Cold Email Statistics: Every Major Study in One Table

Public cold email studies put the average reply rate anywhere from 0.45% (Belkins, 7.5 million emails sent in 2025) to 4.5% (Hunter, 31 million emails sent in 2025, and Saleshandy, 30.3 million emails in 2026). Follow-ups account for somewhere between 42% and 57% of replies, average bounce rates sit around 2% to 4%, and every study that tested open tracking found replies went up when it was switched off. The numbers disagree mostly because each publisher measures a different thing.

Below is every major public study we could find in one table, followed by the statistics grouped by topic, and a section on why they conflict. All pages were fetched on 9 October 2026, and we only quote figures that appeared on the source page. If you want the deep dive on reply rate formulas specifically, that is in cold email reply rate benchmarks; this post is the wider roundup.

The studies, side by side

Nine sources are quoted again and again in cold email articles. They differ in sample, period, use case and, above all, in how they define a reply.

Publisher	Sample stated	Period	How "reply rate" is defined	Headline number
Belkins	7,530,489 emails, 34,393 replies	Jan to Dec 2025	Unique replies ÷ emails sent, excluding auto-replies and bounce notifications	0.45% average reply rate
Instantly	"Billions of cold email interactions"	1 Jan to 18 Dec 2025	All replies, including follow-up responses ÷ total emails sent	3.43% average; top 10% at 10.7%+
Hunter	31 million emails sent by Hunter users, plus surveys	2025	Not defined; framed per recipient ("for every 1000 recipients... only 45 replied")	4.5% average sequence reply rate
Saleshandy	30,314,409 emails, 40,006 sequences	Stated as July to October 2026	Total replies ÷ total emails delivered	4.5% average; best campaigns 15%+
Snov.io	"Over 44 million emails" in one section; "10M+" in the header	Feb to Mar 2025 (for the 44M analysis)	Not defined on the page	2.36% reply rate with open tracking off vs 1.08% on
Gong	"28M+ cold emails"	Not stated	Not stated; reports ratios between top and average reps	344 cold emails per booked meeting for the average rep
lemlist	"Millions of cold emails"	Not stated	Not stated	4.5% for email 1; up to 22.37% across 10 emails
Backlinko and Pitchbox	12 million outreach emails	Page last updated April 2019	Not set out on the page	8.5% of outreach emails got a reply
Mailshake	Survey of 508 outbound professionals	Not stated	Self-reported	Average reply rate of 1% to 4%

Two things stand out before looking at any individual figure. Only Belkins publishes a sample and a definition precise enough to recompute its headline (34,393 ÷ 7,530,489 = 0.457%). And the two biggest "4.5%" figures, Hunter's and Saleshandy's, use different denominators: one per recipient, one per email delivered. They agree by coincidence, not because they measured the same thing.

One more caution on the Saleshandy page. It is widely shared under the title "I Analyzed 53M Cold Emails", but when we fetched it the headline read 30.31 million, the stated period was July to October 2026, and the summary and methodology gave different counts of sending accounts (2,906) and mailboxes (about 39,800). Studies get revised in place, so check the live page before you quote one.

Reply rate statistics

The reply rate figures that matter most are the ones that show what moves the number, not the averages. The averages are in the table above; these are the comparisons inside individual studies, where the formula at least stays constant.

• By use case (Hunter, 2025): sales outreach 3%, marketing 6.2%, headhunting 7.5%, digital PR and link building 13%. This is why a link-building study like Backlinko's 8.5% should not be used as a sales benchmark.
• By company size (Belkins, 2025): 0.72% for companies with 0 to 10 employees, 0.49% for 11 to 50, and 0.22% for 10,000+.
• By seniority (Belkins, 2025): founders and owners 0.57%, C-level 0.42%, VPs 0.32%. Gong separately reports that C-level executives are "30.2% less likely to reply to cold emails than non-executives".
• By industry (Belkins, 2025): Food and Beverage led at 3.47%; construction, financial services, healthcare and legal sat between 0.56% and 0.60%.
• By sender domain (Hunter, 2025): 5.2% from a custom domain against 2.5% from freemail addresses.
• Distribution (Instantly, 2025): the top quartile starts at 5.5% and the top 10% at 10.7%, against a 3.43% average.

