Positive Reply Rate vs Reply Rate: 282 Sends of Data

On Friday I sent 37 emails to electrical distributors in Europe. Three came back by Saturday morning. Two were out-of-office notices, one of them from a company closed for the summer. The third was a short, reasonably polite note from a Swedish firm saying they had no use for what I sell.

Three replies from 37 sends is 8.1 percent. Written into a weekly report, that is the best batch I have ever run. In practice nothing happened. No conversation started, no quotation was requested, and no company knows my name this morning that didn't know it on Thursday.

## The two numbers are not the same measurement

Reply rate counts anything that arrives in the inbox. Out-of-office notices count. "Remove me from this list" counts. A rejection counts.

Positive reply rate counts a human being who wants to keep talking.

Most published benchmarks report the first one, and there is a boring reason for that: sending software can count total replies without anyone reading them, while sorting positive from negative needs a person or a classifier. So the number that gets published is the number that is cheap to produce, and it has drifted into being treated as the number that matters.

I spent about six weeks comparing my results against it.

## What 282 sends produced

The first round finished last Friday. Every name on the list has now been contacted once.

- 282 emails total, 156 of them in the final week (25, 25, 40, 29, 37 across Monday to Friday)
- 4 positive replies from decision-makers
- 1 explicit rejection, from a Hungarian distributor who only handles explosion-proof equipment and central-battery emergency lighting
- Several automated out-of-office notices

Four positive replies from 282 sends is 1.4 percent, and I read that as bad news for most of the six weeks it took to send them. Against the 4 to 5 percent that turns up in every benchmark article, 1.4 looks like failure.

Then I went and read what those benchmarks actually measure. [Sales.co's 2026 Cold Email Benchmark Report](https://sales.co/research/cold-email-statistics) analysed over two million cold emails sent between 2024 and early 2026 and found an average reply rate of 2.09 percent, of which 14.1 percent were positive, 29.9 percent negative, and 45.1 percent automated. The same report puts the effective interested-reply rate across all contacts at roughly 0.64 percent. My 1.4 percent is a little over double that.

I should be careful with the comparison, because it is not clean. My list is 282 companies in one narrow industrial category, contacted from China into Africa, the Middle East, Europe and Southeast Asia. Two million contacts across every sector is a different animal, and 282 is a small enough sample that one more reply moves my rate by a third of a percentage point. But the direction is clear enough to act on. I was not failing. I was measuring against a number that describes a different thing.

## Almost half of all replies are machines

That 45.1 percent figure also killed a theory I had written down on Saturday morning.

I had assumed European buyers auto-reply more than my other markets because cold email is better established there and people have built defences. It felt like a real observation. But if nearly half of all replies everywhere are automated, then two out-of-office notices out of three on a Friday in late July says nothing about European market maturity. It says people take holidays in July. One batch of 37 is not evidence, and I was two hours from publishing it as though it were.

## What the weekly report tracks now

Instead of one number, four:

- Emails sent
- Human replies, with automated responses stripped out
- Positive replies from a named decision-maker
- Requests for materials that turn into an actual quotation

The fourth is the only one that pays for anything, and the gap between the second and the third is where most of the self-deception lives. If you would rather not sort this by hand every week, reply classification is a standard feature in the sending platforms.

The four positive replies break down less impressively than the headline suggests. One was a business director in Uganda stating plainly that he wanted to work together. One was a CEO in Bangladesh asking for a catalogue the same day. One was a chairman in Egypt writing a neutral line to say he was waiting to hear back. One was a director in Vietnam asking for our website and product documentation.

Only the first arrived looking like a qualified lead. The other three arrived looking like admin.

## Which is why I stopped scoring replies

Earlier in the week I gave an AI agent a sourcing job and asked for 300 companies. It found 300 companies and more than 170 email addresses, then applied its own three-part scoring logic to the results and recommended that I contact seven of them.

The research budget was already spent by then. Tokens, verification, my time reading the output. The scoring step ran after the money was gone and threw away 98 percent of what the money bought. An earlier run of the same pipeline finished at [11.4 percent end to end, 500 requested companies down to 57 contactable ones](/blog/ai-outbound-pipeline-yield), and all of that attrition happened before contact, which is where attrition is cheap.

Worse, on the evidence above, a rubric like that would have discarded three of my four wins. A same-day catalogue request scores low on any interest rubric I have seen.

So the filters all moved forward. Geography, product-line overlap, whether the company is an authorised distributor, sanctions exposure, whether they sell anything adjacent to what we manufacture: all of that is checked before a single research call, and I wrote up [how that screen works and what it deletes](/blog/lead-qualification-emerging-markets) separately. After the research is paid for, the only question left is whether there is a named decision-maker with a verified address. After a human replies, there is no scoring at all.

At four positive replies per 282 sends you cannot afford to triage. Losing one is losing a quarter of the quarter.

## The cost of doing it this way

Following every faint signal to the end takes real hours. Writing the custom PDF, chasing the technical department for confirmation, building the quotation. My ceiling is about 40 emails a day across five rotating mailboxes, roughly eight per mailbox, sent in the morning and spaced out. Past that the follow-up work starts eating the quoting work, and the quoting work is the part that earns money.

Which means the permissive policy is affordable only because positive replies are rare. If my rate tripled I would need a second person, not a better rubric.

The Hungarian rejection, incidentally, was the most useful message of the week. It named a reason. Their product line does not overlap with mine, which tells me my pre-send screen leaked, and I can fix a screen. Silence tells me nothing at all.

## FAQ

**What is the difference between reply rate and positive reply rate?**
Reply rate counts every response, including out-of-office notices, unsubscribe requests and rejections. Positive reply rate counts only responses expressing genuine interest. In the Sales.co dataset, 45.1 percent of all replies were automated and 29.9 percent were negative, so the two numbers can differ by a factor of seven.

**What is a good positive reply rate for cold email?**
Roughly 0.64 percent of contacted people send a positive reply on industry averages. My own rate across 282 sends into industrial distribution is 1.4 percent. Any vendor benchmark quoting 4 to 5 percent is almost certainly quoting total reply rate.

**Do out-of-office replies mean anything?**
They confirm the address is live and monitored, which is worth something for list hygiene. They are not pipeline, and leaving them in the reply-rate calculation inflates it substantially.

**Should you score or grade cold email replies?**
Score companies before you spend research budget on them. Do not score the replies. At normal positive-reply rates you receive too few to discard any.