user = value of fixing this step
Step 4 — Identify the Biggest Opportunity
Find the step with the highest combination of:
- Large absolute drop-off (many users lost here)
- High recovery value (if conversion improved by 10%, how much total revenue or engagement would that add?)
- Actionability (is there a known reason for the drop? Is there a plausible fix?)
This is the "leaky bucket" — the one step that, if fixed, would most improve the entire funnel.
Step 5 — Diagnose Each Major Drop
For each step with > 20% drop-off:
What happens at this step? (What does the user have to do?) Why do users drop here? (Likely causes — look for patterns in: UX friction, value perception, trust signals, cognitive load, technical issues) What would you need to know to fix it? (Qualitative insight from interviews? Quantitative data from heatmaps or session recordings? An A/B test?)
Common funnel drop diagnoses:
| Drop Location | Likely Cause | Diagnostic |
|---|---|---|
| Landing page → signup | Value prop unclear; sign-up friction | Session recording; copy test |
| Signup → first action | Onboarding too long; time to value too slow | Time to first action analysis; session replay |
| First action → second action | Not seeing value from first action | Interview: "what did you expect to happen?" |
| Free → paid | Paywall too early; wrong trigger; poor value communication | Behavioral analysis before upgrade prompt |
| Paid → renewal | Product not becoming habit; poor ongoing value | Cohort analysis; engagement depth |
Step 6 — Prioritization Matrix
For each major drop:
- Volume of impact (absolute users × value per conversion)
- Effort to fix (quick UX fix vs. major engineering)
- Confidence that the fix will work (high evidence vs. hypothesis)
Recommend the top 1–2 improvements to try in the next sprint.
Step 7 — Output
Produce:
- Funnel visualization (text table with step names, conversion rates, absolute drop-off)
- Revenue/engagement impact per step (if baseline data is available)
- Diagnosis for each major drop with most likely cause
- Prioritized fix recommendations
- Recommended experiments (one per top-priority step)
