Analyze · ICP-Fit & Lead-Quality Analysis
Audit Outbound List for ICP Accuracy
Audit an outbound prospect list before SDRs touch it so you don't waste sequence sends on accounts that will never close.
When to use
Run this every time a new outbound list is built (Apollo, Clay, ZoomInfo, custom). It catches the 20–40% of accounts that look right but aren't, before they enter sequences and tank your sender reputation.
The prompt
You are an analytics-driven head of marketing for a digital marketing agency, doing list QA before outbound goes live. Agency: [AGENCY_NAME] — [SERVICES] ICP: [ICP_DEFINITION] Outbound list (proposed): [LEAD_LIST] — each row: company, industry, employee_count, revenue, geo, tech_stack, signal_used_to_pull Audit this outbound list for ICP accuracy and tell me which accounts to keep, drop, or research further before SDRs touch it. - Score each account on every ICP criterion — show the matrix. - Bucket: Keep (A/B), Research (C), Drop (D). - Group drops by which criterion failed (industry / size / geo / tech / revenue) — this exposes the bad filter that built the list. - Do not invent data — if a field is blank, score 'unknown' and put in Research. - Suggest the corrected list-build query if a single filter is responsible for >30% of drops. 1) Scored matrix. 2) Disposition column. 3) Drop-reason summary (count by failed criterion). 4) Corrected list-build filter recommendation.
Variables
[AGENCY_NAME]Your agency's name[SERVICES]Services offered[ICP_DEFINITION]Current ICP[LEAD_LIST]Proposed outbound list from Apollo, Clay, ZoomInfo, etc.
Example input
Agency: PixelForge — Webflow + CRO for SaaS ICP: B2B SaaS, Series A–B, 30–200 staff, US/EU, $5–50M ARR, on Webflow or HubSpot CMS List (8 accounts): 1. NimbusAI | SaaS | 90 | $14M | US | Webflow | hiring designer 2. Flowstack | SaaS | 25 | $3M | US | Webflow | hiring designer 3. RetailX | DTC retail | 200 | $40M | US | Shopify | hiring designer 4. EuroSync | SaaS | 70 | $9M | DE | HubSpot | funding round 5. BobsBakery | Local | 4 | $300k | US | Wordpress | none 6. Pulsewave | SaaS | 110 | $22M | US | Webflow | new VP marketing 7. Globex | SaaS | 800 | $200M | US | custom | none 8. SiteSpark | SaaS | 45 | unknown | UK | HubSpot | hiring
Example output
Scored Matrix: | Account | Ind | Size | Rev | Geo | Tech | Total | Disposition | | NimbusAI | 3 | 3 | 3 | 3 | 3 | 15 | Keep | | Flowstack | 3 | 1 | 1 | 3 | 3 | 11 | Keep | | RetailX | 0 | 3 | 3 | 3 | 0 | 9 | Drop (industry, tech) | | EuroSync | 3 | 3 | 3 | 3 | 3 | 15 | Keep | | BobsBakery | 0 | 0 | 0 | 3 | 0 | 3 | Drop (industry, size, rev) | | Pulsewave | 3 | 3 | 3 | 3 | 3 | 15 | Keep | | Globex | 3 | 0 | 0 | 3 | 0 | 6 | Drop (size, rev, tech) | | SiteSpark | 3 | 3 | unk | 3 | 3 | 12 | Research | Drop reasons (n=3): industry x2, size x2, revenue x2, tech x2. Corrected filter: add 'CMS in (Webflow, HubSpot)' AND 'employee_count BETWEEN 30 AND 200' to the source query — would have eliminated all 3 drops upfront.
Pro tips
- Run this between list-pull and sequence-load — saves bounce + spam complaint costs.
- Send the 'corrected filter' back to whoever built the list — that's the real fix.
- Track keep-rate over time as a list-quality KPI.
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