Original data · H2 2026 edition · a production-database study, not a survey
Telegram Cold DM Benchmarks
128,562
delivered Telegram cold DMs analyzed — replying at 13.5%.
Aggregated, anonymized benchmarks from 128,562 delivered Telegram cold DMs sent through Xreacher's Telegram product over its first six months: what reply rates campaigns actually get, and — because every message's text is stored — exactly which copy earns replies. Message length, a question, and the right amount of spintax each move the number by a third or more; personalization by name does not. Recomputed each edition from the full campaign dataset with the methodology published below.
Data window: the 180 days ending July 5, 2026 · Published 2026-07-05 · Free to cite with attribution and a link
The dataset
The dataset
Every figure is computed across all Telegram campaigns sent through Xreacher in the stated window, aggregated and anonymized. One record = one initial send; a send counts as delivered when Telegram accepts it, and delivered messages are the denominator for every reply rate.
128,562
This edition covers 128,562 delivered Telegram cold DMs sent in the 180 days ending July 5, 2026 — the base for every reply-rate figure on this page.
992
The data spans 992 distinct Telegram sending accounts across 45 campaigns and 21 users.
94
Senders used 94 distinct message templates in the window; every delivered DM is tracked to the template it came from.
98.3%
A handful of scaled operations dominate raw volume: the five users running 11+ accounts sent 98.3% of all delivered DMs.
Methodology note: Because a few large senders drive most volume, this page leans on campaign-level medians and cohort splits rather than one pooled average.
Finding 02 · Message copy
The copy that gets replies
Because every DM's rendered text is stored, the copy itself is measurable. Analyzed on a 60,000-message sample of delivered threads. Length and links move the number most; personalization and emoji barely register.
Reply rate by message length (characters)
Reply rate by content feature
50-100 characters
Messages of 50-100 characters replied best at 15.8%, versus 7.9% for long 200-400 character messages — short beats long by roughly 2x.
Methodology note: Length is partly entangled with template (the long band is mostly a few hard-sell templates); the 50-100 sweet spot holds across many templates.
14.0%
DMs containing a question replied at 14.0% versus 12.1% without — a reliable, if modest, +16% relative lift.
3.0%
Messages containing a link replied at just 3.0% versus 13.5% without — a link cut replies by roughly 4.5x.
Methodology note: Directional: only ~200 sampled messages contained a link, but the direction is stark — links likely trip Telegram's spam heuristics and reduce delivery quality.
no measurable lift
Inserting the lead's first name showed no measurable lift: 13.9% when the name was used versus 14.3% when a name was available but left out. Emoji were a wash too (13.7% vs 13.2%).
Methodology note: Correlational, on the sample where a first name was available. A common tactic that did not, on this data, earn more replies.
Finding 03 · Spintax
Spintax has a sweet spot
Spintax (rotating message variations) is standard practice to avoid sending identical copy. Some variation dramatically beats none — but piling on hundreds of renders erodes the gain.
Reply rate by number of spintax variations in the template
11-100 variations
Templates with 11-100 variations replied best at 15.6%; 2-10 variations reached 14.4%, but past 100 variations the rate fell back to 11.4%.
Methodology note: Variation count correlates with specific templates and senders, so read this as a strong association, not a clean dose-response.
3.0%
Templates sent with no spintax variation at all replied at only 3.0% — about a fifth of the rate of well-varied copy, likely because identical repeated messages get throttled or filtered.
Finding 04 · Sending accounts
Older sending accounts reply better
Joining each send to its sending account, account tenure is the clean signal: longer-lived accounts reply at a meaningfully higher rate.
Reply rate by sending-account tenure in the platform
Finding 05 · Read receipts
Read receipts: seen, then replied
Telegram exposes read state on outgoing messages — a signal most channels can't measure. A DM that gets opened replies at six times the rate of one that doesn't, and most opens happen fast.
Delivered → seen → replied
Reply rate by whether the DM was opened
Time from send to open (seen) · 24,425 confirmed opens
About half of opens happen within six hours and ~74% within a day. Read detection is polled ~2-hourly, so buckets are slightly coarse.
