funnel-analysis

Funnel Analysis

The Law

OPTIMISING A FUNNEL BEFORE DIAGNOSING IT OPTIMISES THE WRONG STEP.
"Let's improve the conversion rate" without identifying which step leaks most is guessing — you can improve every step but the broken one and the funnel does not move.
Leak identified + root-caused + hypothesis formed IS a funnel analysis.

When to Use

Trigger before:

• Any CRO (conversion rate optimisation) effort
• Proposing any experiment targeting funnel improvement
• Allocating engineering or design time to "improve" any multi-step flow
• Presenting funnel performance to stakeholders

When NOT to Use

• Single-step actions with no funnel (e.g., a standalone API call — no steps to diagnose)
• Features in pre-launch without traffic data to analyse

The Four Diagnostic Steps

Do not propose solutions before completing all four.

Step 1 — Map the Funnel

List every step between the user's entry point and the conversion goal.

Step 1: [Action] — e.g., "User arrives on landing page"
Step 2: [Action] — e.g., "User clicks CTA"
Step 3: [Action] — e.g., "User completes signup form"
Step 4: [Action] — e.g., "User activates (completes first key action)"
Goal:   [Conversion] — e.g., "User converts to paid"

Use the product's actual steps — not a generic template. Every step must be measurable.

Step 2 — Quantify Each Step

For each step, collect:

• Volume: how many users enter this step per period
• Exit rate: percentage who leave without completing this step
• Time in step: median and p95 time spent before exit

| Step | Entered | Completed | Exit rate | Median time |
|------|---------|-----------|-----------|-------------|
| 1    | 10,000  | 8,100     | 19%       | 2s          |
| 2    | 8,100   | 2,430     | 70%       | 45s         |
| 3    | 2,430   | 2,070     | 15%       | 3min        |
| 4    | 2,070   | 830       | 60%       | 12h         |

The step with the highest exit rate AND the highest volume of exits (exits = entered × exit rate) is the primary leak. Both dimensions matter — a 70% exit rate on 100 users is smaller than a 20% exit rate on 10,000.