Score your open pipeline against your ICP right now

Example outputs in this play are illustrative — they reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the prompt with your own pipeline and connectors to see live results. Personal details in any live examples are masked or abbreviated for privacy.

Every pipeline review surfaces the same question: which of these deals is actually going to close? The honest answer is that most teams don’t know until it’s too late. Deals that look healthy on paper stall at procurement. Deals that seemed risky close fast. The difference is almost always in the data — whether the account actually matches your ICP, whether the contacts are verified and in seat, whether the signals at the account support a close or point somewhere else.

This Claude prompt takes your open deals, runs each one against your ICP and closed-won patterns via Lusha, and returns a ranked pipeline — highest fit at the top, weakest fit flagged at the bottom — with the specific data points behind every score. No gut feel. No spreadsheet formulas. Just your pipeline scored against what actually converts for your business.

Requires: Lusha in Claude connected. Optional: connect your CRM to pull open deals automatically.

The prompt

Open in Claude Open in ChatGPT 
<context>
I want to know which deals in my open pipeline match my ICP
and closed-won patterns — and which ones don't — before my
next pipeline review.

My open deals:
[Paste deal name, company, deal stage, and ACV for each
— or connect your CRM and say "use my open pipeline"]

My ICP: [describe your ideal customer — industry, company
size, geography, funding stage]

My best closed-won customers for pattern matching:
[optional — list 2–3 company names or domains]
</context>

<task>
1. ICP SCORING — For each deal, use Lusha to score the
   account against my ICP:
   - Firmographic fit: industry, size, funding stage,
     geography
   - Signal alignment: are buying signals firing at this
     account right now or is it quiet?
   - Lookalike fit: how closely does this account match
     my closed-won customer patterns?
   - Score each deal: High fit / Medium fit / Low fit

2. CONTACT VERIFICATION — For each deal, verify the
   primary contact via Lusha:
   - Still in seat?
   - Title change or promotion since deal opened?
   - Any departure signals?
   - Flag missing Economic Buyer or Champion

3. DEAL RISK FLAGS — For each deal, surface any signals
   that point away from a close:
   - Key contact departed or changed roles
   - No buying signals at the account
   - Account signals pointing to competitor evaluation
   - Deal stage does not match signal strength
   - Low ICP fit with no compensating signals

4. RANKED OUTPUT — Return the full pipeline ranked by
   ICP fit and signal strength:
   - High fit deals at the top with next action
   - Medium fit deals with what's missing
   - Low fit deals flagged with specific reason
   - One concrete next action per deal tied to the data
</task>

What you'll get back

Your full pipeline ranked by ICP fit and signal strength. High-fit deals at the top with a next action. Low-fit deals flagged with a specific reason before the manager has to ask.

Pipeline ICP scorecard

Deals reviewed: 8  ·  High fit: 3  ·  Medium fit: 3  ·  Low fit: 2  ·  Contacts flagged: 2

High fit deals

CompanyStageACVICP fitTop signalContact status
[Company A]Stage 3$42KHighSeries B closed 6 wks ago✓ Confirmed in seat
[Company B]Stage 2$28KHigh12 SDR roles posted✓ Confirmed in seat
[Company C]Stage 3$55KHighIntent score 81 — your category⚑ VP promoted 3 weeks ago

Next action — [Company C]: Champion was promoted to VP 3 weeks ago. New mandate, new budget cycle. Re-engage with updated business case tied to their new scope. This is a timing window, not a risk.

Medium fit deals

CompanyStageACVICP fitWhat’s missingContact status
[Company D]Stage 2$18KMediumNo signals in last 60 days✓ Confirmed in seat
[Company E]Stage 1$22KMediumSmaller than typical win✓ Confirmed in seat
[Company F]Stage 3$38KMediumNo Economic Buyer verified⚠ Economic Buyer not identified

Next action — [Company F]: Stage 3 with no verified Economic Buyer is the most common reason deals slip at the finish line. Lusha has 3 CFO and VP Finance contacts at this account — verify and get one into the deal before advancing.

Low fit deals — flagged

CompanyStageACVICP fitFlag reasonContact status
[Company G]Stage 2$14KLowWrong industry, no signals⚠ Primary contact departed
[Company H]Stage 1$9KLowBelow ICP size threshold✓ Confirmed in seat

Next action — [Company G]: Primary contact left the company 5 weeks ago. Wrong industry for your ICP. No signals. Recommend moving to closed-lost or reassigning before it ages further in the pipeline.

