Quick answer
The "single CTA beats multiple CTAs by 35-42%" stat that shows up across 2026 cold email guides is usually credited, implicitly or explicitly, to Gong's 304,174-email CTA study. Gong's own published post does not contain this figure anywhere. Tracing it further, the number appears to originate with SmartReach.io's State of Cold Email 2026 report, and even that report attributes it to an unnamed "aggregated outreach copy analyses" citation rather than its own first-party dataset. The advice itself, use one CTA, is still reasonable. The specific percentage backing it is not something you can currently verify.
The claim I'm checking
I'm Hlib Storchak. I build outbound systems for B2B founders and sales teams, 2000+ meetings booked for B2B clients so far, and part of that job is knowing which numbers I quote to clients are actually load-bearing and which ones are just repeated often enough to sound true. A stat that keeps showing up in cold email guides this year: "one clear call to action beats multiple competing ones by 35-42%." It's specific enough to sound researched, it's repeated across more than one site, and it gets waved at readers as a reason to cut a second CTA from their template. I went looking for where it actually comes from before I'd put it in front of a client.
Where the stat shows up, and what it's credited to
The 35-42% figure appears on SmartReach.io's own State of Cold Email 2026 report and on growleads.io's writeup of cold email CTA research, both stated in nearly identical language. Growleads' page sits right next to its coverage of Gong's 304,174-email CTA study, the same study I already traced in a previous teardown for a different claim, interest-based versus meeting-request CTAs. The two claims, CTA type and CTA count, are different questions, but they get discussed on the same pages often enough that a reader skimming for cold email best practices could reasonably assume both numbers come from the same Gong dataset. I wanted to check that assumption directly rather than repeat it.
What Gong's own post actually says
I went back to Gong's own published post and read it specifically for anything about the number of CTAs per email, not just CTA type. It isn't there. The post's entire analysis is about three CTA styles, interest, open-ended, and specific, and how each converts at the cold stage versus the deal stage (15% for a specific CTA cold, rising to 37% once a deal is open, the one hard number the post actually states). There is no sentence, table, or footnote anywhere in the visible text comparing single-CTA emails to multi-CTA emails. Whatever the 35-42% figure is measuring, it isn't measuring what Gong's 304,174-email sample measured.
Tracing it one level further
Since Gong wasn't the source, I went looking for where the 35-42% number actually lives. SmartReach.io's State of Cold Email 2026 report states it plainly in its Chapter 7 section on offers and calls to action: "one clear call to action beats multiple competing ones by 35-42%." That's the closest thing to an original statement of this exact figure that I could find, worded almost identically to how growleads.io and other sites repeat it. So far this looks like a real, dated, named report, not an anonymous aggregator page. The next question is whether the report's own data actually backs the number it's stating.
What SmartReach's own report says about its own number
This is where it gets interesting. SmartReach's report does have real first-party data behind it: 40M+ emails sent to 6M+ prospects, across 5,000+ campaigns from 400+ accounts, including 100+ agencies, covering January to June 2026. That's a legitimate dataset, and other numbers in the same report are drawn directly from it. But the 35-42% single-CTA figure specifically isn't one of them. The report's own source list attributes it to citation [7], described only as "aggregated outreach copy analyses, 2025 to 2026," with a note in the sources section that exact citations for that entry are "being finalised." In plain terms: the report's own words tell you this particular number isn't from its 40-million-email dataset, it's from an unnamed, unstated set of outside analyses that the report itself hasn't fully sourced yet.
Tip. A report having genuine first-party data doesn't mean every number inside it comes from that data. Check each specific stat against its own footnote, not the report's overall sample size.
First-party data versus a cited number, and why the gap matters
This is a distinction I check for on every stat I plan to reuse, and it's easy to miss because a report's headline sample size does a lot of persuading on its own. Forty million emails and 100+ agencies is a real, credible number, and it makes every other claim on the same page feel more solid by association. But a report can run genuine first-party analysis on some questions (send timing, subject line length, follow-up cadence) while sourcing a different, more specific-sounding claim from an outside citation it hasn't fully documented yet. The two kinds of numbers read identically on the page. Only the footnote tells you which is which, and most readers, understandably, don't click through to the sources section at the bottom of a long report.
The source chain, laid out
| Link in the chain | What it actually says | Verified how |
|---|---|---|
| Gong's 304,174-email CTA study | Compares CTA type (interest vs. specific), not CTA count. No 35-42% figure anywhere in the post. | Read the published post directly |
| SmartReach's State of Cold Email 2026 | States "35-42%" in its own text, but attributes it to citation [7], not its own 40M-email dataset. | Read the report's Chapter 7 and its sources section directly |
| Citation [7] ("aggregated outreach copy analyses") | No named study, publication, sample size, or date beyond "2025 to 2026." Marked as still being finalised. | Read the sources list as published |
| growleads.io and similar secondary pages | Repeat "35-42%" with no citation of their own attached to that specific line. | Read the page directly |
Why the underlying advice is probably still right
None of this means "use one CTA" is bad advice, and I'm not arguing the direction is wrong. A single clear ask giving the reader one easy decision instead of two competing ones is a reasonable behavioral claim, and it matches what I see across client accounts: a second CTA, even a soft backup option like "or happy to jump on a call," forces a reader to choose between two asks instead of answering one. What I can't currently back with a verified number is the specific size of that lift. "35-42%" is precise enough to sound like it came from a controlled test. What it actually traces back to is one report's citation to an unnamed source that hasn't published its own methodology yet. Those are different levels of confidence, and a reader deserves to know which one they're getting.
