Library/Prospecting & Pipeline Creation/ICP Definition & Target Account Selection/Build a Data-Driven ICP from Closed-Won and Lost Deals
TestedProspecting & Pipeline CreationSame-Day Use (under 30 min)

Build a Data-Driven ICP from Closed-Won and Lost Deals

Generate a data-driven Ideal Customer Profile with firmographic patterns, buying signals, disqualification criteria

About this prompt

This prompt helps sales directors and RevOps teams build a rigorous ICP definition by analyzing closed-won and closed-lost deal data from the last 12 months. It produces firmographic win patterns, trigger event analysis, disqualification signals, a scoring rubric, and a lookalike account description a rep can apply in the field. Use it annually during GTM planning or after a significant change in win rate.ICP definitions built from opinion rather than deal data tend to reflect the customers the team is comfortable selling to rather than the customers the team consistently wins with. This prompt generates a bottom-up ICP by analyzing the patterns in your actual closed-won and closed-lost history. For closed-won data, populate [PASTE CRM DATA] with company name, industry, employee count, revenue, champion title, deal size, sales cycle length, and win notes; for closed-lost, include the same fields plus reason lost where known. The output includes a firmographic profile, champion title patterns, trigger event analysis, disqualification signals that predict losses, a tiered scoring rubric for evaluating new accounts, and a two-sentence lookalike description for reps to use in the field. Use this prompt for annual ICP validation or after a win rate shift — not as a monthly tool, since ICP patterns require enough deal volume to be statistically meaningful. For best results, include lost deal data even when it's incomplete: the disqualification signals are often the highest-value output, and they require the loss patterns to be visible alongside the wins.

The prompt

I'm pasting my closed-won and closed-lost deal data from the last 12 months. Analyze this data and produce a data-driven ICP definition. CLOSED-WON DEALS DATA: [CLOSED-WON DEALS: COMPANY NAME, INDUSTR
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Variables to replace

CLOSED-WON DEALS: COMPANY NAME, INDUSTRY, EMPLOYEE COUNT, REVENUE, CHAMPION TITLE, DEAL SIZE, SALES CYCLE LENGTH, AND NOTES ON WHY WE WON | CLOSED-LOST DEALS: SAME FIELDS PLUS WHY LOST IF KNOWN | EMPLOYEES / REVENUE RANGE WHERE WE WIN MOST

How to run it

  1. Copy the prompt and paste it into Claude, ChatGPT, Gemini, or Copilot — it's tuned to run in any of them.
  2. Replace the variables — swap each bracketed token for your deal's specifics. The variables list above explains every field.
  3. Make it yours — run it, keep what's strong, and tighten your inputs on the next pass. The prompt improves with your context.

What good output looks like

Make it yours

StagePre-Prospecting
SeatSales Director, RevOps / Sales Ops, Sales Manager
DifficultyAdvanced
Time to valueSame-Day Use (under 30 min)
Works withAI-Agnostic
CategoryProspecting & Pipeline Creation
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