Turn your last 10 wins into your next 40 targets.

“Who looks like my best customers, and where do I find forty more of them?”

Builds a win profile from your closed-won deals, weighing behavioral signals over firmographics, and keeps only the traits that separate wins from losses. Then scores lookalike accounts against it and writes a shared-situation first-touch angle for each.

CLOSE MORE DEALSINTERMEDIATECRMENRICHMENT TOOL WITH PEOPLE AND JOB-POSTINGS DATACONVERSATION TOOL✓ APPROVED 4/4
EXAMPLE PROMPTS
>Build a win profile from my last 10 closed-won deals [pastes CSV]
>Score this account list against that profile and write a first-touch angle for each
>Which traits separate our wins from losses, and which are just firmographic noise?
ABOUT THIS SKILL

What this skill does

Takes your last N closed-won deals and turns them into a win profile, then into a scored list of net-new accounts that match it, each with a first-touch angle you can send this week. Where closed-won-analysis is the retro that tells you why you won, this skill consumes that read and produces the forward artifact: the scored lookalike list and the per-account opener. The profile is not just firmographics. It is what was true at the account before the deal started: what they were hiring for, what tool they were replacing, what changed. Those are the signals a rep can actually act on.

Inputs

  • Works today with: your last 10 to 20 closed-won deals and a comparable closed-lost set, exported from your CRM. Include industry, size, deal size, source, the incumbent tool if you have it, and any notes on what was happening at the account. Paste it or upload a CSV.
  • More powerful connected to a CRM: it reads wins and losses automatically and pulls the fields you forgot to export.
  • Sharper with an enrichment tool (Clay, Common Room): sources the lookalike candidates and fills the behavioral signals, hiring, stack, recent changes.
  • Sharper with product analytics if you are PLG: adds pre-sale usage as a profile signal, which is often the strongest one you have.

How it works

  1. 1
    Win-profile extraction (behavior first)

    Read every win and pull two layers. The firmographic shape: segment, size, industry, region. Then the layer that matters more: the behavioral and situational signals present before the deal started. What roles were they hiring for. What tool were they replacing. What changed at the account, a new leader, a funding round, a stack migration. When we backtested our scoring model against real won and lost deals, the strongest single predictor of a win was a behavioral signal at 3.56x lift; the firmographic fit score everyone trusted came in at 1.13x. The profile weighs behavior over firmographics because the data votes that way.

  2. 2
    Signal vs coincidence (wins against losses)

    A trait counts only if it appears in wins meaningfully more than in losses. "Most of our wins are mid-market" is noise if most of your losses are too. Run every candidate trait through the contrast: share of wins that show it, share of losses that show it, and the ratio. Keep the traits that separate. Drop the rest, even the ones the team believes in.

  3. 3
    Lookalike list build (source and score)

    Source candidate accounts that match the profile from LOOKALIKE_SOURCE, excluding current customers and open pipeline. Score each one against the profile, weighting the traits by how strongly they separated wins from losses. Every score shows its inputs: which traits the account hit, which it missed, and where each read came from. Accounts above SCORE_THRESHOLD make the list.

  4. 4
    First-touch angle (the shared situation)

    For each account on the list, write one opener built on the shared situation, not the shared logo. The shape is "companies that looked exactly like you chose us when X", where X is the situational signal the account matches: the hire, the replaced tool, the change. Never name a customer without written permission. The angle works because the situation is recognizable, not because the name is impressive.

Output format

WIN PROFILE · built from 15 wins vs 18 losses (sample data)

Trait                          In wins   In losses   Keep?
Hiring RevOps or SalesOps      67%       22%         YES (3.0x)
Replacing spreadsheet/DIY      53%       17%         YES (3.1x)
New sales leader < 6 months    40%       11%         YES (3.6x)
Mid-market, 100-500 staff      73%       61%         NO (1.2x, noise)

LOOKALIKE LIST · 3 of 41 candidates above threshold 70
Account     Score   Traits hit                      First-touch angle
Northwind   88      RevOps hire, DIY stack, new VP  New sales leader + spreadsheet ops
Vertex      79      RevOps hire, DIY stack          Hiring RevOps onto a manual stack
Acme        74      New VP, DIY stack               New leader inheriting DIY tooling

Next move:
  1. Northwind first. It hits all three separating traits.
  2. The angle for all three: "companies that looked exactly like
     you chose us when the new leader hit the spreadsheet wall."

Tips

  • A profile trait counts only if it separates wins from losses, not wins from thin air. Show the contrast for every trait that makes the profile.
  • Every lookalike score shows its inputs: the traits hit, the traits missed, the source of each read. A bare number is not a score.
  • Never name a customer in outreach without written permission. The default angle is the anonymous shared situation.
  • No angle drafted from a signal the skill cannot source. A guessed situation is worse than a generic opener.
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