Copy-paste formulas for TAM list building 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.
Segment classification
A: company · B: segment definition · C: source signals
=GPT("Classify whether this company is in our addressable segment. Company: " & A2 & ". Segment definition: " & B2 & ". Source signals: " & C2 & ". Return In/Out/Unclear, the likely sub-segment, reason, and confidence.")
Fit score
A: company · B: ICP criteria · C: source signals
=GPT("Score this company 1-5 for fit within our TAM. Company: " & A2 & ". ICP criteria: " & B2 & ". Source signals: " & C2 & ". Return score, reason, confidence, and what to verify manually.")
Enrichment note
A: company · B: source notes · C: what you want
=GPT("Write a short enrichment note for " & A2 & ". Source notes: " & B2 & ". What we want: " & C2 & ". Return 2-3 sentences of useful context and flag any guess as estimated.")
QA missing-data flag
A: AI output · B: source signals · C: required fields
=GPT("QA this output: " & A2 & ". Source signals: " & B2 & ". Required fields: " & C2 & ". Return missing data, risky assumptions, unsupported claims, and pass/review/fail.")
Short answer
TAM list building in Google Sheets is a spreadsheet-native way to assemble, clean, and classify a total addressable market list with consistent rules. Instead of researching companies one at a time, GPT for Sheets runs prompts across the seed list and produces segment classifications, fit scores, and enrichment notes in adjacent columns.
Fastest path: Install GPT for Sheets → add your seed list → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for founders, RevOps, demand gen, and agencies building target account lists who want fast, reviewable classification and enrichment at scale.
Workflow
A practical TAM-building sheet usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Company name | Stable row anchor for each candidate |
| B | Segment signals or source notes | Keeps classification grounded in evidence |
| C | Segment definition and ICP criteria | Defines what belongs in the TAM |
| D | In/Out/Unclear classification | Filters the seed list down |
| E | Fit score | Sorts the TAM into priority |
| F | Tier and enrichment note | Prepares accounts for outreach |
| G | QA flag | Catches classifications not supported by signals |
Step-by-step setup
- Write the segment definition and ICP criteria explicitly before classifying.
- Assemble and de-dupe the seed list, keeping source signals in column B.
- Start with 10 representative rows and confirm the In/Out calls match your intent.
- Score fit and tier the In rows for outreach.
- Add a QA formula that flags classifications the signals do not support.
- Fill down once the prompt works on sample rows.
From TAM to tiers
A raw TAM is just a starting point. Classify each company in or out of the addressable segment, score the In rows against your ICP, and split them into tiers so outreach starts with the strongest fits. Pairing TAM building with ICP scoring keeps the same rubric consistent from market sizing through routing.
Use cases
- Market sizing: classify a seed list into your addressable segment.
- List building: assemble and clean a target account list at scale.
- Prioritization: score and tier accounts for outreach sequencing.
- Enrichment: add useful context to each account in the same sheet.
- QA: flag classifications and scores not supported by signals.
Best for / not best for
Best for: founders and RevOps teams that build target account lists in Google Sheets and want consistent, reviewable classification and scoring.
Not best for: teams that need a licensed market database, decisions without review, or a fully managed platform outside Sheets.
The strongest use case is when you already have a seed list and need consistent classification and scoring. Treat AI-classified firmographics as estimates and verify before relying on them.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- ICP fit scoring in Google Sheets
- ABM target account list building
- Account research automation
- Upgrade GPT for Sheets
Safety, compliance, and data quality
AI output should be treated as a draft. Treat AI-classified firmographics as estimates, keep source signals visible, respect source-data terms, and verify before use. For outreach, follow consent, deliverability, and local compliance rules.
Frequently Asked Questions
What is the fastest way to build a TAM list in Sheets?
Install GPT for Sheets, write your segment definition and ICP criteria, add a seed list, paste one classification formula into row 2, review the calls, then fill it down once it works on sample rows.
How is TAM building different from ICP scoring?
TAM building decides whether a company belongs in your addressable market; ICP scoring ranks how good a fit the in-market accounts are. They pair well: classify first, then score and tier.
Can it confirm firmographics?
No. It estimates segment and firmographics from your signals and explains its reasoning, but those estimates must be verified before you rely on them.
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 building your TAM in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and build your TAM where your data already lives.
Install GPT for Sheets or compare plans to turn a seed list into a classified, scored, tiered TAM.