Is Your Email Verifier Deleting Real Buyers?

TL;DR: Yes, standard email verifiers often delete real buyers because they cannot resolve catch-all domains and default to labeling those addresses catch-all, risky, or unknown, which most outbound teams treat as invalid. In Cedric Lauster's own test, AnyMailFinder confirmed 7 of 10 addresses that MillionVerifier had marked catch-all, meaning a delete-the-catch-all rule would have discarded those real decision-maker inboxes. The fix is to run a second verifier pass on the catch-all slice instead of deleting it outright.

Key Takeaways

• A catch-all domain accepts mail to any address, which means verifiers cannot confirm a specific mailbox and label the result catch-all, risky, or unknown instead of valid.
• Eric Nowoslawski found that his validator marked 73 of 500 proven-real, positive-reply addresses as catch-all, and a second verifier, LeadMagic, validated all 73.
• In Cedric Lauster's own test on 100 small US agencies, AnyMailFinder confirmed 7 of 10 addresses that MillionVerifier had marked catch-all as valid.
• The fix is a second verification pass on the catch-all slice instead of automatic deletion, and only addresses that pass get sent.

Yes, probably. Standard email verifiers cannot resolve catch-all domains, so they label those addresses "catch-all" "risky" or "unknown" and most outbound teams delete that bucket to protect their bounce rate. The problem is that the label says nothing about whether the person exists. It only says the verifier could not confirm the mailbox, and on small-company domains that is a large slice of your real buyers.

I tested this myself in September 2026 on 100 small US agencies. Of 10 addresses MillionVerifier marked catch-all, AnyMailFinder confirmed 7 as valid. Those 7 were real decision-maker inboxes that a delete-the-catch-all rule would have thrown out before a single send. That is the whole problem in one number, and it is worth understanding before you look at your cold email reply rate benchmark and blame the copy.

What is a catch-all domain and why can't verifiers see through it?

A catch-all domain is configured to accept mail sent to any address at that domain, whether the mailbox exists or not. That breaks the one trick verifiers depend on.

Standard verification works by opening an SMTP conversation with the receiving mail server and asking whether a specific mailbox exists. A normal server answers honestly: accepted, or user unknown. A catch-all server says yes to everything. So the verifier gets a positive response for cedric@domain.com and an equally positive response for asdfgh@domain.com, which means the result proves nothing.

Faced with that, most verifiers do the responsible thing and refuse to call it valid. They return "catch-all" "accept-all" "risky" or "unknown." Then most outbound teams treat that as a synonym for invalid and suppress the whole segment.

It is not a rare edge case either. In Eric Nowoslawski's test below, 73 of 500 proven-real inboxes sat in that bucket.

And the distribution is not random. Catch-all setups are far more common at smaller companies with limited IT resources than at enterprises with a dedicated mail admin. Small firms on Google Workspace frequently sit on catch-all configurations without ever thinking about it. That is exactly the owner-run, 2-to-50-person segment that founder-led B2B campaigns are built to reach. The filter bites hardest on the buyers you most want.

How much of your addressable market gets filtered out by delete-the-catch-all rules?

More than the label suggests. In Eric Nowoslawski's test below, 73 of 500 proven-real inboxes were graded catch-all. In my own test on small agencies, 7 of 10 addresses marked catch-all turned out to be valid.

There is a second cost people miss. If most senders delete the catch-all bucket by default, the people in that bucket get less cold email than everyone else. You are not removing a random slice of your market, you are removing the least saturated slice.

And verifiers do not agree with each other on the same address. In my test, two tools disagreed on 7 of the 10 catch-all addresses.

So a list "cleaned" by one tool is not a clean list. It is a list filtered by one vendor's particular blind spot. This compounds with the other way founders accidentally shrink their market, which I wrote about in why industry filters miss most of your TAM. Narrow the industry filter, then delete the catch-all slice, and a 20,000-company market becomes a few thousand contacts who all look the same.

What happens when a verifier calls a real buyer "risky" or "unknown"?

