LinkedIn Reply Rate Benchmarks: What Good Looks Like in 2026

Nearly every request we get for LinkedIn reply rate benchmarks is really a request for reassurance. Someone is running a campaign, the number on the dashboard is 11 percent, and they want to know whether that is a crisis or a Tuesday.

The honest answer is that 11 percent is meaningless without three other pieces of information: how broad the list was, how senior the recipients were, and how fast the team answered when people replied. A global average across all of those variables is not a benchmark. It is an average of things that should never have been averaged.

So this post does the segmented version. The ranges below come from campaigns we have observed directly and from anonymised, aggregated activity inside Replaiy accounts. They are working bands, not published industry standards, and your own baseline will beat any of them as a management tool.

LinkedIn reply rate benchmarks by list quality

List quality explains more variance than anything else we measure: more than copy, more than seniority, more than send volume.

List type	Acceptance rate	Reply rate (of accepted)	Positive reply share
Named-account list with a recency trigger	40-55%	25-40%	30-45%
Tight ICP, filtered on role plus evidence	30-40%	15-25%	25-35%
Broad title-and-headcount filter	20-30%	8-14%	15-25%
Scraped or purchased list, minimal filtering	10-20%	3-8%	5-15%

The pattern worth internalising is that the metrics move together. A campaign does not usually have strong acceptance and weak replies; when it does, the cause is almost always a mismatch between the reason people accepted and the message they then received.

How reply rates change by seniority

Seniority does not simply reduce reply rate. It changes the shape of the reply.

• Individual contributors and managers reply most often, but a larger share of those replies are informational: questions, redirects, “we don’t own that here”.
• Directors and VPs reply less often, and their replies are more decisive in both directions. A yes from this band converts to a meeting at a noticeably higher rate.
• C-level at companies under 200 people behaves more like the director band than most people expect, provided the message is short and specific.
• C-level at larger companies has the lowest reply rate of any segment and the highest referral rate. A reply is often a handoff to someone two levels down, which is a good outcome that most dashboards score as neutral.

If you are running a single blended reply-rate target across all four bands, you are systematically punishing the reps working the hardest accounts.

Reply rate by sequence step

Each additional message costs you something: goodwill, report-rate risk, and the reps’ time. It is worth knowing where the returns stop.

Sequence step	Share of total replies	Notes
First message after acceptance	~50%	Highest positive share. Keep it short and do not ask for time.
Follow-up 1	~30%	Works best when it adds a new fact, not a reminder.
Follow-up 2	~15%	The permission-to-close message performs disproportionately here.
Follow-up 3+	~5%	Marginal. Measurably raises negative sentiment.

The practical implication: a three-touch sequence to a good list beats a six-touch sequence to a mediocre one, and it costs you less account risk. If you are tempted to add a fifth message, add a segment instead.

Why industry averages are worse than useless

There is a reason we resist publishing a single headline number, and it is not modesty.

A blended average takes campaigns with 5x differences in list quality, seniority mix, offer strength and response speed, and collapses them into one figure. The result describes no real campaign. Worse, it gives teams a target that is achievable by accident on a narrow list and impossible on a broad one, which pushes managers toward the wrong diagnosis.

The specific harm is misattribution. A team benchmarking against a 20 percent average, sitting at 9 percent, will nearly always conclude the copy is wrong, because copy is the visible, editable thing. In audits, the cause is the list roughly four times out of five. Months get spent testing subject lines on an audience that was never going to answer.

There is also a survivorship problem. Benchmark figures circulating publicly come disproportionately from teams and vendors with a reason to publish them, which is to say from campaigns that went well. Nobody writes up the quarter where the reply rate was 4 percent.

Use external numbers for one purpose only: checking that you are in the right order of magnitude. If you are at 2 percent, something is structurally broken. If you are at 14 percent, no external figure can tell you whether that is good. Only your own segmented history can.

The metric almost nobody instruments

Reply rate measures whether people answer you. It says nothing about whether you answered them.

Across the campaigns we look at, median first-response time varies by more than two orders of magnitude between teams, from a few minutes to several days, and it correlates with reply-to-meeting rate more strongly than any copy variable we can isolate. That relationship, and how to instrument it, is the subject of why response time decides who books the meeting.

Reply rate is a top-of-funnel vanity metric the moment your inbox is the bottleneck. Two teams with an identical 20 percent reply rate can differ by 3x in meetings booked.

Build your own baseline in four weeks

Benchmarks borrowed from someone else’s campaigns are a sanity check. A baseline built from your own is a management tool. Here is the minimum viable version.

1. Pick one segment and freeze it. Do not change targeting mid-measurement. You need a clean denominator more than you need a big one.
2. Run 200 to 300 contacts through an unchanged sequence. Fewer than that and week-to-week noise will swamp any effect you are trying to see.
3. Tag every reply by shape, not just by sentiment: interested, not now, wrong person, objection, question, refusal. Six buckets is enough.
4. Log first-response time on every thread. If your tooling will not do this automatically, a timestamp in a spreadsheet is fine for four weeks.
5. Compute four numbers: acceptance rate, reply rate on accepted, positive reply share, and reply-to-meeting rate. Publish them per segment, never blended.
6. Change one variable and repeat. Copy first is the wrong instinct. Change the list.

What to do when your numbers are below the band

Work in this order, because it is roughly the order of effect size.

First, narrow the list. Cut the worst-performing segment entirely rather than rewriting its copy. Most campaigns improve more from removing 30 percent of the list than from any message test.

Second, fix response time. If replies sit for a day, you are losing meetings that your reply rate already earned you. This is where teams get the fastest improvement without touching targeting, and it is where an AI layer like Replaiy pays for itself: it drafts or sends the reply while the prospect is still in the app, rather than when the rep next opens the tab.

Third, shorten the first message. In audits, over-long openers are the most common single copy fault. One observation, one question, no link.

Fourth, then test copy. By this point you will have a clean enough baseline to actually read the result.

For the wider context on how these metrics fit into a campaign, start with our complete guide to LinkedIn outbound in 2026, and for turning replies into booked time, see the objection handling playbook for LinkedIn DMs.

The short version

Treat any single-number LinkedIn reply rate benchmark with suspicion. Segment by list quality first, seniority second, sequence step third. Then measure your own campaigns for four weeks and manage against that, because the only benchmark that can tell you whether you improved is the one you produced yourself.