My take
The mistake I see most often when I take over an account isn't picking the wrong CTA structure, it's a rep quoting a specific-sounding percentage to a manager or a client as if it settled the question, when the number was never checked past the first site that used it. I still recommend single-CTA emails to every client, for the same behavioral reason stated above, but I say it as "this consistently performs better in my own send data and matches the general pattern across studies I trust," not "single CTAs lift replies by exactly 35-42%, per Gong." The first claim is honest and still persuasive. The second one collapses the moment someone checks it, and once a client catches you repeating an unverifiable number, they start doubting the ones you can actually back up.
A six-step checklist for tracing any stat like this
Run any specific-sounding cold email percentage through this before you repeat it, especially one attached to a named study like Gong's:
1. Find the number's most-cited primary source. Not the blog post that quotes it, the original study or report it's attributed to.
2. Open that primary source directly. Don't trust a summary of a summary. Search the page itself for the exact number or the metric it claims to measure.
3. Check whether the primary source actually measures that question. A study about CTA type isn't automatically a study about CTA count, even if both get discussed under "cold email CTA best practices."
4. If it's not in the primary source, look for where the number was first stated. Search the exact percentage in quotes. The report or page that states it most specifically, with a date and a named methodology, is usually closer to the origin than a listicle repeating it.
5. Check that source's own citations. A named, dated report with a real sample size can still attribute a specific line to an outside, unnamed source. Read the footnote, not just the report's overall credibility.
6. Decide what confidence level you're actually quoting. "This is this vendor's own first-party finding," "this is a stat this report cites from an unnamed source," and "this is a number nobody can currently source" are three different claims. Say which one you're making.
Key takeaways
- The "single CTA beats multiple CTAs by 35-42%" figure does not appear anywhere in Gong's own 304,174-email CTA study, verified by reading the post directly.
- The number traces most directly to SmartReach.io's State of Cold Email 2026 report, which states it but attributes it to an unnamed "aggregated outreach copy analyses" citation, not its own 40M-email first-party dataset.
- A report can mix genuine first-party findings with cited outside numbers on the same page. The overall sample size doesn't vouch for every individual stat inside it.
- The behavioral case for a single CTA still holds on its own logic and matches patterns I see across client accounts. The specific 35-42% figure just isn't something you can currently verify.
- Before repeating any specific-sounding cold email stat, trace it to its named primary source, open that source directly, and check whether it actually measures the claim being attached to it.
Where a shaky source still doesn't mean a wrong number
One honest caveat: an unsourced citation isn't proof the number is false, only that it's currently unverifiable. Thirty-five to forty-two percent is a plausible range for a well-designed single-CTA test, and it's entirely possible some agency ran that exact analysis internally and it fed into SmartReach's "aggregated outreach copy analyses" bucket without a public writeup. The honest position is not "this number is wrong," it's "this number cannot currently be checked, so don't cite it as if it were audited." If SmartReach or another named source ever publishes the underlying study behind citation [7], that would change the confidence level. Until then, I'd cite the direction and skip the specific percentage.
FAQ
Does Gong's cold email study say single CTAs get 35-42% more replies?
No. Gong's published post covers CTA type, interest-based versus specific meeting asks, not the number of CTAs per email. There is no 35-42% figure, or any single-vs-multi-CTA comparison, anywhere in Gong's own text.
Where does the 35-42% single-CTA stat actually come from?
The most direct source I could find is SmartReach.io's State of Cold Email 2026 report, which states the figure in its Chapter 7 section on offers and CTAs. The report itself attributes that specific number to an unnamed "aggregated outreach copy analyses, 2025 to 2026" citation, not to its own 40M-email first-party dataset.
Should I stop using a single CTA in cold emails?
No. The behavioral logic for a single, low-friction ask over competing CTAs is sound and matches what I see across client accounts. Just don't quote "35-42%" as an audited fact when you make that case, since it currently traces to an uncited source.
How do I check a cold email stat before I use it with a client?
Find the study it's credited to, open that source directly rather than a summary of it, and confirm the source actually measures the specific claim attached to it. If the number isn't there, search for where it was first stated with a name, date, and methodology attached, and check that source's own citations too.
Is a report with a large sample size automatically trustworthy for every stat it publishes?
Not automatically. A report can run genuine first-party analysis on most of its findings while sourcing one specific, more precise-sounding claim from an outside citation it hasn't documented. Check the footnote on the individual number you plan to repeat, not just the report's overall dataset size.