Finding 06 · Send timing
Send timing
How send time relates to reply rate, in UTC. Unlike some channels there's no dramatic window — reply rate stays in a narrow 11-16% band all day.
Reply rate by send hour (UTC) · delivered DMs
16:00-18:00 UTC
Reply rate is fairly flat by hour; the gentle high points sit around 16:00-18:00 UTC and 10:00 UTC (~16%), the lows around 07:00-08:00 and 19:00 (~11%).
Methodology note: A weak correlation across a global sender base, not a causal test — treat send-hour as a minor lever at most.
Reply rate by send hour — full table
| UTC | New York (ET) | Berlin (CET) | DMs sent | Reply rate |
|---|---|---|---|---|
| 00:00 | 20:00 | 02:00 | 5,304 | 13.86% |
| 01:00 | 21:00 | 03:00 | 3,814 | 13.90% |
| 02:00 | 22:00 | 04:00 | 3,528 | 15.53% |
| 03:00 | 23:00 | 05:00 | 2,907 | 13.07% |
| 04:00 | 00:00 | 06:00 | 2,630 | 11.71% |
| 05:00 | 01:00 | 07:00 | 1,994 | 13.39% |
| 06:00 | 02:00 | 08:00 | 5,527 | 12.96% |
| 07:00 | 03:00 | 09:00 | 10,474 | 11.52% |
| 08:00 | 04:00 | 10:00 | 8,634 | 11.13%lowest |
| 09:00 | 05:00 | 11:00 | 7,566 | 12.62% |
| 10:00 | 06:00 | 12:00 | 7,154 | 15.68% |
| 11:00 | 07:00 | 13:00 | 6,746 | 13.21% |
| 12:00 | 08:00 | 14:00 | 5,872 | 13.47% |
| 13:00 | 09:00 | 15:00 | 4,794 | 14.66% |
| 14:00 | 10:00 | 16:00 | 4,356 | 13.09% |
| 15:00 | 11:00 | 17:00 | 4,394 | 13.47% |
| 16:00 | 12:00 | 18:00 | 3,999 | 16.33% |
| 17:00 | 13:00 | 19:00 | 4,713 | 15.15% |
| 18:00 | 14:00 | 20:00 | 4,972 | 16.37%highest |
| 19:00 | 15:00 | 21:00 | 5,155 | 11.87% |
| 20:00 | 16:00 | 22:00 | 5,729 | 13.37% |
| 21:00 | 17:00 | 23:00 | 4,175 | 14.85% |
| 22:00 | 18:00 | 00:00 | 6,870 | 13.71% |
| 23:00 | 19:00 | 01:00 | 7,260 | 13.55% |
Delivered DMs, 180 days ending July 5, 2026. Reply rate stays within a narrow 11-16% band across the day, so send-hour is a minor lever. Hours are UTC; a global sender base spans many local timezones.
Finding 08 · Targeting
Who replies
Reply rate by the lead's stored profile. The attributes captured at scrape time are limited, but the ones available show clear patterns.
Reply rate by lead's public @username
14.0%
Leads with a public @username replied at 14.0% versus 9.8% for those without — a 1.4x gap.
Methodology note: Directional, sampled (~124k threads). Username presence is the only lead-profile attribute stored, so richer 'who replies' cuts aren't available this edition.
Finding 09 · Historical trend
Reply rate over time
How the monthly reply rate has moved across the six months of data.
Reply rate by month · delivered threads in parentheses
Summary
Practices associated with higher reply rates
Each guideline restates a finding above, with its qualifier. Correlational findings describe associations in observational data, not causal effects.
Keep the message short. 50-100 character DMs replied best (15.8%); long 200-400 character messages fell to 7.9%.
15.8% vs 7.9%Ask a question. DMs with a question replied at 14.0% versus 12.1% without — a small but reliable lift.
+16%Don't paste a link in the first message. Messages with a link replied at 3.0% versus 13.5% without. Directional, but the drop is steep — links likely hurt delivery.