Example outputs in this play are illustrative — they reflect the structure, fields, and format of real Lusha connector output, but were not pulled from a live session. Run the prompt with your own pipeline and connectors to see live results.

Built by: Lusha
Time to build: 1 min
Difficulty: Easy
Tools: Claude, Lusha, Salesforce
Type: Prompt

Why use Lusha in Claude

Pipeline reviews fail for one reason: the data behind every deal is either missing, stale, or disconnected from the signals that actually predict outcomes. A rep says a deal is on track because the last call went well. The manager accepts it because there’s nothing else to go on. Six weeks later it slips, and the post-mortem reveals the champion left four weeks ago and the account went quiet on every signal channel.

This play runs the check that should happen before every pipeline review but almost never does. Lusha’s Deep Intelligence layer scores each deal against your actual ICP — not a generic model, but the specific firmographic and signal patterns from your own closed-won data. A deal at a company that matches your best customers on four out of five dimensions, with a live intent signal and a funding round in the last 90 days, scores differently from a deal at a company with no signals, below your typical size threshold, and a contact who joined three months ago and might not have budget authority yet.

The contact verification step is where deals that look healthy on the surface get exposed. A champion who was promoted three weeks ago isn’t a risk — it’s a timing window. A primary contact who left the company five weeks ago and nobody noticed is a risk, and the only way to catch it before it becomes a lost deal is to check. Lusha verifies every key contact live during the session so the scorecard reflects who’s actually in the room, not who was there when the deal opened.

The output is a ranked pipeline with one concrete next action per deal tied to the specific data point that matters most. Not a status report. A set of decisions, made before the manager has to make them for you.

Lusha data is sourced and used in accordance with Lusha’s Privacy Policy and Terms of Use.

FAQ

  • Do I need to connect my CRM or can I paste my deals manually?

    Both work. If you paste your deals directly — company name, deal stage, and ACV — the play runs immediately without any additional setup. If you connect your CRM, it pulls your open pipeline automatically and you don’t have to maintain a separate list. For teams running this weekly, the CRM connection is worth setting up once. For a one-off pipeline review, paste works fine.

  • How is ICP fit scored if I don't provide best customers for pattern matching?

    The play scores on firmographic fit alone — industry, company size, geography, and funding stage against the ICP you describe. Adding best customers for pattern matching makes the scoring more precise because Lusha can identify the specific combinations of attributes that appear in your actual wins, not just the filters you set. It’s optional but meaningfully improves the output. Even two or three company names is enough to give the pattern matching engine something real to work from.

  • How is this different from the Pipeline Review Skill?

    The Pipeline Review Skill is built for weekly risk monitoring — it sweeps your pipeline for deals that have gone quiet, contacts that have departed, and coverage gaps by buying role. This play is built for a specific question: which of my deals actually matches my ICP and closed-won patterns? It’s the scoring and ranking motion, not the risk monitoring motion. Use this before a QBR when you need to know which deals deserve your focus. Use the Pipeline Review Skill every Monday to catch what’s moving and what’s stalling.

  • What if most of my pipeline scores as medium or low fit?

    That’s valuable information. It means your pipeline doesn’t reflect your ICP — either the prospecting motion is pulling in the wrong accounts, the qualification bar is too low, or the ICP definition needs updating. The play surfaces that gap with data rather than gut feel, which makes it a useful input for a sales leadership conversation about where pipeline is coming from and whether the qualification criteria need tightening before the next quarter starts.

  • Can a sales manager run this across a rep's full pipeline before a 1:1?

    Yes — and that’s one of the strongest use cases. Paste the rep’s full deal list before the 1:1, run the play, and walk into the conversation knowing which deals are genuinely well-qualified, which ones have data gaps, and which ones should probably be moved to closed-lost before they age further. The rep gets a data-backed review instead of a gut-check conversation, and the manager doesn’t have to rely on the rep’s self-reported pipeline health to make forecasting decisions.

Related Prompts

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Connect once, run anywhere. Works in Claude, ChatGPT, n8n, Clay, or any agent connected to Lusha.

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