The moment you actually need this

You need a list that marketing and sales trust. The slow path is a quarter of meetings across product, sales, marketing, and the C suite. The faster path is to define a small set of buying signals that predict fit, score a starter set of accounts, and prove lift. Most off the shelf tools do not tag pain or use case. They will not label why a company is likely to care. That is the gap we close here.

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Start with source data

Begin with what you already have.

• Wins and losses - 5 to 10 short notes per deal.
• Call summaries - Gong or notes from discovery.
• SME time - 20 minutes with a person who knows why customers buy.
• Public hints - website, careers, product updates, press, LinkedIn posts.
  

Your goal is to find 5 to 7 signals that correlate with pain or an active initiative. Examples: marketing automation platform in use, team size band, running webinars, hiring RevOps or MOPs, buying motion. When in doubt, prefer a signal that points to pain or a real project over a vague firmographic field.

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Crawl - ChatGPT

What you do

• Paste 5 to 10 win or call notes and 30 to 100 account names you already know.
• Ask for candidate signals that separate strong fit from weak fit.
• Create a simple scoring rule and label A, B, C.
• Pick one micro segment you could target this week.
  
  

Copy-paste prompt

You are my analyst. From the WIN_NOTES below, propose 5–7 buying-fit signals that predict strong fit for our go-to-market. Each signal must be concrete and observable.

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Return a table

Signal | How to recognize it | Why it matters for us

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Create a simple scoring rule

+2 per signal present, −2 per disqualifier (list up to 3 clear disqualifiers).

Bands: A = 6–10, B = 3–5, C = 0–2.

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Apply the rule to ACCOUNT_LIST.

Use only reasonable inferences from the names or notes provided. If unknown, leave blank.

Return:

Account | Signals present (short notes) | Score | Band | 2-sentence rationale

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From the A-band results, propose 2 micro-segments with criteria, why it helps messaging, and the member accounts.

No fluff. Leave blanks when uncertain.

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What you get

• A 5 to 7 signal model, a scoring rule, and an A or B or C label for each account you provided.
  
  
• Two candidate micro segments to launch now.
  
  
• Limitation - you update this manually until you run the weekly flow.
  
  

Walk - n8n or Gumloop

What you do

• Build a weekly flow that fills your 5 to 7 fields for 200 to 500 accounts, scores A or B or C, and writes back to a sheet or CRM.
• Export or write an Active List for your chosen micro segment.
• Add a simple confidence flag so you can see which rows are solid and which need review.
  
  

Flow outline

1. Input - Google Sheet or CRM view with Account and Website.
   
2. Checks - careers page for RevOps or MOPs hiring, site nav for Webinars or Events, tech hints for MAP in use, public posts for current initiatives.
   
3. Map - set your 5 to 7 fields. Leave blank rather than guess.
   
4. Score - apply +2 or −2, label A or B or C, set a confidence note.
   
5. Write back - update sheet and optional HubSpot or Salesforce fields.
   
6. Micro segment - create a tab or Active List for the target slice.
   
7. Notify - send a short weekly summary to Slack or email.
   

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Output

• 200 to 500 accounts with fields and A or B or C labels updated weekly.
  
• A current micro segment list you can use now.
  
• Caveat - no code flows are rigid. Expect edits when fields or pages change.
  
  

Run - Operating agents

What changes

• Agents research, write small bits of code when needed, and operate inside your tools.
  
• They infer signals from sites, careers, posts, and product notes, and reconcile oddities.
  
• They add and verify the right contacts by role, then dedupe against CRM.
  
• They maintain smart lists and segments directly in HubSpot or Marketo or Eloqua, not in CSVs.
  
• They score with the same model, halt on uncertainty, and log every change with a reason.
  
  

Output

A live, ranked list in your MAP and CRM with fresh contacts and micro segments, a Monday delta report of what changed and why, and no manual export or import work.

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Signals that matter

• MAP in use - HubSpot or Marketo or Eloqua or unknown
  
• Team size band - 0 to 5 or 6 to 10 or 11 to 20 or 21 plus
  
• Current motions - Webinars or Dinners or PLG or ABM
  
• Ops hiring - None or Planned or Active
  
• Buying motion - Self serve or Light sales or Enterprise
  
• Pain or initiative clues - audit, migration, backlog, missed target, public push
  
• Disqualifiers - agency operates MAP, RFP only, vendor lock you cannot influence
  
  

Prefer 5 to 7 signals total. Add more only when it changes outcomes.

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Model and list outline

1. Signals table - name, how to recognize, why it matters
   
2. Scoring rule - +2 per signal, −2 per disqualifier, A or B or C bands
   
3. Account table - signals present, score, band, short rationale
   
4. Micro segments - criteria, why it helps, member accounts
   
5. Active List - a live audience in your MAP that matches the micro segment
   
   

Nuance to watch

Two big gaps hurt most lists. First, pain and use case are missing in the default filters of common data tools. You need to infer or gather them. Second, low code workflows look simple but behave like code. Columns change, pages move, APIs rate limit. That is why reliability slips without an engineer. Operating agents help by deciding when they have enough information, writing their own small tools when a step requires it, and stopping when something looks wrong.

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Get started

• Crawl - paste win and call notes into ChatGPT with the prompt above and label 50 to 100 accounts. Pick one micro segment.
  
• Walk - build the weekly flow to fill 5 to 7 fields, score, and write an Active List. Add a confidence flag.
  
• Run - add operating agents when you want research, contact add and verify, smart list maintenance, and a weekly delta report across your stack.

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Book a live build - see your ICP signals scored and a micro segment live in your MAP in 15 minutes.