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Telegram profile photo for Loyalty Club

Channel

Loyalty Club

@loyalty_clinic

On this record: Growth · Engagement · What this channel posts · Reactions · Posts · Citations · Cite this entry

544subscribers

-1 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of Under 1,000.

Register entry

Telegram ID-1001620534291
TypeChannel
Username@loyalty_clinic
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded8 August 2026
Last confirmed live8 August 2026
Measurements held3
Confirmed unchanged1 time, most recently 8 August 2026
On Telegramt.me/loyalty_clinic

Growth

544545544.57 Aug 2026, 21:55 — 545 subscribers8 Aug 2026, 03:47 — 545 subscribers8 Aug 2026, 21:07 — 544 subscribers7 Aug 2026, 21:558 Aug 2026, 21:07
3 measurements taken within a single day, net -1. Dots are measurements; the straight line between them is drawn to join them, not to claim we know the path taken in between — snapshots are recorded only when a count changes, so gaps mean “no change observed”, never “interpolated”. The vertical axis spans 544–545 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
8 Aug 2026, 21:07544-1
8 Aug 2026, 03:47545no change
7 Aug 2026, 21:55545first reading

Engagement

5 posts held, back to 12 December 2025the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 1 pageof Telegram’s post history, 20 posts per page.

Nothing published in the last 30 days. ERR and ER are rolling 30-day measures, so there is nothing to compute — we hold 5 posts for this entry, the most recent from 7 March 2026. An engagement rate over an empty window would be a number about nothing.

What this channel posts

Video runtime
54s
Average length
27s

Measured directly from 2 videos with a duration reading, out of the posts we hold for this channel — not this channel’s whole posting history, only the sample this register has actually read. An exact reading to the second, taken from the post itself rather than from Telegram’s own rounded chrome, so it carries no mark.

Reaction mix

37 reactions across 5 posts, in 3 distinct kinds. The most used accounts for 43.2% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
custom 54540172937365049391643.2%
custom 54539557855098610701437.8%
custom 5454323181307327886718.9%

Custom emoji. Every row above is a Telegram custom emoji, which the public preview renders as an element carrying only a numeric id — no character, and no image we can reach. The id is printed as-is rather than substituted with a look-alike glyph, because a stand-in would be our invention showing where a measurement should be. The counts beside them areTelegram’s.

No sentiment is inferred, and none should be read in. This table is ordered by count and by nothing else. Emoji do not carry stable meaning across languages or communities — 🙏 is thanks in one channel and mourning in another — so we publish which ones were pressed and how often, and pass no judgement on what an audience meant by them.

Precision. Telegram publishes reaction counts per emoji and short-forms each one — 4.34K, 1.2M — so any single kind at or above 1,000 reaches us at three significant figures, and only counts below 1,000 are exact. The shares above are ratios of those figures and carry the same error. This is also why the total here can differ slightly from a reaction total printed elsewhere on the page: both are sums of the same rounded parts, taken over samples with different edges.

Coverage. Reactions were read on 5 of the 5 sampled posts in this sample. Summed by Telegram’s own count on each post — not by adding up the per-emoji breakdown above — those same posts carry 37reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 5 most recent posts we hold, published 12 December 2025 to 7 March 2026, using the newest reading held for each. Telegram Stars are excluded: they are a payment, not a reaction, and they have their own section.

Recent posts

7 Mar 2026, 15:56 UTC≈1,080 views7 reactionsread 8 August 2026
Forwarded from @vashakoltsovaVideo

Каждый раз, когда я где-то слышу рандомную фоновую музыку без слов, в голове рождаются разные мелодии) Вот вчера в салоне Loyalty, после процедуры, зависла на несколько минут. Чуть забыла, что нахожусь в процедурном кабинете, а не в музыкальном 😁

custom 54540172937365049393custom 54539557855098610702custom 54543231813073278862

20 Feb 2026, 13:39 UTC≈1,120 views10 reactionsread 8 August 2026
Photo

Раньше я никогда не ходила на лазерную эпиляцию и очень переживала: что будет больно, страшно и вообще некомфортно. Но стоило мне попасть в студию Loyalty, как первая мысль была: «Почему я не пришла сюда раньше?» Сервис здесь на высшем уровне — всегда предлагают напиток по собственному меню (даже матча есть 🍵), атмосфера невероятно уютная. Отдельная любовь к мастеру Диане. Очень внимательная, приятная и профессиона

custom 54540172937365049396custom 54539557855098610704

23 Jan 2026, 11:43 UTC≈1,320 views9 reactionsread 8 August 2026
Forwarded from @ekaterinas_blogPhoto

Второй сеанс по RSL-массажу на этой неделе done✔️ Естественно, я чувствую себя намного легче, подслилась водичка, правда, интервал между процедурами сделала маленький, в один день, а надо хотя бы два-три. Плюс этой процедуры еще в том, что она положительно влияет на здоровье сосудов (что для меня тоже актуально) - сосудорасширяющее и сосудотонизирующее воздействие обеспечивает усилие кровотока, микроциркуляции, сниж

custom 54540172937365049394custom 54539557855098610703custom 54543231813073278862

