Copy-paste formulas to clean and score a ZoomInfo 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 spreadsheet.
Normalize a record
A: raw record · B: target format
=GPT("Normalize this exported record into the target format. Record: " & A2 & ". Target format: " & B2 & ". Return cleaned fields only, and flag anything ambiguous as Needs review.")
Fit score
A: account/contact · B: ICP criteria · C: exported fields
=GPT("Score this row 1-5 for fit. Account/contact: " & A2 & ". ICP criteria: " & B2 & ". Exported fields: " & C2 & ". Return score, reason, confidence, and what to verify manually.")
Outreach angle
A: contact · B: signal · C: offer · D: tone
=GPT("Write a specific outreach opener for " & A2 & " based on this signal: " & B2 & ". Offer: " & C2 & ". Tone: " & D2 & ". Keep it factual and under 70 words.")
QA missing-data flag
A: AI output · B: exported fields · C: required fields
=GPT("QA this output: " & A2 & ". Source fields: " & B2 & ". Required fields: " & C2 & ". Return missing data, risky assumptions, unsupported claims, and pass/review/fail.")
Short answer
ZoomInfo export cleanup and scoring in Google Sheets is a spreadsheet-native way to turn a large CSV into clean, scored, ready-to-work rows. Instead of fixing and qualifying records one at a time, GPT for Sheets runs prompts across the whole export and produces normalized fields, fit scores, and outreach drafts in adjacent columns.
Fastest path: Install GPT for Sheets → import your ZoomInfo CSV → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for SDR, BDR, and ops teams who export contact and company lists from ZoomInfo and want faster, reviewable cleanup and scoring at scale.
“ZoomInfo” is a trademark of its owner. GPT for Sheets is not affiliated with ZoomInfo. Respect the source’s terms of use, and verify records before outreach.
Workflow
A practical cleanup sheet usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Raw exported record | Stable row anchor for each lead |
| B | Exported fields to normalize | Keeps AI grounded in the source data |
| C | ICP criteria or goal | Makes scoring specific |
| D | Cleaned / normalized fields | The tidy version you work from |
| E | Fit score | Sorts the list into tiers |
| F | Outreach draft or next action | Turns scoring into execution |
| G | QA flag | Prevents bad records from moving forward |
Step-by-step setup
- Import the ZoomInfo CSV and keep the original columns intact in A-B.
- Start with 10 representative rows before filling down the whole export.
- De-dupe and normalize obvious field issues before scoring.
- Add constraints: target field format, scoring rubric, and a missing-data rule.
- Add a QA formula that flags missing facts and unsupported assumptions.
- Fill down once the prompt works on sample rows.
De-dupe and field hygiene
Large exports often contain duplicate contacts, inconsistent titles, and partial records. Normalize key fields, collapse duplicates, and standardize titles and segments before scoring so the rubric is applied consistently. Keep the raw export in place so every cleaned value can be audited.
Use cases
- Cleanup: normalize messy export fields into consistent columns.
- Scoring: tier leads by fit before reps invest time.
- Personalization: draft outreach from row-specific signals.
- Routing: split the export into priority tiers for assignment.
- QA: flag duplicate, partial, or risky records.
Best for / not best for
Best for: SDR/BDR and ops teams that export from ZoomInfo and want faster, reviewable cleanup and scoring inside Google Sheets.
Not best for: teams that need a licensed real-time data feed, decisions without review, or a fully managed platform outside Sheets.
The strongest use case is when you already have an export and need clean, scored output. Use GPT for Sheets as the cleanup, scoring, and personalization layer after export.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Apollo export cleanup & scoring
- AI enrichment for CSV exports
- AI lead scoring formulas
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft. Respect ZoomInfo’s terms of use, verify records before outreach, keep source columns visible, and avoid inferring sensitive attributes. For outreach, follow consent, deliverability, and local compliance rules.
Frequently Asked Questions
What is the fastest way to clean a ZoomInfo export?
Install GPT for Sheets, import the CSV, keep original columns intact, paste one normalize formula into row 2, review the output, then fill it down once it works on sample rows.
Does this replace ZoomInfo?
No. It is a cleanup, scoring, and personalization layer for data you have already exported. “ZoomInfo” is a trademark of its owner and GPT for Sheets is not affiliated with it; respect the source’s terms.
Can it fix every record automatically?
No. It can normalize and flag records, but ambiguous or partial rows should be marked Needs review and checked by a human before outreach.
Should I trust every AI output automatically?
No. Treat output as a structured draft and use QA columns to flag missing evidence, unsupported claims, and rows that need manual research.
Start cleaning your ZoomInfo export
If your team already exports from ZoomInfo, install GPT for Sheets and run these formulas where your data already lives.
Install GPT for Sheets or compare plans to turn an export into clean, scored, ready-to-work rows.