You already know I like showing my work.
So this week, we’re cracking open the clone magnet.
No APIs.
No enrichment credits.
Just Clay formulas, Apollo data, and 1,000 B2B SaaS accounts ready to test.
This one started with a client project —
They asked for a master list, plus some lookalikes.
And I wanted to find a way to do it without spinning up a whole enrichment stack on day one.
🧪 What I realized…
Apollo gives you a lot more than people think.
Tech stack
SEO + metadata + keyword data
Revenue + funding
Employee Count (not just range)
Most of y’all are sleeping on the keyword field.
But it’s a goldmine - especially if you know how to work formulas.
🛠️ So I built a system...
Here’s how it works:
Pick 25 of your best-fit customers
Grab their Apollo keyword fields
Extract the common themes with AI
Match those keywords against a big list of accounts
Use formulas to count keyword overlaps
Sort by matches - your lookalikes rise to the top
🎁 I’m giving you my list
To help you try it yourself, I’m dropping:
✅ A 1,000-account B2B SaaS list
✅ Website traffic (Feb 2025)
✅ Parsed tech stack (CRM, ESP, SEP, etc.)
✅ BDR headcount
✅ Apollo keywords
All pre-enriched. All ready to run.
This was part of a much bigger project for a client —
but I trimmed it down so you can test the core idea fast.
🧠 What’s the play?
This isn’t the final list.
It’s the first filter - the foundation.
Once you’ve got your matches, then you enrich.
Then you layer in funding data, GTM roles, intent signals.
But this step saves you time and credits upfront.
And honestly?
The accounts with the most keyword matches tend to be
the ones that close fastest.
⚙️ Want to build it yourself?
Let’s get surgical with outbound.
Formulas first. Credits later.
🧪
Shawn
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