Copy-paste formulas to enrich a Smartlead export in Google Sheets
Paste a formula into row 2, test it on a few rows, then drag down to run the workflow across your export.
Company research from domain
A: company · B: domain · C: notes
=GPT("Research this account for cold email: " & A2 & " (" & B2 & "). Notes: " & C2 & ". Return a 2-sentence summary, likely pain point, and one specific personalization hook. If evidence is weak, say Needs manual research.")
Fit / sendability score
A: company · B: ICP criteria · C: source text
=GPT("Score this lead 1-5 for ICP fit. Company: " & A2 & ". ICP: " & B2 & ". Source: " & C2 & ". Return score, reason, confidence, and whether it should stay in the sequence.")
Personalized first line
A: contact · B: hook · C: offer · D: tone
=GPT("Write one personalized cold-email first line for " & A2 & " using this hook: " & B2 & ". Offer: " & C2 & ". Tone: " & D2 & ". Keep it factual, specific, under 30 words, no fake familiarity.")
QA / spam-risk flag
A: first line · B: required fields · C: source text
=GPT("QA this cold-email line: " & A2 & ". Required fields: " & B2 & ". Source: " & C2 & ". Flag missing data, unsupported claims, spammy phrasing, and pass/review/fail.")
Short answer
Enriching a Smartlead export in Google Sheets means cleaning, researching, scoring, and personalizing your contact list before it goes back into a sending tool. Instead of sending a raw CSV, GPT for Sheets runs prompts across every row to add company research, ICP fit scores, and personalized first lines in adjacent columns.
This keeps source data, AI output, and QA labels in one reviewable sheet, so you catch thin or risky rows before they ever hit a sequence.
Fastest path: Install GPT for Sheets → paste your export → add a research formula → review 10 rows → fill down → reimport clean contacts.
Workflow
A practical Smartlead enrichment sheet usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Company name | Row anchor for each account |
| B | Domain / website | Grounds research in an inspectable source |
| C | Contact and role | Targets personalization |
| D | Notes / source text | Keeps the AI grounded in evidence |
| E | AI company summary | First useful interpretation |
| F | ICP fit score | Decides who stays in the sequence |
| G | Personalized first line | Drives reply rate |
| H | QA / spam-risk flag | Stops weak rows before sending |
Step-by-step setup
- Start with 10 representative rows before filling down the full export.
- Keep raw Smartlead fields unchanged so you can audit changes.
- Run a research formula, then inspect low-confidence rows.
- Score ICP fit and drop or pause rows below your threshold.
- Add a QA formula to flag missing data and spammy phrasing.
- Reimport only clean, scored, personalized rows.
Why teams enrich exports in Sheets
Sending tools are great at delivery and sequencing, but the quality of a campaign is set before the send. Doing enrichment, scoring, and first-line personalization in Google Sheets keeps a clear audit trail and lets a human review every row. Smartlead is a trademark of its owner; this page is unaffiliated, makes no claims about it, and does not link to competitor domains.
Use cases
- Pre-send cleanup: normalize names, fix casing, and drop incomplete rows.
- ICP filtering: score fit and pause off-target contacts before they burn domain reputation.
- First-line personalization: draft specific openers grounded in real signals.
- List QA: flag spammy phrasing and unsupported claims before sending.
- Deliverability hygiene: review rows for risky content that could hurt inbox placement.
Best for / not best for
Best for: outbound teams who export contacts to CSV and want reviewable AI enrichment and personalization before loading them back into a sending tool.
Not best for: teams needing a licensed contact database, guaranteed email verification, or fully automated sending decisions without human review.
The strongest use case is the step between export and send: research, score, personalize, and QA in Sheets, then reimport only the rows that pass.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Cold email personalization in Google Sheets
- Clean and enrich CSV leads
- Write personalized cold email lines
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft. Use lawful public and business data only, keep source columns visible, store source URLs or dates when relevant, and verify ownership and contact details before outreach. Do not infer sensitive attributes. For outreach, follow consent, deliverability, and local compliance rules.
Frequently Asked Questions
How do I enrich a Smartlead export in Google Sheets?
Export to CSV, open it in Google Sheets, install GPT for Sheets, paste a research formula into row 2, review the output, fill it down, then reimport the cleaned and scored rows.
Will this verify email addresses?
No. GPT for Sheets researches, scores, and personalizes; use a dedicated verification tool for deliverability and always confirm before sending.
Can it write personalized first lines at scale?
Yes, it can draft a first line per row from the signals you provide, but keep a QA column and review for accuracy and tone before sending.
Should I trust every AI output automatically?
No. Treat output as a draft and use QA columns to flag missing evidence, unsupported claims, and rows that need manual review.
Enrich your Smartlead export in Google Sheets
If you export contacts before sending, install GPT for Sheets and clean, score, and personalize them where the data already lives.
Install GPT for Sheets or compare plans to turn a raw export into a reviewed, personalized list.