Copy-paste formulas for firmographic enrichment
Paste a formula into row 2, test it on a few rows, then drag down to run the workflow across your spreadsheet.
Industry + model
A: company · B: website/notes
=GPT("Based only on the provided info, infer the industry and business model (B2B, B2C, B2B2C, marketplace, other) for: " & A2 & ". Info: " & B2 & ". Return industry | model | confidence(low/med/high). If info is thin, use low confidence and add [verify].")
Size band
A: company · B: signals (headcount hints, etc.)
=GPT("Estimate an employee size band (1-10, 11-50, 51-200, 201-1000, 1000+) for " & A2 & " from these signals: " & B2 & ". Return band | confidence | what would confirm it. Do not state an exact headcount you cannot support.")
Segment assignment
A: enriched fields · B: your segment rules
=GPT("Assign a segment using these rules: " & B2 & ". Enriched fields: " & A2 & ". Return segment | reason. If the rules do not clearly apply, return Unsegmented [review].")
QA / confidence flag
A: enriched row · B: required fields
=GPT("QA this enriched row: " & A2 & ". Required fields: " & B2 & ". Flag low-confidence or unsupported inferences and missing required fields, then return pass/review/fail.")
Short answer
AI firmographic enrichment in Google Sheets is a workflow for RevOps and marketing teams who want a bare company list turned into usable segmentation without a heavy data vendor for every field. GPT for Sheets runs AI formulas across a table of company name and the signals you have, inferring industry, business model, size band, and segment in adjacent columns — each with a confidence and QA flag so you know what to verify.
Fastest path: Install GPT for Sheets → add company and target columns → paste a formula from the formula section → review 10 rows → fill down. For plans, see GPT for Sheets pricing.
This page is for purchase-intent teams that already keep account lists in spreadsheets and want fast, transparent enrichment.
Workflow
A practical sheet for this workflow usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Company name | Stable row anchor |
| B | Website copy, notes, or signals | Evidence the model infers from |
| C | Your segment rules | Makes segmentation consistent |
| D | Industry + model | Core firmographic fields |
| E | Size band + confidence | Honest sizing with uncertainty |
| F | Segment | Routing and prioritization |
| G | QA / confidence flag | Marks what to verify before acting |
Step-by-step setup
- Start with 10 representative accounts before filling down.
- Keep raw fields unchanged; write enriched fields to new columns.
- Ask for confidence and what would confirm each inference.
- Add rules: never state an exact headcount you cannot support; add [verify] when thin.
- Add a QA formula that flags low-confidence rows.
- Fill down, then verify the low-confidence rows before relying on them.
Why enrich in a spreadsheet with confidence flags
Firmographic inference is only useful if you know how much to trust it. Enriching in a sheet — with the inferred field, a confidence level, and a QA flag side by side — lets you test on 10 accounts, verify the low-confidence ones, and segment on data you understand, instead of importing opaque fields you cannot audit.
Copyable formula notes
Paste the cards into row 2 and drag down. Always ask for confidence and a “what would confirm it” note, and keep the rule that the model must not assert exact figures it cannot support.
Use cases
- Infer industry and business model from the data you have.
- Estimate size bands honestly, with confidence.
- Segment accounts using your own rules.
- Flag low-confidence inferences to verify before acting.
Best for / not best for
Best for: RevOps and marketing teams that keep accounts in Sheets and want fast, transparent, confidence-aware enrichment.
Not best for: treating inferred firmographics as verified facts, or using them for high-stakes decisions without confirming the low-confidence rows.
Use GPT for Sheets as the inference and segmentation layer, and verify anything the QA column flags as low confidence.
Internal links and next workflows
- GPT for Sheets
- GPT for Sheets pricing
- ABM target account list building
- Data cleaning for CRM imports
- Clay alternative for SaaS founders
Safety, compliance, and data quality
Base inferences only on data you may use, never assert exact figures the evidence does not support, and keep raw columns intact for audit. Treat enrichment as a draft, verify low-confidence rows, and segment on fields you understand. A pass / review / fail QA column keeps inferred firmographics honest.
Frequently Asked Questions
What is the fastest way to enrich a company list in Google Sheets?
Install GPT for Sheets, add company and target columns, paste one inference formula into row 2, review a sample, then fill down and verify the low-confidence rows.
Is the inferred data accurate?
It is a transparent estimate with a confidence level, not a verified record. Confirm low-confidence rows before relying on them for important decisions.
Can I apply my own segmentation rules?
Yes. Pass your segment rules in the formula so accounts are segmented consistently, with an Unsegmented [review] fallback when rules do not clearly apply.
Do I need to copy and paste between ChatGPT and Sheets?
No. GPT for Sheets runs AI formulas directly in spreadsheet cells, which is better for repeatable bulk enrichment and QA review.
Start this workflow in Google Sheets
If your account list already lives in spreadsheets, install GPT for Sheets and enrich it where your rows already live.
Install GPT for Sheets or compare plans to turn a bare company list into confidence-aware firmographics and segments.