Campaign Attribution Readout
Turn a pasted campaign results export into an honest readout — what influenced pipeline, where the in-market accounts actually came from, and what to do next.
What the campaign created vs. what was already in-market
| Credit type | Pipeline |
|---|---|
| Campaign-activated | $244k (58%) |
| Already in-market | $176k (42%) |
Overview
Take a pasted campaign results export and produce an attribution readout that cross-checks the campaign's influenced accounts against HG intent — distinguishing accounts the campaign genuinely activated from accounts that were already in-market — so the readout credits the campaign honestly.
Use cases
Attribution leadership believes
Influenced-pipeline numbers get discounted because everyone knows they're inflated. Splitting activated from already-in-market gives a credible number that survives scrutiny.
Invest where the campaign actually works
Knowing which segments the campaign genuinely activated — not just touched — tells you where to put the next quarter's budget.
View workflow prompt
# Campaign Attribution Readout
## Parameters
- `{{campaign_results}}` *(required)* — Pasted campaign results (campaign, accounts touched, MQLs, influenced pipeline $, account domains). Example: `Q2 Observability Webinar - 184 accounts - 31 MQLs - $420k influenced`
- `{{category}}` *(required)* — Category whose intent contextualizes attribution. Example: `observability`
## Purpose
Produce an honest attribution readout from {{campaign_results}} by cross-checking influenced accounts against {{category}} intent — so the team can tell which results the campaign created versus which accounts were already shopping and would have converted anyway.
## Process
1. **Parse results** — read {{campaign_results}} (accounts, MQLs, influenced pipeline).
2. **Intent timing check** — `company_intent`/`intent_category` for influenced account domains; note which were already in-market before the campaign (pre-existing intent) vs. which look campaign-activated.
3. **Honest credit** — split influenced pipeline into 'campaign-activated' (no prior intent) and 'campaign-touched' (already in-market) — the readout credits both but distinguishes them.
4. **Segment performance** — `company_firmographic` to see which segments the campaign worked best in.
5. **Next-action** — recommend what to double down on and what to cut based on the honest read.
## Output Format
Markdown with:
- `# Campaign Attribution Readout`
- `## Headline Numbers` (from the export)
- `## Activated vs. Touched` (intent-cross-checked credit split)
- `## Segment Performance` (where it worked)
- `## Next Actions` (double down / cut)
- `## Citations`
## Quality Checklist
- Activated-vs-touched split cites `company_intent` timing
- The readout credits the campaign without overclaiming pre-existing intent
- Segment performance cites `company_firmographic`
- Next actions follow from the honest read