Nothing happens to the buyer. They keep existing, keep opening their inbox, and keep buying from whoever reached them. The only thing that changed is that you removed them from your list.

The sharpest demonstration of this comes from Eric Nowoslawski, founder of Growth Engine X, in a LinkedIn post published on 1 September 2026. He took 500 addresses that had sent his agency a positive reply in the previous day and a half. Every one of those inboxes was proven real, working, and attached to a human who had just typed a reply.

He ran them back through his existing validator. It graded 73 of the 500, 14.6%, as catch-all. His agency's standard practice was to delete catch-all addresses before sending, which means those 73 people would never have been emailed at all. A second verifier that specializes in catch-all detection, LeadMagic, validated all 73.

Read that sequence again. These were not theoretical contacts. They were buyers who had already raised their hand. The validator still said no.

That is the conflation at the heart of the problem, and CleanList.ai names it directly: a catch-all or risky label only means the verifier could not confirm the mailbox. It says nothing about whether the person is a real, relevant buyer who would respond. Teams hear "risky" and translate it to "does not exist." Those are completely different claims.

Nowoslawski's directive is the practical takeaway: benchmark your own verifier the same way, then stop deleting the catch-all slice and route it through a second verifier that can resolve it instead.

What does Cedric's own side-by-side test on small agency lists show?

It shows that on small-company lists the bottleneck is not finding an address. It is the verifier.

In September 2026 I took 100 small US agencies, advertising, marketing and PR firms with 2 to 10 staff, and tried to find a decision maker's email for each one by two different routes.

Route one: database finder plus separate verifier. Prospeo found a decision maker at 35 of the 100 companies and revealed 26 email addresses. Those 26 went through MillionVerifier. It passed 13 as valid and marked 10 as catch-all, with the remainder invalid or unknown. Under a delete-the-catch-all rule, 100 target companies produce 13 sendable contacts.

Route two: find and verify in one step. AnyMailFinder's decision-maker search, which finds and verifies in a single pass, returned 58 valid decision-maker emails from the same 100 companies.

The detail that matters most is the overlap. Of the 10 addresses MillionVerifier had marked catch-all, AnyMailFinder confirmed 7 as valid. Seven real decision-maker inboxes, in a sample of 100 companies, that one route would have deleted and the other route sends to with confidence.

Two caveats, stated plainly. This was one test, on 100 companies, in one segment. It shows a direction, not a universal ratio. And it is a tool comparison, not a campaign result.

What I changed because of it: for small agency and consultancy lists I now run AnyMailFinder first and use the database finder only as a small top-up for the gaps.

In my experience, the list is where most cold email campaigns are won or lost, and the verifier is the least examined part of the list. Founders will rewrite a subject line nine times and never once ask whether the tool that built their send list is silently removing a third of their market. I would rather spend an afternoon testing two verifiers against each other than a month optimizing copy that is only ever reaching the easy half of the market. That is the same logic behind everything in how AI and automation generate leads for B2B: fix the input before you polish the output.

What should you do instead of deleting catch-all addresses?

Treat catch-all as a reason to run a second verification pass, not a reason to delete. The rule I work to is simple and it has four parts.

One external verifier result of valid is required before any address is uploaded. A finder's own confidence score is a hint, never the gate. If the tool that found the address is also the tool grading it, that is not an independent check.

Catch-all triggers a second pass, not a deletion. Route the catch-all slice through a verifier that specializes in resolving accept-all domains. Only what that second pass marks valid goes into the send list. The rest stays out.

Never send to risky or unknown to hit a volume target. A bounce costs more than a lead is worth. If the second pass cannot resolve an address, it does not get emailed. That is the whole point: this approach expands your verified list, it does not lower your standard.

Verify everything, including pre-filled data. Addresses that arrived from a data provider already populated get the same treatment as anything else.

What is a safe bounce rate to protect while you do this?

Low single digits, and you watch it like a hawk.

According to The Digital Bloom's B2B Email Deliverability Report (2025), the average B2B bounce rate was 2.0%, while cold email specifically averaged 7.5%. The 2% figure is the standard worth aiming at. The 7.5% cold email average is what happens when lists are not properly verified, not a target to relax into.

