Funnel analysis

When this is useful

You are about to run this process and need one reusable working document.

What the workbook covers

Measurement definition

Funnel

Scenario comparison

Completed fictional example

Investigation and intervention brief

Final checks

Use with real evidence

Download the full Markdown resource, replace placeholders with your situation and retain sources beside claims. Keep unanswered questions visible rather than filling them with plausible guesses.

Put it to work

1. Read the full worksheet and define the decision it supports.
2. Fill the relevant tables using dated observations and explicit assumptions.
3. Review the output with its owner and record the next decision.

Limits and tradeoffs

The worksheet structures work; it does not supply missing customer evidence or guarantee a business result. Adapt examples and assign a responsible owner.

Worksheet preview

# SaaS funnel analysis worksheet

Copy into your preferred editor. Define units, progression and cohort maturity before calculating. A scenario is a model with assumptions, not a forecast or proof of achievable lift.

## Measurement definition

- Decision this analysis should inform:
- Unit: user / account / another defined unit:
- Eligible starting population:
- Cohort entry date/range:
- Observation window and minimum cohort age:
- Required order of events:
- Identity matching approach and limitations:
- Excluded internal/test records:
- Data sources and known definition changes:
- Validation checks performed:

## Funnel

| Step | Exact event / condition | Eligible count | Conversion from previous step | Quality / data concerns |
|---|---|---|---|---|
| Start | | | — | |
| Step 2 | | | | |
| Step 3 | | | | |
| End | | | | |

Step conversion = next-step count / eligible previous-step count. Use rates as decimal fractions in multiplication: 5% = 0.05.

## Scenario comparison

| Candidate change | Baseline rate | Scenario rate | Expected downstream outcome | Assumptions / evidence | Effort / risk |
|---|---|---|---|---|---|
| | | | | | |

Relative lift = (new rate - baseline rate) / baseline rate. Percentage-point change = new percentage - baseline percentage. Relative lift is undefined when baseline is zero; investigate counts and measurement instead of dividing by zero.

## Completed fictional example

Ordered mature cohort with resolved user identities: 2,000 visitors → 100 signups → 40 activated → 10 paid. The rates are 5%, 40%, and 25%; overall conversion is 0.5%.

| Scenario | Calculation | Expected paid customers |
|---|---|---:|
| Baseline | 2,000 × 0.05 × 0.40 × 0.25 | 10 |
| Signup rate reaches 6% | 2,000 × 0.06 × 0.40 × 0.25 | 12 |
| Activation reaches 50% | 2,000 × 0.05 × 0.50 × 0.25 | 12.5 |
| Activated-to-paid reaches 35% | 2,000 × 0.05 × 0.40 × 0.35 | 14 |

Fractional outcomes are model expectations. Real customer counts are whole numbers. These scenarios hold other rates and traffic constant; changes can affect audience quality and downstream rates. Figures are not benchmarks.

## Investigation and intervention brief

- Where the experience appears to fail:
- Evidence from data QA:
- Evidence from successful and unsuccessful users:
- Proposed explanation and alternatives:
- Change to investigate or make:
- Expected outcome and assumptions:
- Quality/reliability guardrails:
- Owner, dependencies and effort:
- Review method and window:
- Result, confidence and next decision:

## Final checks

- Same unit and comparable maturity across steps?
- Counts reflect ordered progression rather than unrelated monthly totals?
- Missing, duplicate and internal events checked?
- Impact compared with effort, evidence and risk?
- Small-sample or before/after evidence described honestly?


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Original resource by Jon Bishop. CC BY 4.0. Illustrative examples are not customer results.