Positive Reply Rates in DACH Outbound: Industry Benchmarks 2026

“What’s a good reply rate?” is unanswerable without context

It’s the most common question in outbound — and the most commonly mis-answered: what’s a good reply rate? The honest answer: without industry context, the question can’t be answered. A rate that would be disappointing in B2B software sales can be a top performer in a saturated services market. Anyone benchmarking their campaign against a context-free average from a US blog post is likely drawing the wrong conclusions — and then optimizing in the wrong place.

This article delivers two things: a clean separation of the metrics used to steer outbound campaigns — and concrete industry benchmarks for positive reply rates in the DACH region, drawn from our anonymized campaign dataset as of June 2026.

Positive reply rate, reply rate, meeting rate: what measures what?

Three metrics get routinely confused in outbound, even though they answer completely different questions:

• Reply rate: the share of contacted people who reply at all — regardless of content. It measures whether messages arrive and trigger a reaction.
• Positive reply rate: the share of positive or interested replies among all replies received. It measures whether the reactions that do come in go in the right direction.
• Meeting rate: the share of contacted people with whom a meeting is scheduled. It measures the end of the outbound funnel — but depends on factors far downstream of the first message.

The decisive point: the reply rate also counts rejections, unsubscribe requests, and complaints. A campaign can raise its reply rate while getting worse at the same time — for instance if a more provocative subject line provokes more reactions, but the increase consists of annoyed rejections. Anyone looking only at the reply rate rewards noise.

The positive reply rate is therefore the more honest steering metric for the question that actually matters in outbound: are we reaching the right people with the right message? It filters out the noise and leaves only the signals that pipeline is built from. And it’s available earlier and less distorted than the meeting rate, which additionally depends on calendar logistics, follow-up discipline, and sales cycle — why closing quality still matters in the end is covered in Meeting-to-close rate as North Star. For the layer before that, the raw reply rates by channel, it’s worth looking at Cold email reply rates in B2B.

Important for everything that follows: the positive reply rate is a rate on replies, not on contacts. 100 contacts, 10 replies, of which 4 are interested = 40 percent positive reply rate at a 10 percent reply rate. Anyone mixing up the two reference values is comparing apples to oranges — more on that below.

The benchmarks: positive reply rates in DACH outbound (as of June 2026)

The following figures come from our ongoing campaign operations: anonymized benchmarks across all active workspaces, aggregated at the industry level. No customer sees another customer’s data — what gets shared across industries is exclusively aggregated rates and proven patterns, never raw data.

Industry (DACH)	Positive Reply Rate
B2B Software	51%
Solar / Renewable Energy	38%
GTM Services	27%
Average across all active workspaces	23%

Two notes before you hold your own campaign up against this:

First: the 23 percent average sits below the three named industries because it includes all active campaigns — including difficult niches, early test segments, and campaigns still in calibration. The industry values above show what’s achievable in settled setups.

Second: these figures are orientation, not a guarantee. They describe what’s possible in an industry with a clean ICP, specific messaging, and working deliverability. A single campaign can land above or below without either deviation automatically proving anything.

Why industries perform so differently

A spread of 27 percentage points between B2B software and the overall average isn’t chance and isn’t a quality judgment on individual teams. It has structural causes:

Buying-trigger density. Industries differ massively in how often an acute occasion for purchase arises among target customers. Where funding rounds, team growth, regulatory changes, or tenders continuously create new buying situations, outbound hits open doors more often. Where demand only arises every few years, most people contacted simply aren’t in the market right now — and reply positively less often accordingly. How to systematically exploit such occasions is described in the article on signal-based outbound.

Competitive pressure in the inbox. Decision-makers in heavily courted segments — such as GTM and sales services — receive cold outreach every day from providers promising exactly the same thing. Every additional message there competes against an already jaded filter. In industries where qualified outbound is still rare, the same craftsmanship quality has a much stronger effect.

Deal size and explanation overhead. The larger and more complex-to-explain the offer, the higher the bar for a spontaneous “yes, tell me more”. A clearly defined software problem with a known solution category produces positive replies faster than an offer the recipient first has to mentally place.

Timing sensitivity. Some industries buy in cycles — budget years, funding windows, seasonal business. Solar and renewables are a good example: if outreach hits an open decision window, rates are high; outside of it, the same campaign drops significantly. The industry value averages across both states.

The consequence: a benchmark is only a meaningful comparison within the same industry. A GTM services campaign at 27 percent positive reply rate is performing on benchmark — the same number would be a clear warning sign in B2B software.

Benchmarking your own rate fairly

Before holding your campaign up against the table above, three conditions without which the comparison is worthless:

Same definition. Check what your system counts as “positive”. Does a “not right now, but check back in Q4” count as positive or neutral? Does a forward to a colleague count? Different classification logic shifts the rate by double-digit percentage points. Only benchmark against values whose definition you know — the one used here is: positive/interested replies divided by all replies.

Sufficient volume. Rates on small numbers are noise. Anyone calculating a positive reply rate after 60 contacts and five replies is measuring chance. As an order of magnitude for what reliable volume looks like: in a campaign for Jochen Schweizer mydays, 2,728 contacts went into outreach, with a 6.3 percent reply rate and only a 1.0 percent unsubscribe rate — at sample sizes like that, rates become interpretable, and the low unsubscribe rate also shows that volume and precision aren’t a contradiction.

Measure separately by segment. A campaign that mixes two personas or two industries produces an average that applies to neither. The real signal — segment A works, segment B doesn’t — disappears in the average. Separate measurement per segment is the precondition for turning the rate into a decision.

Below benchmark — now what?

If your own positive reply rate is clearly below the industry value under fair criteria, there’s a proven checking order — from the biggest to the smallest lever:

1. Sharpen the ICP. The most common cause of low positive reply rates isn’t bad copywriting, it’s the wrong recipients: companies without the problem, roles without budget authority, segments without a buying occasion. Before touching the messaging, the target-group definition belongs on the test bench — the toolkit for that is in the guide to ICP definition in B2B.
2. Make the messaging more specific. Generic messages produce generic reactions — meaning mostly rejections. The more precisely the message hits the recipient’s industry, situation, and likely occasion, the higher the share of interested replies. Specificity beats creativity.
3. Check the channel mix. Some target groups are practically unreachable by email but reply on LinkedIn — and vice versa. Anyone running only one channel may be measuring their target group’s channel preference rather than the quality of their outreach.

And an honest caveat to close this list: there are situations where even a craftsmanlike, clean campaign stays below the industry benchmark — for instance because the offer sits in a niche with structurally few buying occasions. Benchmarks show what’s achievable, they don’t replace a business-model diagnosis.

Where such benchmarks come from — and how to work with them

Reliable industry benchmarks don’t come from surveys, they come from ongoing operations: from many parallel campaigns whose replies are classified consistently and whose rates are aggregated across industries. That’s exactly how the numbers in this article came about — as an anonymized evaluation across the active workspaces on GTM Goat, our GTM system for the DACH region. The practical value lies less in the table itself than in what’s behind it: anyone starting out in an industry doesn’t have to find out from scratch what outreach works there, but starts with patterns already proven in that industry — while their own positive reply rate is measured cleanly and by segment from day one. How such a system learns from every reply is described in the article on closed-loop outbound.

If you want to know where your industry stands and what your current rate means in comparison: via contact, this can be assessed concretely against your own numbers.