3.0% vs 13.5%Use spintax, but don't overdo it. No variation replied at 3.0%; 11-100 variations peaked at 15.6%; past 100 the gain faded to 11.4%.
15.6%Warm accounts up. Accounts active 90+ days replied at 13.5% versus 10.4% for accounts under 30 days.
13.5% vs 10.4%Don't over-invest in name personalization. Inserting the lead's first name showed no measurable lift (13.9% vs 14.3%).
no liftBenchmark against the campaign median (12.6%), not a single pooled number. Top-decile campaigns exceeded 25.7%.
>25.7%Keep threads under watch for a week: 9.5% of replies arrived more than seven days after the initial DM.
9.5%Methodology
How these figures are computed
Source
Full production dataset of Xreacher's Telegram product: 128,562 delivered cold DMs, 992 sending accounts, 45 campaigns, 21 users, 180 days ending July 5, 2026. Read-only aggregation over the campaign message store, joined to templates, accounts, and leads. Aggregated and anonymized; no individual customer, account, campaign, template, or scrape source is identifiable.
What counts as delivered / replied
Delivered = a send Telegram accepted. Replied = the tracker matched an inbound message on the thread. Reply rate = replied ÷ delivered; failed sends are excluded from the denominator.
Content & sampling
Message-content and personalization cuts run on a random 60,000-message sample of delivered threads; lead joins on a ~200,000-thread sample. Cell sizes are disclosed inline. The link arm is low-volume and marked directional.
Entanglement
Message length, spintax variation count, and template are correlated (long templates tend to be the hard-sell ones), and volume is dominated by a few large operators. Content and template findings are strong associations, not controlled tests, and are labeled directional where samples are thin.
Timing precision
Reply and read detection are polled roughly every two hours, so time-to-reply and time-to-open are reported in buckets as upper bounds; the sub-hour buckets are noisy.
What is not published
Absolute delivery/failure rates (most failure records are uncategorized), specific template text or scrape-source handles, and any per-customer detail. Follow-ups are excluded this edition — the feature launched recently and appears on under 0.2% of threads. Every figure is recomputed from scratch each edition.
FAQs
Where does this data come from?
From aggregated, anonymized campaign data across all Telegram outreach sent through Xreacher's Telegram product in the stated window — 128,562 delivered cold DMs from 992 sending accounts. Figures are computed with database-level aggregations over the full dataset (large random samples for message-text and lead joins), and no individual customer, account, campaign, template, or scrape source is identifiable.
What counts as a reply?
A thread counts as replied when the contacted lead sends any response after the initial DM. Reply rate is replies divided by delivered messages (sends Telegram accepted); failed sends are excluded from the denominator. Reply detection is polled roughly every two hours, so reply timing is reported in buckets, not to the minute.
Does personalizing with the lead's name help?
Not measurably, in this data. DMs that inserted the lead's first name replied at 13.9% versus 14.3% for messages that had a name available but didn't use it. What moved reply rate far more was message length, whether the message asked a question, whether it contained a link, and how much spintax variation the template used.
Why report a median campaign rate instead of one overall average?
Because volume is concentrated: five users running 11+ accounts sent 98% of all DMs. A single pooled number is dominated by those operations, so we publish the campaign-level median (12.6%) and cohort splits alongside the pooled rate (13.5%).
Can I cite these benchmarks?
Yes. Every stat has an anchor link you can reference directly, and you may quote any figure with attribution to 'Xreacher Telegram Cold DM Benchmarks' and a link to this page. The edition label states the data window so citations stay accurate as the page is refreshed.
How often is this updated?
Each edition is recomputed from the full dataset and the edition label and publication date are updated accordingly. This is an early dataset — the Telegram product is roughly six months old — so smaller cohorts are labeled directional and will firm up as volume grows.
Last updated 2026-07-05 · Cite as “Xreacher Telegram Cold DM Benchmarks (H2 2026)” with a link to this page; every stat above has its own anchor.