Copy-paste formulas for a FullEnrich alternative workflow 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.
Company research
A: company · B: source notes · C: offer
=GPT("Research this company for outreach: " & A2 & ". Source notes: " & B2 & ". Offer: " & C2 & ". Return a concise summary, likely industry and size, useful signals, missing data, and one next action. If evidence is weak, say Needs manual research.")
Role / seniority tag
A: title · B: context
=GPT("From this job title: " & A2 & " and context: " & B2 & ", return department, seniority level, and whether it is likely a decision-maker. Mark anything not stated as Unknown.")
Fit score 1-5
A: account · B: ICP · C: source text
=GPT("Score this account 1-5 for fit. Account: " & A2 & ". ICP: " & B2 & ". Source text: " & C2 & ". Return score, reason, confidence, and what to verify manually.")
Personalized opener
A: contact/role · 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.")
Short answer
A FullEnrich alternative in Google Sheets means doing research, scoring, and personalization with AI formulas in the spreadsheet instead of adopting a separate enrichment platform. FullEnrich is a waterfall enrichment tool that finds contact data such as emails and phone numbers across multiple providers; GPT for Sheets is a lighter, spreadsheet-native option for teams that want the research and prioritization layer where their lists already live.
Fastest path: Install GPT for Sheets → add your source columns → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for RevOps and SDR leaders evaluating FullEnrich who already work in Google Sheets. GPT for Sheets is not affiliated with FullEnrich and is not a contact-data or waterfall provider; FullEnrich and other product names are trademarks of their respective owners, and the comparison here is factual and non-defamatory.
Workflow
A practical sheet for this workflow usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Account or contact name | Stable row anchor for each record |
| B | Source notes: website copy, export, CRM fields | Keeps AI grounded in inspectable evidence |
| C | Offer or product | Sharpens relevance and scoring |
| D | Target signals to find | Defines what the AI should look for |
| E | AI research summary | First useful interpretation of the row |
| F | Fit score and label | Sorts the list for routing |
| G | Outreach opener or next action | Turns research into execution |
| H | QA flag | Stops unsupported claims before outreach |
Step-by-step setup
- Start with 10 representative rows before filling down hundreds.
- Keep raw source and export fields unchanged so you can audit the AI output.
- Run one formula to create a research summary, then inspect weak rows.
- Add constraints: max length, required format, and what to do when data is missing.
- Add a QA formula that flags missing facts and unsupported assumptions.
- Fill down once the prompt works on your sample rows.
How a Sheets workflow compares with FullEnrich
FullEnrich runs waterfall enrichment, checking several data sources to return contact details like verified emails and phone numbers, and it is a strong fit for teams whose core need is contact-data coverage. GPT for Sheets takes a different approach: it adds AI research, scoring, and personalization directly to the spreadsheet, working on lists you have already sourced. It does not ship a proprietary contact database or run waterfall lookups, so pair it with your own sourced data or a dedicated provider for contact details. It is not affiliated with FullEnrich and is not a drop-in replacement for every feature; the comparison here is factual and non-defamatory.
Use cases
- Enrich exports: add AI research to lists you already pulled.
- Prioritization: score and label accounts before reps invest time.
- Signal extraction: pull structured signals out of messy source text.
- Personalization: draft openers grounded in a specific, inspectable signal.
- QA: flag rows missing evidence or making unsupported claims.
Best for / not best for
Best for: teams that already keep account and contact lists in Google Sheets and want a lightweight, reviewable way to research, score, and personalize without standing up a separate platform.
Not best for: teams whose primary need is waterfall contact-data coverage; in that case use GPT for Sheets as the research and personalization layer on top of data sourced elsewhere.
The strongest use case is enriching and prioritizing lists you already control. GPT for Sheets supplies the AI research and QA layer; you supply lawful, sourced contact data.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Waterfall enrichment in Sheets without Clay
- CSV lead enrichment in Google Sheets
- Best AI model for lead enrichment in Sheets
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft. Use lawful public and business data only, do not rely on GPT for Sheets to reproduce a contact database, keep source columns visible, store source URLs or dates when relevant, and verify contact details before outreach. Follow consent, deliverability, and local compliance rules.
Frequently Asked Questions
Is GPT for Sheets a FullEnrich replacement?
Not exactly. FullEnrich provides waterfall contact-data enrichment; GPT for Sheets is a spreadsheet-native AI layer for research, scoring, and personalization on lists you already have. Many teams use it as a lighter alternative or alongside a data provider. It is unaffiliated with FullEnrich.
Does it find emails and phone numbers like FullEnrich?
No. GPT for Sheets does not run waterfall lookups or include a contact database. You supply lawful, sourced rows and it adds AI research, scoring, and drafting in adjacent columns.
What does it replace and what does it not?
It replaces the manual research, prioritization, and personalization work; it does not replace a dedicated contact-data provider. Pair it with your sourced data for full coverage.
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 verification.
Try a FullEnrich alternative workflow in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and run these formulas on the lists you already have.