Book a DemoTry for free
Try for free
List building

Build a lookalike list from your best customers

Start from the accounts that actually closed and expand outward on what they share.

Try in Unify

By Unify Documentation reviewed

The conversation

Sample accounts and numbers, for illustration

YouTake our closed-won accounts from the last 18 months, work out what they have in common, and build a list of 200 companies that look like them. Show me which attributes drove each match.

Unify
3 steps · CRM, Ocean.io, BuiltWith
  1. Pulled 41 closed-won opportunities since March 2025 from Salesforce and resolved each to a company.
  2. Enriched them with Ocean.io firmographics and BuiltWith technology, then looked for attributes most customers share and most other companies do not.
  3. Searched for companies that fit the pattern and set aside anything already in the CRM.

Forty-one closed-won accounts from the last 18 months share five attributes strongly enough to search on. Below are the first of 200 matches, each with the attributes that put it on the list.

41
closed-won accounts read
since March 2025
5
attributes that separate them
region was not one
200
matches in the list
ranked by attributes matched
CompanyEmployeesSupport toolLast roundMatched on
Merridian Systems640ZendeskSeries B · 14 months agoAll five
Brightpath410IntercomSeries A · 9 months agoAll five
Halden Robotics880ZendeskSeries C · 20 months agoFour · support team is 12
Lumen Freight260IntercomSeed · 6 months agoFour · below the size band
The pattern
Mid-market software, 200 to 1,200 employees, a support or success team of 15 or more, Zendesk or Intercom in the stack, and a funding round in the last 24 months. Region did not matter.

The 200 are ranked by attributes matched. Companies matching all five sit at the top; the tail matches three.

YouWhich attribute mattered most, and which of our customers does the pattern miss?

Unify

Support-team size separated customers from everyone else more than any other attribute. Six customers fall outside the pattern; all six are enterprise deals that came through partners.

  1. 1Support or success team of 15 or more37 of 41 customersFewer than 8% of candidates before filtering
  2. 2Zendesk or Intercom in the stack34 of 41Common among candidates too, so it narrows less
  3. 3Funding round in the last 24 months31 of 41
  4. 4Employees 200 to 1,20039 of 41Sets the band more than it separates
  5. 5RegionNo effectCustomers are spread evenly
Weight
A support team of 15 or more was present at 37 of 41 customers and at fewer than 8% of candidates before filtering. Support tool came second, funding third. Employee count mostly set the band.
The six outside it
Enterprise accounts closed through a partner. That is a different motion, so the list leaves them out rather than widening the profile to fit them.

YouSave the 200 as an audience, keep it current, and drop anyone who becomes a customer or an open opportunity.

Unify

Done. The audience refreshes weekly, and the CRM check runs on every refresh.

Set up

  • Audience saved: Look-alikes, closed-won pattern 200 companies ranked by attributes matched, with the matching attributes on each record.
  • Weekly refresh Companies that start to fit the pattern join; any that stop fitting leave.
  • CRM exclusion Customers and open opportunities in Salesforce are removed on each refresh.

Review the top 20 before anyone is enrolled. The ranking is evidence-weighted, not a buying signal.

Building the list, with and without Unify

The pattern comes from deals that actually closed, not from a filter someone set a year ago.

Without UnifyWith Unify
The target list starts from an industry code and a headcount band somebody picked last yearUnify reads closed-won first, so the list is built from deals that already happened
Nobody can say what the accounts you won have in common, beyond a hunchThe shared attributes are named out loud: size, stack, structure and who sat on the committee
Every row looks the same, so reps work the top of the list and ignore the restEvery row carries the reason it matched, so a strong fit is easy to tell from a weak one
Customers and open opportunities are spotted by the rep, usually after the email has goneExisting customers, open opportunities and other reps' accounts drop out before anyone sees it

Start from this prompt

Make it your own in Unify, then review the results.

Starting promptEdit in Unify

Take our closed-won accounts from the last 18 months, work out what they have in common, and build a list of 200 companies that look like them. Show me which attributes drove each match.

Try in Unify

What else you can ask for

The walkthrough is one path through this workflow. These are the turns people take most often.

How it changes by who you sell to

Software
Closed-won accounts usually share a stack, a funding stage and an engineering headcount band, and BuiltWith and Ocean.io read all three. Ask for those attributes by name and exclude companies below your seat floor.
Professional services
The pattern is practice area and office footprint, not revenue. Ask Unify to match on practice mix and number of offices, and to skip firms that only list one partner.
Manufacturing
Facility count and product category carry the match; revenue bands are too wide to help. Ask for companies with a similar number of plants in the same product category, and name the regions you can serve.
Retail and e-commerce
Store count, category and commerce platform are the attributes that repeat across your customers. Ask for brands on the same platform in the same category, and set a store-count or order-volume floor.
Financial services
What your customers have in common is regulatory scope and headcount, not revenue. Ask for institutions under the same regulator with a similar operations headcount, and exclude holding companies.
Healthcare
Facility count and system membership are the attributes that carry, and both are public. Ask for systems of a similar size, then ask which facilities under each one look most like your best customers.
Setup

Connect what this workflow uses

Each guide covers the connection, what Unify reads and writes, and what to check before the first run.

Try it with your own market in mind

Start from the prompt above, change the criteria to your segment, and keep asking. Review every output before it goes anywhere.

Try for freeBook a demo

Questions about this workflow

What does Unify use to build a lookalike list?

Your closed-won accounts from Salesforce or HubSpot, matched against Ocean.io firmographics and BuiltWith technology data to find companies with the same attributes.

Does it explain why an account made the list?

Yes. The prompt asks which attributes drove each match, so every company comes with the reason it looks like your customers.

How many closed-won accounts do I need?

The prompt uses the last 18 months. A few dozen wins are enough to read a pattern; fewer than ten and the attributes will be too loose to trust.

Can I exclude companies we already work?

Yes. Connect your CRM and ask Unify to leave out customers, open opportunities and anything closed-lost in the last year.