Best for
- Sales teams that need better account quality, not just more leads
- RevOps teams trying to turn closed-won patterns into account prioritization
- Founders and GTM teams building a more focused outbound motion
Use your best closed-won customers to find lookalike accounts, explain why each account fits, and give reps a ranked research map instead of a generic lead list.
A ranked list of best-fit lookalike accounts with fit reasons, research notes, buying committee hints, and first prospecting angles.
Quick view
Check the audience, core tools, and access before you start the detailed steps.
Best for
Core tools
6 tools used across the workflow.
Access
Preview the open steps, then unlock the remaining implementation details and prompts.
Why this works
Most lead research starts with broad filters like industry, headcount, and job title. Those filters can produce a list, but they do not explain why an account is worth pursuing now. This workflow starts with actual customers, extracts the patterns behind fit, then uses AI to turn lookalike accounts into ranked research briefs a rep can act on immediately.
Expected results
Research output
25-75 ranked accounts
This is a practical first-pass range after starting with 10-25 seed customers and filtering lookalikes by fit evidence and actionability.
Rep handoff quality
2-minute account briefs
Each selected account has a concise explanation of fit, evidence, uncertainty, and first outreach angle instead of only firmographic fields.
Time saved
6-10 hours per list
AI-assisted pattern detection and enrichment reduce manual CRM review, account discovery, and research brief writing.
List confidence
Fit plus trigger scoring
Accounts are ranked by both resemblance to best customers and signs that they are worth acting on now.
Step-by-step workflow
The first 3 steps are open. Pro unlocks the remaining steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and implementation details.
45 min
45 min
Start in Salesforce and export 10-25 customers that represent your best-fit accounts. Do not only choose the biggest logos. Include accounts with strong retention, fast sales cycles, expansion, strategic fit, and happy champions. Add columns for company name, domain, industry, employee count, ACV, sales cycle length, original trigger, main use case, and why the account worked.
A clean seed account list that shows what high-fit customers actually have in common.
Split the list into two groups: best revenue accounts and easiest-won accounts. They may reveal different ICP patterns, and both can be useful for outbound.
30-45 min
30-45 min
Use Claude to analyze the seed list and identify patterns across firmographics, use cases, triggers, pain points, team structure, and buying process. Ask for hypotheses, not final truth. You want the model to surface why these accounts bought and what traits are likely to predict another good account.
A set of ICP pattern hypotheses that can guide lookalike account discovery.
Do not let Claude stop at industry and company size. Push it to identify operational patterns, buying moments, and internal pressures that are harder to filter but more useful for research.
1 hour
1 hour
Use Keyplay to find and score accounts that resemble your seed customers. Start with the strongest ICP patterns from Claude and create segments around fit signals, firmographics, technology, industry, and company behavior. Export a working list into Clay so you can enrich and inspect accounts before handing them to reps.
A scored lookalike account universe ready for deeper research and prioritization.
Do not send the first scored list directly to reps. Use it as a hypothesis set. The next steps separate genuinely promising accounts from accounts that only match surface-level filters.
Pro workflow preview
Previewing 3 of 7 steps
Get the remaining 4 steps, copy-paste prompts, pro tips, tool-by-tool setup guidance, and ongoing workflow and prompt releases.
$9/month
Analyze these closed-won customer accounts and identify patterns we can use to find lookalike accounts.
Seed customer data:
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Our product or service:
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For each pattern, output:
1. Pattern name
2. Evidence from the seed list
3. Why this pattern may predict fit
4. How we could detect this pattern in public data
5. Whether this pattern is high confidence, medium confidence, or weak
6. Example account signals to search for
Avoid generic filters only. Include buying triggers, operating model, team structure, and business pressure when possible.Related workflows
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