Analysis / Marketing performance and attribution
Find conversion losses in the marketing funnel
Use Proforma to find conversion losses in the marketing funnel. Produce marketing conversion funnel and segment drop-off investigation table from your business data.
Marketing leader · Growth analyst
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Your starting prompt.
Analyze marketing funnel conversion for cohorts entering during [period]. Calculate counts, conversion rates and time between stages under our supplied definitions, deduplicating records by stable ID. Compare [segments] and identify the largest numerical drop-offs with sample sizes. Keep recent incomplete cohorts separate, show tracking gaps and prepare investigation questions without claiming that a funnel stage caused the observed loss.
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Method and review
Keep the assumptions visible.
Align attribution windows, conversion definitions and currencies. Compare observed costs and outcomes; use a suitable experiment before describing an effect as causal.
For this task, check
A tracking gap between stages can resemble a real conversion drop-off.
Before you begin
- Uploaded exports or permitted Data covering the listed inputs
Work with permitted files or queryable data. Verify the result against the original records.
Synthetic illustrative example
See the method in practice.
Synthetic input. Illustrative output checked locally; this is not a live Proforma run or customer result. One fully observed cohort; counts already deduplicated by stable ID and constrained to sequential stage entry.
These invented records explain the method. They are not a recorded Proforma run.
| Stage | Unique accounts | Window |
|---|---|---|
| Visit | 100 | Closed |
| Lead | 20 | Closed |
| Qualified lead | 8 | Closed |
| Opportunity | 4 | Closed |
| Transition | Converted / eligible | Conversion | Did not advance |
|---|---|---|---|
| Visit → lead | 20 / 100 | 20.0% | 80 |
| Lead → qualified | 8 / 20 | 40.0% | 12 |
| Qualified → opportunity | 4 / 8 | 50.0% | 4 |
Method
Stage conversion = accounts entering the next stage / accounts at the prior stage in the same cohort. Drop-off counts are the difference. Aggregate counts cannot recover stage duration or diagnose identity-tracking gaps; event-level data is needed.
What the result tells you
The largest numerical drop is before lead creation, but that alone does not establish its cause or commercial priority. No stage is blamed and no recent incomplete cohort is mixed into this result.
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