The risk of ignoring this is domain-wide, not batch-specific. Bounces from a blind send land on the reputation of the whole sending domain, not just that batch. That is why the answer is a second verifier and not "send to catch-all and see what happens."

Bounce rate is also your real-world audit of any verifier's label. If a tool says valid and your bounces climb, the tool is wrong, whatever its marketing page claims. Track it alongside your other deliverability inputs, including sending limits per mailbox, which is the other lever that quietly decides whether your mail lands.

How do you check whether your own verifier is throwing away real buyers?

Run the diagnostic on data you already have. It takes under an hour and it needs no new tooling.

1. Pull every address that sent you a positive reply in the last couple of weeks. Those are proven real inboxes attached to real people who engaged. No verifier opinion required.
2. Run that list through your current verifier. Treat it as a blind test. The verifier does not know these people already replied.
3. Count everything it marks catch-all, risky, unknown or invalid. Every one of those is a buyer your verifier would have deleted before you ever emailed them.

If that count is zero, your verifier is fine for your market and you can stop here. If it is anywhere near the 14.6% Nowoslawski found, you have a list problem wearing a copy problem's clothes.

Then use the number to decide what to do next. A meaningful share means you add a second verifier pass on the catch-all slice before your next send, and you re-check whether addresses you suppressed in past campaigns are actually sendable.

One thing this diagnostic will not fix on its own: a bigger verified list only pays off if the conversation after the reply works. If positive replies are not turning into calls at a reasonable rate, that is a separate problem, and I covered where the line sits in what share of positive replies should become meetings.

Where should you start?

Run the positive-reply diagnostic on your own list first. It costs nothing and it tells you within an hour whether your verifier is the reason your campaign feels smaller than your market should be.

If you want help turning that into a working outbound system, with the finder, verifier and sending infrastructure built on your own accounts, book a Growth Mapping Call and I will walk through your current setup with you.

Frequently Asked Questions

What is a catch-all email domain?

A catch-all domain is configured to accept mail sent to any address at that domain, whether the mailbox actually exists or not. This means a verifier's SMTP check gets a positive response for every address it tries, real or fake, so it cannot confirm whether a specific mailbox genuinely exists.

Why do email verifiers mark valid addresses as risky?

Verifiers mark addresses risky or catch-all when the receiving mail server accepts mail for any address, making it impossible to confirm a specific mailbox exists. CleanList.ai (2026) notes this label only means the verifier could not confirm the mailbox, not that the person or address is invalid.

How much of my B2B market could be filtered out by deleting catch-all addresses?

There is no reliable universal figure. In Eric Nowoslawski's test, 73 of 500 proven-real inboxes (14.6%) were graded catch-all, and in Cedric Lauster's test on small agencies, 7 of 10 addresses marked catch-all were valid. Small-company lists are hit hardest, because catch-all setups are more common there.

Which companies are most likely to have catch-all email setups?

Smaller companies with limited IT resources are far more likely to run catch-all configurations than enterprises with dedicated mail admins. This hits owner-run, 2-to-50-person B2B companies hardest, exactly the segment many founder-led outbound campaigns are built to reach.

Can a positive reply prove an email verifier is wrong?

Yes. Eric Nowoslawski tested 500 addresses that had already sent positive replies, proven real inboxes, through his validator, which marked 73 of them (14.6%) as catch-all. A second verifier, LeadMagic, validated all 73, showing the first tool would have wrongly excluded real buyers.

What bounce rate should I aim for when sending cold email?

According to The Digital Bloom's B2B Email Deliverability Report (2025), the average B2B bounce rate is 2.0%, while cold email specifically averages 7.5%. The 2% figure is the target to aim for; the higher cold email average reflects poorly verified lists, not an acceptable norm.

How do I check if my verifier is deleting real buyers?

Pull every address that sent you a positive reply recently, run that list through your current verifier as a blind test, then count how many it marks catch-all, risky, unknown, or invalid. Any meaningful share found this way means your verifier is likely deleting real buyers.