Notice that Belkins' entire industry range would sit below Instantly's average. That is the denominator and the auto-reply rule at work, not a sign that Belkins' clients write worse emails.

Follow-up statistics

Every study that measured it found follow-ups add a large share of replies, but they disagree on how large.

Source	Finding
Instantly (2025 data)	Step 1 produces 58% of replies; follow-ups produce 42%
Saleshandy (2026 data)	Follow-ups 1 to 5+ account for 29.0%, 15.3%, 7.3%, 2.9% and 2.1% of replies, about 57% combined
Hunter (2025 data)	One message: 3.3% total reply rate. Three messages: 6.8%
lemlist	4.5% for the first email, then 3.75%, 2.75% and 2.1% for emails two to four
Backlinko and Pitchbox (2019)	One extra follow-up raised replies by 65.8%

Saleshandy's per-step reply rates also show the decay clearly: 0.80% on the first follow-up falling to 0.37% by the fifth and later steps. Each extra step still produces replies, but each also lands in front of people who have already ignored you, which is where spam complaints come from. We work through that trade-off against Gmail's complaint threshold in how many follow-ups a sequence should have.

Snov.io adds a useful counterpoint. In its data, campaigns with and without follow-ups had the same average per-email reply rate (0.41%), while follow-up campaigns produced more replies in total (9,678 against 8,769). A per-email rate can stay flat while a sequence produces more conversations, which is another reason to read the formula before the number.

Bounce rate statistics

Bounce figures are the most consistent statistics in cold email, and the clearest link between list quality and results.

• Hunter, 2025: 3.6% average bounce rate across its users' sends.
• Saleshandy, 2026: 1.21% for addresses verified as valid, 2.11% for unverified, 2.22% for risky and 10.58% for addresses flagged bad. The section heading on that page claims "unverified lists report a bounce rate of 40%", but no figure in the body supports it, so we have left it out.
• Instantly: recommends keeping bounces under 2%, without publishing a measured average.

The Saleshandy breakdown is the one worth remembering. Sending only to addresses a verifier marked valid roughly halved the bounce rate compared with sending blind, and the "bad" bucket bounced at nearly nine times the valid rate. Bounces matter beyond the wasted send: mailbox providers read them as a sign you don't know who you're writing to. For the platform thresholds and other published averages, see email bounce rate benchmarks, and for the causes, why cold emails bounce.

Open rate and tracking statistics

Open rates are the least trustworthy number in this roundup, and two independent datasets suggest measuring them costs replies.

• Average open rates: Hunter reports 30% for 2025; Saleshandy reports 21% and calls the metric "directional". Both rely on a tracking pixel, which Apple Mail Privacy Protection and security scanners can fire without anyone reading the email. Our open rate benchmarks post explains why the averages vary so much.
• Open tracking off vs on (Hunter, 2025): 7.4% reply rate without open tracking, 4.4% with it.
• Open tracking off vs on (Snov.io, 44 million emails, February to March 2025): 2.36% without, 1.08% with.
• Link tracking (Snov.io): emails with link tracking showed a 0.87% reply rate against 0.21% without. This runs the other way from open tracking and has no stated explanation on the page, so treat it as unexplained rather than a recommendation.

Two different datasets, two different formulas, same direction on open tracking. That is about as strong as cold email evidence gets. The likely mechanism, a remote image and often a shared tracking domain in every message, is covered in open tracking and cold email deliverability.

List size, volume and targeting statistics

Smaller, tighter sends get higher reply rates in every study that split by size.

• Hunter, 2025: sequences with 21 to 50 recipients replied at 6.2%; 500+ recipients at 2.4%. The average sequence had 449 recipients.
• Saleshandy, 2026: sequences under 200 prospects reached 15% to 20%, 200 to 500 reached 11% to 13%, and 500 to 1,000 reached 8%.
• Hunter, 2025: accounts sending 20 to 49 emails per day per account replied at 5.7%, against the 4.5% overall average.
• Backlinko and Pitchbox, 2019: emailing several contacts at the same organisation produced a 93% higher response rate than emailing one, with diminishing returns at five or more.

