Copy-paste formulas for NAICS / SIC classification in Google Sheets
Paste a formula into row 2, test it on a few rows, then drag down to run the classifier across your spreadsheet.
Industry label
A: company · B: source text
=GPT("Classify this company's primary industry in plain English: " & A2 & ". Source text: " & B2 & ". Return a short industry label only. If unclear, say Unclear.")
Best-fit NAICS guess
A: company · B: source text
=GPT("Suggest the most likely 2- to 6-digit NAICS code and its title for this company: " & A2 & ". Source text: " & B2 & ". Return code and title only. This is a best-effort guess, not official.")
Best-fit SIC guess
A: company · B: source text
=GPT("Suggest the most likely 4-digit SIC code and its title for this company: " & A2 & ". Source text: " & B2 & ". Return code and title only. This is a best-effort guess, not official.")
Confidence + rationale
A: company · B: assigned code · C: source text
=GPT("For company " & A2 & " assigned code " & B2 & ", rate confidence high/medium/low and give a one-line reason from this source text: " & C2 & ". Flag if manual review is needed.")
Short answer
Assigning NAICS or SIC codes in Google Sheets means classifying each company by industry so you can segment, route, and size your list. With GPT for Sheets you run a classification prompt across every row to suggest a best-fit code and a plain-English label, with a confidence note and rationale in adjacent columns. The codes are best-effort guesses, so keep a confidence column and verify before downstream use.
Fastest path: Install GPT for Sheets → add company and source columns → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for RevOps, data ops, and marketers who need to normalize a company list into NAICS, SIC, or industry categories for segmentation and routing.
NAICS vs SIC, in brief
NAICS (North American Industry Classification System) and SIC (Standard Industrial Classification) are both systems for labeling a business by what it does. NAICS is the newer, more granular system (2- to 6-digit codes) used widely across North America; SIC is the older 4-digit system still required by some datasets and forms. Many teams keep a plain-English industry label alongside the code so the data is readable in reports and routing rules.
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 for each company |
| B | Source notes: website copy, description, CRM fields | Keeps AI grounded in inspectable evidence |
| C | Industry label (plain English) | Readable category for reports |
| D | Best-fit NAICS or SIC code | The classification you need |
| E | Confidence (high/medium/low) | Tells you which rows to verify |
| F | Rationale | Explains the assignment |
| G | Review flag | Marks rows for manual check |
Step-by-step setup
- Start with 10 representative companies before filling down hundreds.
- Keep raw source fields unchanged so you can audit the AI output.
- Run the label formula first, then the code formula, on your sample.
- Add a confidence-and-rationale formula so you know which rows to trust.
- Verify and standardize the codes against an official reference for low-confidence rows.
- Fill down once the classifier works, then use the codes for tiering and routing.
Use cases
- Segmentation: group companies by industry for campaigns.
- Routing: send leads to the right rep or playbook by code.
- TAM sizing: estimate market segments from classified lists.
- CRM hygiene: fill missing industry fields consistently.
- QA: flag low-confidence codes for manual review.
Best for / not best for
Best for: RevOps and data teams that keep company lists in Google Sheets and want a fast, reviewable way to add industry labels and best-fit codes at scale.
Not best for: workflows that require officially certified classifications; for those, treat the AI output as a first pass and confirm against an authoritative source.
The strongest use case is turning a list with missing or messy industry data into consistent labels and best-fit codes, with a confidence column that tells you exactly which rows still need a human.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- Categorize leads by industry in Sheets
- Normalize job titles in Google Sheets
- Account tiering scoring in Sheets
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI-suggested NAICS and SIC codes are best-effort guesses, not official classifications. Always include a confidence column and verify low-confidence rows against an authoritative reference before using the codes for compliance, filings, or reporting. Keep source columns visible, use lawful data, and do not infer sensitive attributes.
Frequently Asked Questions
Can AI assign NAICS codes in Google Sheets?
Yes, as a best-effort suggestion. GPT for Sheets reads the company name and source text and returns the most likely NAICS code and title, plus a confidence note so you know which rows to verify.
What is the difference between NAICS and SIC?
NAICS is the newer, more detailed system (2- to 6-digit codes) used across North America; SIC is the older 4-digit system still required by some datasets. The formulas here can suggest either.
Are the codes official?
No. They are AI best-effort guesses based on the evidence you provide. Verify low-confidence rows against an authoritative reference before any compliance or reporting use.
How do I handle uncertain rows?
Use the confidence-and-rationale formula to mark high, medium, or low confidence, then manually review the medium and low rows before finalizing.
Start classifying companies in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and classify every company by industry where your list already lives.