Between 1 May and 10 July 2026, around 10,000 outbound messages were sent through SalesXMachina (exactly 9,925): 7,834 emails, 729 LinkedIn direct messages and 1,362 LinkedIn connection requests. Recipients were just under 6,000 contacts at around 2,500 companies, mostly in the DACH region.
We did not just count this data, we ran the numbers: 14 hypotheses, each tested statistically on its own, plus a screening across 17 further variables. Every incoming reply was read and categorised until no leftover category remained. This is the result. No opinions, just numbers.
What comes back when you send 10,000 messages
- 441 replies in total, 31.5 percent of them out-of-office notices
- 209 replies came from real people. That is a reply rate of 3.5 percent of the people contacted
- By far the most common human reply (40.7 percent): no need right now, get back to me later
- Around 9 percent of human replies are genuine interest, meaning a request for a meeting or an analysis
Sounds sobering. But only at first glance, because the spread between what works and what does not is enormous. The best segments in our data reply 13 times more often than the worst.
What works: the confirmed levers
1. LinkedIn beats email by a factor of 8
Reply rate per message: LinkedIn direct message 8.2 percent, LinkedIn connection request with note 4.6 percent, email 0.97 percent. Email made up 79 percent of our volume but delivered only just over a third of the replies. The channel is the single biggest lever in outbound.
2. An accepted connection request is worth its weight in gold
Contacts you are already connected with (1st degree) reply at 25.2 percent. Everyone else at 1.9 percent. That is a factor of 13. Every activity that gets connection requests accepted pays off several times over later.
3. Researched hooks beat generic signals
Messages with an individually researched hook achieved a 10.6 percent reply rate. Generic triggers such as expansion (1.9 percent) or leadership changes (0.9 percent) fell well behind. It is not the signal itself that sells, but the visible homework behind it.
4. Job changers reply almost twice as often
Campaigns targeting contacts who had recently changed jobs reached 4.6 percent versus 3.0 percent for the rest. The effect also shows at the individual level: people less than 6 months into their role reply at 5.9 percent, everyone else at 3.0 percent. New role, new budget, open mind.
5. Following up quickly keeps the conversation alive
When the follow-up came within 24 hours of a reply, there was a second reply in 47 percent of cases. Those who waited longer only got 26 percent. Speed after the first reply is one of the easiest levers to pull.
6. A well-kept LinkedIn profile on the recipient's side is a buying signal
Contacts with a filled-in LinkedIn bio replied on LinkedIn at 10.4 percent, contacts without a bio at 6.7 percent. People who maintain their profile are active on the platform and therefore reachable.
7. Mid-sized companies reply, corporates stay silent
Companies with 51 to 200 employees: 4.6 percent reply rate. Companies with more than 5,000 employees: 1.6 percent. If you target both, weight your volume accordingly.
8. Sunday is the underrated sending day
Messages sent on Sunday achieved 4.1 percent versus 2.0 percent on the weekly average, the only weekday that was statistically significant. All other weekdays show no measurable difference from one another.
9. The right length depends on the channel
For emails, longer ones won: 900 to 1,200 characters reached 2.6 percent versus around 1.0 percent for shorter emails. On LinkedIn it was the other way round: messages under 900 characters came in at around 10 percent, longer ones dropped to 5.3 percent. There is no blanket rule for message length.
10. Guessed email addresses are wasted volume
Validated addresses: 2.1 percent reply rate. Constructed addresses, guessed from a pattern: 0.18 percent. A single reply from 543 contacts. Without verification, email outbound is busywork.
What does not work: myths that died
Myth 1: Seniority brings replies
No measurable difference between C-level plus VP (3.2 percent) and manager level and below (3.0 percent). C-level alone was even below average at 2.7 percent. The assumption that you always have to start at the very top does not hold up against the data.
Myth 2: People reply in the morning and evening
Per hour, at least as many replies come in during the day (10 am to 5 pm) as in the morning, and significantly fewer in the evening. The magic morning reply slot does not exist in our data.
Myth 3: Connection requests are accepted within hours
Half of all accepted connection requests took more than 3.6 days. Only 12.6 percent were accepted within 6 hours. If you build your sequence on fast acceptances, you are planning against reality.
Myth 4: You are either good at outbound or bad
The distribution of reply rates across senders is not split in two. Most sit in the middle, a few outliers at the top reach 10 to 15 percent. Outbound is not a talent lottery but a craft with a learning curve.
Myth 5: The AI lead score predicts who will reply
The correlation between our own AI score and the likelihood of a reply was statistically significant but tiny (r equals 0.03). For predicting who replies positively, it was useless. To be honest here: our own scoring does not yet prioritise well enough either.
Two results with a twist
The third sequence step barely brings in replies anymore (0.7 percent per message versus 1.9 to 2.8 percent in steps 1 and 2). But: the replies it does bring are above-average genuine interest. Based on this data, cutting step 3 would be a mistake. It filters out the hard-to-reach and brings in late but good replies.
The same goes for the call to action: asking directly for a meeting generates more replies than offering an analysis. But replies to an analysis offer contain genuine interest four times as often. If you count meetings, ask for the meeting. If you want pipeline quality, offer value.
Methodology
The basis is anonymised, aggregated data from campaigns that ran through SalesXMachina between 1 May and 10 July 2026: exactly 9,925 messages (7,834 emails, 729 LinkedIn direct messages, 1,362 connection requests) to 5,959 contacts at 2,518 companies, 441 incoming replies, 209 of them human. Reply rates were calculated against the respective sending population, not as a distribution across replies. Significance: two-proportion z-test, significance level 5 percent, Wilson confidence intervals, cells that were too small excluded. When screening across many variables we also applied a Bonferroni correction and cross-checked spurious effects from channel confounding within the channels. Out-of-office notices, bounces and system emails were removed from all rates. Correlation is not causation, and individual effects (such as industry) overlap with campaign types.
What this means for your outbound
- Shift volume from email to LinkedIn wherever you can
- Invest in accepted connection requests, they pay back at a factor of 13
- Research the hook instead of sending generic triggers
- Prioritise contacts who are new in their role
- Follow up within 24 hours when someone replies
- Verify every email address before you write to it
- Test Sunday as a sending day
- Write long emails and short LinkedIn messages
- And plan a proper follow-up system for the 40 percent who want to buy later
We built exactly these learnings into SalesXMachina: find signals, research hooks, choose the right channel, follow up, schedule the next touch. Automatically, every week, without the energy dropping off. If you want to see what this looks like for your product, get in touch.
Happy selling, Axel