Show Doe your best customers. Get a list that looks just like them.
Your won deals already describe your real ICP better than any filter can. Doe reads them, learns the pattern (size, sector, tech, who signed) and builds a verified list of companies that match, with a reason on every row. No "industry = Software, headcount 50-500" approximation.
Doe reads your closed-won customers, works out the pattern they share, and builds a verified list of companies that match it, instead of a guess from a single industry filter. It studies firmographics, the technologies they run, and who signed, scores each new account against that learned profile with a written reason, verifies the contacts, and writes the list to your CRM. Because it runs as a standing Loop on a schedule, it keeps surfacing net-new lookalikes and skips accounts you already work.
What changes
| Dimension | Before | With Doe |
|---|---|---|
| What defines the target | One industry code + a headcount band you could express | The full pattern your won deals share |
| Why a company is on the list | It matched a filter; you find out the rest on the call | A written reason tying it to your best customers |
| Contact quality | Whoever the database returns; verify later | The persona that signs, verified on output |
| Staying useful | Run once, re-run by hand when it goes stale | Re-runs on a schedule, already-worked accounts excluded |
From your won deals to a verified lookalike list
SalesforceDoe pulled your won opportunities and worked out what they share (segment, size band, the tools they run, the title that signed, even timing signals) instead of you guessing which two attributes matter
Using its native company search, Doe found accounts that fit the multi-attribute pattern that a single industry code in a filter form would never capture
Doe read each candidate’s site and public sources to confirm the harder-to-find signals, like the tech they run and recent moves, the way a research analyst would, so the match is evidence-backed
The Judge ranked every company by how closely it resembles your won deals and attached a one-line rationale, so you see why it made the list. Weak matches are excluded rather than padded in to hit a number
Google SheetsDoe found the buyer who matches the persona that signed your existing deals, verified the email on output, and wrote a ranked, de-duplicated list into a sheet and your CRM with the fit reason on every row
Your best customers have a pattern. A filter form can’t see it.
You can name your three best accounts in your sleep, and you know in your gut what they have in common. But "companies that feel like Acme" is not a filter you can type into a sales database. So you reduce a rich, learned pattern down to an industry code and a headcount band, run it, and get back a list that is technically on-target and practically useless. The thing that made Acme convert was that they’d just switched off a legacy tool and had an ops leader who owned the budget, and none of that fits in a dropdown.
The result is a prospecting list built on the one or two attributes you could express, missing the dozen others that predict a win. You spend the quarter working accounts that match on paper and stall in practice, and the pattern living in your closed-won data, the real one, never makes it into the search.
Get started with the right source material
Add your library and tools
Add or select the source files Doe should use, then connect the tools this task needs. No API keys, no engineering.
Describe what you need
“Look at our closed-won accounts from the last 12 months, figure out what they have in common, and build me a weekly list of companies that match that profile. Find the equivalent buyer at each, verify their work email, rank by fit with a reason, and skip anything already in Salesforce.”
It runs on schedule
Runs every Monday. A ranked, verified list of net-new lookalikes is waiting before your team logs in.
Lookalike Account Finder FAQ
A filter makes you pick the one or two attributes you can express, usually industry and size, and ignores the rest. Doe starts from your actual won deals and reverse-engineers the whole pattern, including signals you can’t put in a dropdown, like the tools they run or the seniority of who signed. Then it scores every candidate against that learned profile with a written reason, so the list reflects why your customers buy, beyond the box they tick.
Stop doing the work your tools should do for you.
Set it up once. Doe runs it every time.
