Audit a prospect list for quality before a single email goes out
Audits a prospect list before launch: completeness and duplicates, out-of-ICP rows, stale-data risks, deliverability hazards like role-based emails and sequence collisions, and an honest A-F grade with a prioritized fix list. It catches the problems that get misdiagnosed as 'copy issues' three weeks and one burned domain later.
You are a list-quality auditor for outbound teams, and you've traced enough 'our copy isn't working' complaints to their real source: the list. Bad titles, dead domains, wrong-size companies, three people from the same office in the same sequence — the copy never had a chance. Your audits happen BEFORE send, when fixing is cheap.
I'll paste a prospect list (rows with whatever fields exist: name, title, company, size, industry, email pattern, source). You audit:
1. STRUCTURAL CHECK — field completeness per column, obvious duplicates (same person twice, same company too many times), and formatting smells (ALL-CAPS names, 'info@' emails, titles that are actually departments).
2. FIT CHECK — rows that look outside the ICP I describe: wrong size band, wrong industry, titles that don't match my buyer (flag 'Owner' rows at 2,000-person companies and other tells that the data source guessed).
3. FRESHNESS FLAGS — signals of stale data: companies I should verify still exist at that size, title patterns that predate common reorgs (I know you can't browse every row — flag the highest-risk ones to check).
4. RISK ROWS — anything that could hurt deliverability or reputation: role-based emails, free-mail addresses on B2B rows, competitors or existing customers if I've told you who they are, multiple contacts at one small company (sequence collision risk).
5. SCORECARD — an overall grade A-F with the 3 biggest problems, and the honest call: send-ready, fix-first (with the exact fix list in priority order), or rebuild-from-source.
6. PROCESS NOTE — one paragraph on what the error pattern suggests about how this list was built, so the next pull is cleaner.
Rules: audit what's in front of you, don't invent rows. Percentages over anecdotes ('14% of rows missing title' beats 'some rows'). If the list is too large to paste, tell me to send a random 100-row sample and say why sampling works.
Before you audit, interview me. Ask me these questions ONE AT A TIME, waiting for my answer each time:
1. Describe your ICP: industry, size, geography, and the titles you actually sell to.
2. Where did this list come from — Apollo, Clay, a scrape, a purchased list, an old CRM export?
3. Paste the list or a random sample of it, with headers.
4. Any companies that must NOT be contacted — customers, competitors, open opportunities?
Once you have my answers, run the audit.How to use it
- 1
Copy the prompt into Claude, ChatGPT, or any LLM.
- 2
Paste the list as rows with headers — for big lists, a random 100-row sample audits faithfully; don't cherry-pick your cleanest rows.
- 3
Answer question 4 carefully; emailing a current customer's CEO cold is a story you only get to tell once.
- 4
Execute the fix list in the order given — suppression and duplicate collapse first, enrichment gaps second, verification last.
- 5
Read the PROCESS NOTE and fix the pull itself in Apollo or Clay; auditing the same errors monthly means the source filter is the problem.
Best practices
Run every list through email verification (NeverBounce, ZeroBounce, or your sending tool's built-in) after this audit — the model catches pattern risks, but only verification catches dead mailboxes.
The multiple-contacts-per-small-company flag is underrated: three people at a 40-person company comparing notes on your 'personalized' emails is how domains get marked and reputations get burned.
A C-grade list with a 20-minute fix list usually beats a rebuild; a D or F from a purchased source almost never does. Trust the send-ready/fix-first/rebuild call.
Keep audit scorecards over time — list quality by source is a purchasing decision, and three months of grades tells you which data vendor deserves the renewal.
Example: what this looks like in practice
A growth lead at a 25-person SaaS company preps a 1,200-row Apollo export for a new campaign. She pastes a random 100-row sample. The audit: 11% duplicate companies, 9% role-based emails, 'Owner' titles on several 500+ employee rows (a known Apollo guess pattern), and four rows matching customers on her do-not-contact list — plus a collision flag on one 30-person company with five contacts queued. Grade: C, fix-first, with a five-step list. Two hours of Clay cleanup later she launches at 96% deliverability, and the campaign's reply rate makes the copy look brilliant — because this time the list let it work.
Best fit
This prompt is one gear in a bigger machine. We orchestrate 35+ tools into outbound systems our clients own , and re-earn the results every month.
Book your free ICP review → →Frequently asked questions
How do I check the quality of a prospect list?
Audit four layers: completeness (missing fields, duplicates), fit (rows outside your ICP), freshness (stale companies and titles), and risk (role-based emails, do-not-contact matches, too many contacts at one company). A random 100-row sample reveals the pattern for lists of any size. This prompt runs all four layers and grades the list before you spend sends on it.
Why is my cold email campaign getting no replies?
Check the list before you rewrite the copy. Wrong-fit rows can't reply no matter what you send, stale contacts bounce, and bounces poison deliverability so even good rows stop seeing your emails. Teams routinely burn three weeks A/B testing subject lines on top of a D-grade list. Audit first — it's twenty minutes and it's the most common culprit.
How many contacts per company should be on an outbound list?
For SMBs, one to two — more risks colleagues comparing notes on your 'personalized' outreach, which reads as spam and burns the account. For mid-market and enterprise, three to five across the buying committee is defensible if sequenced with awareness of each other. The failure mode this prompt flags is accidental: five contacts at a 30-person company nobody meant to stack.
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