29 Dec 2025, 17:20 UTC≈1,580 views3 reactionsread 8 August 2026
Forwarded from @foryoursouIPhoto

massage time🪽 под конец года закончила свой курс rsl-массажа в Loyalty clinic! все, что могу сказать: это настоящее спасение для кожи и фигуры! теперь хочу повторить курс весной или летом, потому что заметила очень классный результат [с тела уходят все отеки, кожа становится безумно мягкой, разгоняет лимфу, подтягивает фигуру, а еще очень хорошо влияет на здоровье сосудов!] вообщем вау💔🤞🏼 в клинике я была у каждог

custom 54539557855098610703

12 Dec 2025, 09:21 UTC≈1,370 views8 reactionsread 8 August 2026
Forwarded from @ekaterinas_blogPhoto

Четвертая процедура по RSL-массажу done✔️Я прям очень довольна процессом, если бы мне кто-то сказал, что так фигура может меняться без спорта, я бы не поверила) Спорт, конечно, тоже нужен, но и так тело после массажа преображается, даже появляется полоска сбоку на ноге, как после тренировок) Качество тела меняется, думаю даже больше процедур сделаю, чем планировала) И в The Clinic Loyalty всегда приятно возвращаться

custom 54540172937365049393custom 54543231813073278863custom 54539557855098610702

Showing the 5 most recent of 5 posts we hold for @loyalty_clinic. View and reaction counts are the latest single reading for each post, not a live figure, and a recent post is still accumulating both. A view count marked was rounded by Telegram before we ever saw it — t.me prints views in full below 1,000 and to three significant figures above, so ≈1,200,000 means somewhere between 1,150,000 and 1,249,999. Unmarked counts are exact. Text is reproduced from the public post preview and truncated for length.

Citation-graph rank

Citation-graph rank — 721,564 of 1,481,217entries in the measured graph. A weighted position computed from the forward and mention edges below — republished posts weigh more than named mentions — and recomputed periodically, over the whole graph. Published only as this ordinal position, never as a score: a position is a fact, and a score printed beside one channel’s name would read as a verdict this register does not make. The two counts beneath stay separate for the same reason mentions are never summed with forwards anywhere else on this page — a named-by count costs nothing to manufacture. The top 100 by this measure, or how it is computed.

Forward network

Built only from forwarded posts we have actually read, on both sides. Coverage is early and deliberately incomplete: a missing link means we have not read the post that would prove it, never that the relationship does not exist. Counts are distinct forwarded posts observed, so they only ever go up as we read more.

Mentions

Named by 1 registered channel — every channel on the register whose own posts have named this one, by its current username or any other username it currently holds, merged from two separately captured readings of the same fact so a namer caught by only one of them is not missed and a namer both caught is not counted twice. A username this channel has since dropped is not matched — that handle may belong to someone else now, and crediting today’s namer to yesterday’s owner would misattribute it.

Named by

Channels on the register whose posts name this channel's handle.

A mention is a weaker signal than a forward and is counted separately for that reason — naming a channel is not republishing it, and a handle in a post body is easy to place deliberately. The post counts beside each row below are distinct posts in which the handle appeared, from posts we have read on both sides — the “Named by N registered channels” figure above is a different count, of distinct NAMING CHANNELS rather than posts, and is not the sum of the rows under it.

Cite this entry

A live page changes as we take new readings, so a citation should name the measurement it is based on, not just the URL. The line below cites the subscriber count as measured 8 August 2026 — this entry's latest reading, not the date you are reading this.

“Loyalty Club” (@loyalty_clinic), 544 subscribers as measured 8 August 2026. Telegram Register, tgregister.com/channel/loyalty_clinic.

Full measurement history, CC BY 4.0. Every reading this register holds for this entry, not just the latest one, as a dated, downloadable record: CSV · JSON. Free to use with attribution to tgregister.com. Each file carries its own generation timestamp, which is the figure to cite for exactly when the data was retrieved.