Some caution is due. Small sequences are usually the hand-built ones, so size is standing in for effort and fit. Still, the per-inbox figure lines up with what mailbox providers reward: steady, modest volume from each sender. Our guide to cold emails per day per inbox covers the provider limits behind that.

Length, personalisation and content statistics

On length, most studies agree on short. On personalisation, the measured effects are smaller than the claims around them.

• Gong: reply rates "drop sharply once emails exceed 100 words"; the best performers sit between 50 and 100 words. Subject lines of 1 to 4 words had the best open rates.
• Instantly: top performers keep emails under 80 words.
• Gong (28M+ emails): pitching in the email body reduces reply rates by "up to 57%".
• Snov.io: personalised emails replied at 0.58% against 0.42% for generic ones.
• Backlinko and Pitchbox, 2019: personalised body copy got 32.7% more replies; personalised subject lines 30.5% more.
• Saleshandy, 2026: a single soft call to action produced 78% more positive replies than hard calls to action.

One result cuts against the consensus. Snov.io reports a 0.54% reply rate for emails under 100 words and 1.06% for its 5,000+ word group, without explaining what kind of email runs that long. It is a good example of why a single cross-tab from one platform should not override several studies pointing the other way. If you're writing the email itself, how to write a cold email puts these findings into practice, and personalisation at scale covers the data side.

Meetings and multichannel statistics

The studies disagree most sharply on the number that matters most: how many emails it takes to book a meeting.

• Gong: the average rep needs "344 cold emails to land one meeting". Top 10% of reps book 8.1 times as many meetings as average reps.
• Saleshandy, 2026: top campaigns book 2 to 3 meetings per 100 cold emails.

Those figures are roughly seven to ten times apart, and they describe different groups (average reps against top campaigns), so both can be true. Saleshandy also reports positive reply rates by channel mix: 0.11% for email only, 0.21% for email plus LinkedIn, and 0.28% for email plus calls, with 99% of its sequences being email only.

Why the numbers disagree

Five things explain nearly all the conflict between these studies. The reply rate post goes deeper on the formula problem; here is the short version across all metrics.

1. The denominator. Per email sent, per email delivered, per recipient, or per opener. A four-step sequence roughly quadruples the sends, so per-send rates fall as sequences get longer even when more people reply.
2. What counts as a reply. Belkins removes auto-replies and bounce notifications. Instantly counts "all replies". Most others don't say. Out-of-office messages alone can move a per-send rate noticeably.
3. Who is in the sample. Platform data covers that platform's active customers; agency data covers professionally run campaigns; Backlinko's covers link-building outreach. Hunter's own split shows digital PR replying at more than four times the rate of sales outreach.
4. When and how the page was written. Belkins changed its denominator between editions. Saleshandy's headline and period changed between the version most people cite and the live page. Snov.io's header and body give different sample sizes.
5. Citations that loop. Many "statistics" pages quote each other. Woodpecker's roundup, for instance, attributes its headline 3.43% to Instantly. Three articles agreeing may be one study quoted three times.

Every publisher in the table sells cold email software, data or services. That doesn't make the numbers false, but it is a reason to favour the studies that publish their sample and definition.

How to use these statistics

Use them for direction, and use your own data for targets. The statistics that agree across independent studies are the ones worth acting on:

• Follow-ups add a large share of replies, with sharply diminishing returns per step.
• Smaller, better-targeted sends reply at higher rates.
• Open tracking is associated with lower reply rates in both datasets that tested it.
• Verified addresses bounce far less than unverified ones.
• Short emails without a pitch in the body outperform long ones in most studies.

None of that needs a benchmark to apply. For your own numbers, measure per delivered prospect with auto-replies removed, label the formula every time you report it, and compare this month with last month rather than with someone else's platform average. Keep spam complaints well inside Google's guidance, which says to stay below 0.10% and "avoid ever reaching a spam rate of 0.30% or higher"; the requirements are summarised in Gmail and Yahoo bulk sender rules.

The cheapest statistic to improve is bounce rate, because it depends only on the list. Before your next campaign you can check a sample with the free email verifier or run a whole file through the free list health check for duplicates, typos and dead domains. If you send at volume, SimpleVerifier's monthly plans for full lists are on the pricing page.