Quick answer: There is no honest universal number for a good LinkedIn reply rate, because it depends on who you contact, what you offer, and how you count. A better question is whether your rate is improving against your own baseline. Define the rate clearly, measure it over a steady period, and change one thing at a time, starting with targeting.
Why a single benchmark is misleading
You will see headline figures online claiming what a good rate looks like. Be careful with them. They usually mix very different situations:
- Different audiences, from friendly niches to crowded executive lists.
- Different definitions, such as replies per connection request versus replies per message sent.
- Different offers, from a free resource to a paid service.
- Different data quality, with some counting every automated reply and others only real ones.
A figure that is fine for one team can be a red flag for another. So we will not quote one here. What you can do is build a benchmark from your own data, which is more useful than any average.
First, define what you are counting
Before you measure, decide exactly what counts as a reply and what the denominator is. Pick definitions and stick with them.
| Term | Common definition | Watch out for |
|---|---|---|
| Acceptance rate | Accepted connection requests divided by requests sent | Count after enough time has passed |
| Reply rate (of messages) | People who replied divided by people you messaged | Decide whether to include one-word or negative replies |
| Reply rate (of connections) | People who replied divided by people who accepted | Different from the above; label which one you use |
| Positive reply rate | Replies showing interest divided by people contacted | Needs a consistent rule for "positive" |
| Meeting rate | Meetings booked divided by people contacted | The number that matters most to the business |
Write the definition next to the number every time you report it. If your team uses different definitions, you will argue about a number that means different things to different people. For more on the full set of numbers, see the LinkedIn outreach metrics that matter and the acceptance rate glossary entry.
Build your own baseline
A baseline is just your starting point, measured with care.
- Pick one audience. Mixing very different segments hides the picture.
- Pick one sequence. Keep the messages and timing steady during the measurement.
- Send enough to learn something. Tiny batches swing wildly. If you only have a few dozen contacts, expect noisy results and be cautious about conclusions.
- Wait for responses. Many replies come days after the message. Measure after a fixed window, for example two weeks after the last message in the sequence, and use the same window every time.
- Record it. Date, audience, sequence version, contacts reached, replies, positive replies, meetings.
- Repeat across several batches. One batch is a data point. Three or four give you a range.
Your baseline is the range you see from steady, comparable batches. A "good" rate for you is one that beats that range without hurting the quality of the conversations.
Read the result in context
A single rate rarely tells you what is wrong. Look at the chain:
- Low acceptance, low replies. Targeting or profile problem. People do not recognize a reason to connect.
- Good acceptance, low replies. The first message is weak, or the offer does not match the audience.
- Good replies, few positive. You may be reaching people who are curious but not buyers. Check your targeting.
- Good positive replies, few meetings. The problem is in reply handling and scheduling. See how to book more meetings from LinkedIn replies.
- Strong everywhere on a small list. Be cautious. A small sample can flatter you.
What to change first
Change things in this order, because earlier items usually matter more:
- Who you are contacting. Tighten the list using an ideal customer profile. See how to define yours.
- What you are offering. Make the ask small and clear, such as a short question or a useful resource, not a demo request.
- The first message. Make it specific, short, and about them. See how to write cold messages that get replies.
- The follow-ups. Add or improve the second and third touches. See follow-up message templates.
- Timing and cadence. Check the best time to send messages and the gaps between steps.
- Sequence length. Add or remove a step only after the above are in place.
Change one item at a time and give it enough contacts to show a difference. If two batches look different but both are small, treat the gap as inconclusive.
Common traps
- Counting automated or irrelevant replies. Out-of-office messages and "not interested" count as replies only if you decided they would.
- Comparing across segments. A rate on one audience tells you little about another.
- Chasing the rate at the expense of fit. Easy replies from the wrong people feel good and lead nowhere.
- Judging too early. Measuring before responses arrive undercounts.
- Changing everything at once. You will never know which change helped.
- Ignoring volume safety. Pushing volume to chase more replies can raise account risk. See how to automate outreach without getting restricted.
A simple tracking sheet
Keep a table like this, one row per batch:
| Batch | Audience | Sequence version | Contacts | Accepted | Replied | Positive | Meetings |
|---|---|---|---|---|---|---|---|
| 1 | |||||||
| 2 |
After a few rows, compute each rate and look at the spread. The outreach ROI calculator can help you turn those rates into pipeline estimates, using your own figures.
Where FollowArc fits
Sophia ranks incoming replies by intent so real interest is easy to separate from noise, which makes it simpler to judge positive replies consistently. Setting your own definitions and baseline is still up to you. You can try FollowArc free for 7 days or read the AI SDR use case.