Copy-paste formulas for ICP fit scoring 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.
Fit score with rationale
A: account · B: ICP criteria · C: source signals
=GPT("Score this account 1-5 against our ICP. Account: " & A2 & ". ICP criteria: " & B2 & ". Source signals: " & C2 & ". Return score, one-paragraph rationale tied to the criteria, confidence, and what to verify manually.")
Tier label
A: account · B: score/rationale · C: tier rules
=GPT("Assign a tier (A/B/C) using these rules. Account: " & A2 & ". Score and rationale: " & B2 & ". Tier rules: " & C2 & ". Return the tier and a one-line reason.")
Disqualify check
A: account · B: disqualifiers · C: source signals
=GPT("Check this account against disqualifiers. Account: " & A2 & ". Disqualifiers: " & B2 & ". Source signals: " & C2 & ". Return Keep or Disqualify, which disqualifier applies if any, and confidence.")
QA the score
A: score output · B: source signals · C: criteria
=GPT("QA this fit score: " & A2 & ". Source signals: " & B2 & ". Criteria: " & C2 & ". Return missing data, risky assumptions, criteria not actually supported by signals, and pass/review/fail.")
Short answer
ICP fit scoring in Google Sheets is a spreadsheet-native way to score every account against your ideal customer profile with consistent rationale. Instead of judging accounts one at a time, GPT for Sheets runs a scoring rubric across the whole list and produces fit scores, rationale, and tiers in adjacent columns.
Fastest path: Install GPT for Sheets → add account and ICP-criteria columns → paste a formula from the formula section → review 10 rows → fill down the sheet.
This page is for RevOps, SDR leaders, founders, and marketers who need to define and score ICP fit across large lists and want fast, reviewable, repeatable output.
Workflow
A practical ICP-scoring sheet usually has these columns:
| Column | What to put there | Why it matters |
|---|---|---|
| A | Account or company | Stable row anchor for each account |
| B | Source signals: firmographics, notes, enrichment | Keeps scoring grounded in evidence |
| C | ICP criteria and weighting | Defines what “fit” means |
| D | Fit score | The number you sort and route by |
| E | Rationale | Makes the score auditable |
| F | Tier (A/B/C) | Routes accounts to the right motion |
| G | QA flag | Catches scores not supported by signals |
Step-by-step setup
- Write your ICP criteria explicitly in one column before scoring anything.
- Start with 10 representative accounts and check that the rationale matches your judgment.
- Calibrate the rubric: adjust criteria and weighting until scores feel right on known accounts.
- Add a disqualify check so clear non-fits are removed early.
- Add a QA formula that flags criteria the signals do not actually support.
- Fill down once the prompt works on sample rows.
Calibrating the rubric
A scoring model is only as good as its calibration. Run it on accounts you already know are strong or weak fits, compare the AI score and rationale to your own judgment, and refine the criteria until they agree. Keeping the rationale column visible makes it easy to see when the model is rewarding the wrong signals.
Use cases
- Prioritization: score and tier large account lists before reps engage.
- Routing: send A-tier accounts to one motion and B/C to another.
- Disqualification: remove clear non-fits early to protect rep time.
- Enrichment pairing: score right after enrichment so signals are fresh.
- QA: flag scores that the underlying signals do not support.
Best for / not best for
Best for: RevOps and sales teams that keep account lists in Google Sheets and want consistent, auditable ICP scoring at scale.
Not best for: teams that need scores treated as final decisions without human review, or a fully managed scoring platform outside Sheets.
The strongest use case is when you already have accounts and signals in a sheet and need consistent, explainable scores. Treat scores as AI estimates that guide prioritization, not as final verdicts.
Internal links and next workflows
- GPT for Sheets product page
- GPT for Sheets pricing
- AI lead scoring formulas
- Clay alternative for RevOps
- Account research automation
- Upgrade GPT for Sheets
Safety, compliance, and data quality
Scores are AI estimates that require human review. Do not present inferred attributes as verified facts, keep source signals visible next to each score, and avoid scoring on sensitive or protected attributes. Calibrate against known accounts before relying on the output.
Frequently Asked Questions
What is the fastest way to start ICP fit scoring?
Install GPT for Sheets, write your ICP criteria in a column, add account and signal columns, paste one scoring formula into row 2, review the rationale, then fill it down once it works on sample rows.
Why include a rationale column?
The rationale makes each score auditable. It shows which criteria drove the score so you can calibrate the rubric and catch cases where the model rewarded the wrong signal.
Can I trust the scores as final decisions?
No. Treat scores as AI estimates that guide prioritization. Verify high-value accounts and keep a human in the loop before committing resources.
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 ICP fit scoring in Google Sheets
If your team already works in spreadsheets, install GPT for Sheets and score your accounts where your data already lives.
Install GPT for Sheets or compare plans to turn account rows into scored, tiered, reviewable priorities.