Sales · Find & Prepare
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.
Works acrossDoeSalesforceWeb ResearchGoogle Sheets
What you get.
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.
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.
What changes.
- 01What defines the targetBefore · One industry code + a headcount band you could expressWith Doe · The full pattern your won deals share
- 02Why a company is on the listBefore · It matched a filter; you find out the rest on the callWith Doe · A written reason tying it to your best customers
- 03Contact qualityBefore · Whoever the database returns; verify laterWith Doe · The persona that signs, verified on output
- 04Staying usefulBefore · Run once, re-run by hand when it goes staleWith Doe · Re-runs on a schedule, already-worked accounts excluded
From your won deals to a verified lookalike list
- 01Reads your closed-won accounts and finds the shared patternSalesforceDoe 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
- 02Searches for companies that match the learned profileDoeUsing 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
- 03Confirms the fit signals on the open web where neededWeb ResearchDoe 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
- 04Scores each account against your best customers, with a reasonDoeThe 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
- 05Finds the right contacts, verifies them, and writes the listGoogle 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
- 06RecurringEvery Monday at 6:00 AMYou set this up once in chat. Doe turns that conversation into a Loop, a scheduled job that re-reads your latest won deals each run, refreshes the pattern, and surfaces only net-new lookalikes, skipping everyone already in your pipeline. As your closed-won set grows, the profile it matches against gets sharper, so the list improves run over run instead of going stale. New lookalikes pushed to your CRM + a summary in #prospecting.
Start with the right source material.
- 01Add your library and toolsAdd or select the source files Doe should use, then connect the tools this task needs. No API keys, no engineering.
- 02Describe 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.”
- 03It runs on scheduleRuns every Monday. A ranked, verified list of net-new lookalikes is waiting before your team logs in.
Before you delegate.
- 01How is this different from an "ideal customer" filter in a sales database?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.
- 02How many won deals does Doe need to find a good pattern?It works with as few as a handful and gets sharper the more you give it. With a small set it leans on the strongest shared signals; with a larger set it can separate the attributes that predict a win from the ones that are just common. You can also tell it to weight specific accounts ("find more like these three") and it will bias the pattern toward them.
- 03Are the contacts and emails verified?Yes, verified on output. Doe uses its native enrichment and a verification waterfall, and only counts a row as complete once it has a deliverable email. Risky, catch-all, or unverifiable addresses are flagged rather than slipped into the list, because a list full of bounces is what gets you throttled by the mailbox providers.
- 04Does the list just repeat the same companies every week?No. The Loop carries an exclusion set across runs, so anything already in your CRM or worked in a prior run is skipped. Each scheduled run re-reads your newest won deals, so as you close more business the pattern updates and the agent surfaces fresh matches. It is not re-printing last week’s list.
- 05Where does the data come from, and is it compliant?Company and contact data comes from Doe’s native search and enrichment plus public web sources it reads the way a research analyst does. Nothing is pulled from behind a login or against a platform’s terms. The fit reason, the source of every field, and the verification status travel with each row, so RevOps can audit why a company is on the list.