DM setter benchmarks 2026: data from Data coming Instagram and Facebook setter teams

Say a setter on your team replies to new leads in 40 minutes. That might make them the fastest person you have, or the slowest setter in your niche. Without outside numbers there's no way to know which.

Most setter team owners only have their own team to compare against. This report gives you a second reference point, built from real conversations, so you can see where your team sits and what to fix first.

Headline benchmarks at a glance

How did we measure DM setter performance?

We pulled 30 days of contacts and full conversations from Data coming Ellipsend accounts, covering Data coming conversations from Data coming to Data coming.

An account made the sample only if its setters were actively replying to leads during the window. The accounts span mixed niches, including Data coming.

Every account counts equally. We worked out each account's result first, then took the median across accounts, so one very large team can't drag the numbers toward its own habits.

Here's exactly what each term means in this report.

Automated messages never count as human replies. The speed-to-lead clock ignores the automation entirely and starts when the lead answers it. Percentages of leads use all leads as the base, including leads who never got a human reply. The median speed to lead covers only leads who did get one.

Times of day use each account's local time zone, so a lead at 9pm means 9pm where that team works.

How we keep customers anonymous

No account, brand or person is named or identifiable anywhere in this report. We only publish numbers aggregated across the whole sample. We don't publish a niche breakdown or any other cut small enough that someone could work out which account it came from. If a cut has too few accounts or messages to mean much, we say so instead of printing a number.

What this report doesn't cover

Booking rate and revenue aren't in this edition. Offer prices and booking setups vary too much between accounts to compare fairly yet. Every number here is about setter activity and lead behavior inside the DMs.

How fast do DM setters reply to new leads?

The median speed to lead across the sample was Data coming. Data coming of leads got a human reply within 30 minutes.

A median can hide a long tail, so look at the bottom rows too. Those are leads who raised their hand and then waited hours for a person, or never heard from one at all.

Part of speed to lead is coverage. A setter can't reply in five minutes to a lead who arrives while nobody is in the inbox.

When do Instagram DM leads arrive?

New leads peaked at Data coming, and Data coming was the busiest day.

What is the after-hours gap?

The after-hours gap is the share of leads that arrived during an hour when nobody on the team replied to any lead within 30 minutes. Across the sample it was Data coming.

Those leads may still get an answer later, but they wait for it, and the speed-to-lead table shows how long that wait runs. Compare your team's shift hours against the arrival table. The hours where leads show up and nobody is on are where your speed to lead gets made or broken.

How many follow-ups does it take to get a reply in the DMs?

A follow-up here is a setter message sent to a quiet lead, someone who hasn't replied for 24 hours.

Follow-up coverage across the sample was Data coming. That's the share of quiet leads who got at least one follow-up.

Read the two numbers together. Coverage tells you whether quiet leads get chased at all. Reply rate tells you whether chasing them is worth it. A lead who goes quiet hasn't necessarily said no, and the 2nd and 3rd rows show how often a later try still gets an answer.

Do voice notes get more replies than text in DMs?

Data coming messages got the highest reply rate in the sample.

Swipe the table to see every column

One caution before you switch every message to one format. Setters choose when to send a voice note or a video, and they may save them for warmer leads. If they do, some of the gap comes from who received the message. Test it on your own team before you rewrite your scripts around it.

What should you do with these numbers?

Start by pulling the same numbers for your own team over your last 30 days, using the definitions above. Then compare line by line.

Benchmarks tell you whether your team's habits are normal. They don't tell you whether those habits make money. For that, use dollars per DM conversation (dollars per DM), the revenue (total sale value, including payment plans) from a setter's leads divided by the number of leads that setter messaged.

There's no outside benchmark for dollars per DM in this report, because a $500 offer and a $10,000 offer produce completely different numbers. Measure it against your own golden standard instead, meaning your team's normal dollars per DM over a typical stretch like the last 90 days. Say yours is $30. If a setter drops to $22 while their speed to lead and follow-up coverage fell behind the benchmarks above, you know where to coach. If their numbers held steady, look at your content or your sales calls first.

The fastest team isn't automatically the best team. Speed and follow-ups are the inputs. Dollars per DM is the scoreboard.

How often are these benchmarks updated?

We plan to publish a new edition every quarter, each built from a fresh 30-day window. Definitions stay the same between editions so you can compare one quarter to the next. If we change a definition or how the sample is chosen, the new edition will say so at the top. The edition date and last-updated date sit under the title.

How to track these numbers in Ellipsend

Every number in this report comes from conversations run through Ellipsend, a DM sales platform for setter teams. Speed to lead and follow-ups come from the same conversations, so you can track them for your own team. Ellipsend can also show each setter's dollars per DM next to their response rate, so you can hold your team against your golden standard without keeping a spreadsheet.

Once tracking is set up, leads can be tied to the post, story or ad they came from. So when a setter's number moves, you can check whether it was the setter or the content.

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