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Channel

Павел Лисовский. Аптечный и Фармбизнес от первого лица.

@LisovskiyP

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

4,729subscribers

-2 since we began measuring on 6 August 2026

Risers and fallers across the register · movement among entries of 3,162–10,000.

Register entry

Telegram ID-1001399097570
TypeChannel
Username@LisovskiyP
CreatedBetween 1 April 2018 and 31 July 2021— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded6 August 2026
Last confirmed live13 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 13 August 2026
On Telegramt.me/LisovskiyP

Growth

4,7254,7314,7286 August 2026 — 4,731 subscribers7 August 2026 — 4,730 subscribers10 August 2026 — 4,725 subscribers13 August 2026 — 4,729 subscribers4,7296 August 202613 August 2026
4 measurements spanning 7 days, net -2. 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 4,724–4,732 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
13 Aug 2026, 06:384,729+4
10 Aug 2026, 06:444,725-5
7 Aug 2026, 00:364,730-1
6 Aug 2026, 05:324,731first reading

Engagement

19 posts held, back to 12 July 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 7 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
23.5%
avg views ÷ 4,729 subscribers
Avg views / post
1,110
17 posts measured
Reaction rate
1.76%
reactions ÷ views · ER floor
Posts in window
17
of 19 held

ERR is average views per post over the last 30 days divided by subscribers, the definition TGStat uses, so this figure is comparable with the one you will see elsewhere. It falls structurally as a channel grows: a high ERR on a small channel and a low one on a large channel describe reach mathematics, not quality. We publish the figure and the sample it came from and pass no verdict on it.

ER is defined industry-wide as (forwards + reactions + comments) ÷ views— note the denominator is views, not subscribers. Telegram’s public web preview carries views and reactions but not forward or comment counts, so the reaction rate above is the reactions term only and is therefore a floor: the true ER for this channel is higher by an amount we have not measured and will not estimate. It is computed over the 15 of 17 measured posts that carry a reaction reading, and over those same posts' views.

What these figures were computed from
WindowRolling 30 days · latest post in window 11 August 2026
Posts held19 (12 July 202611 August 2026)
Views total18,875
Reactions total300
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken12 Aug 2026, 05:26 UTC

Views are a single reading per post, taken at the time above. A post published in the last day or two is still accumulating views, which pulls the 30-day average down slightly. That is a property of the standard definition rather than a fault in it, so we keep the definition rather than “correcting” the number into something nobody can reproduce.

Precision. Telegram publishes view counts on its public widget in short form — 8.12K, 3.7M — so any reading at or above 1,000 reaches us rounded to three significant figures, and only counts below 1,000 are exact. Averages and rates derived from them are shown to the same precision rather than to the unit: a figure like 3,701,250 would assert digits nobody measured.

Reaction counts are published per emoji and rounded the same way, so a total below 1,000 is exact and a larger one is a sum that may carry a rounded component from each emoji above 1,000. Because it is a sum, it does not look rounded — read a large reaction total as three significant figures per contributing emoji rather than as the figure it prints.

What this channel posts

Video runtime
59s
Average length
59s

Measured directly from 1 video 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

374 reactions across 17 posts, in 11 distinct kinds. The most used accounts for 46.3% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥17346.3%
8723.3%
👍6717.9%
💯133.48%
😢102.67%
😁82.14%
🏆51.34%
30.802%
💊30.802%
😱30.802%
🤔20.535%

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 17 of the 19 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 374reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 19 most recent posts we hold, published 12 July 2026 to 11 August 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

11 Aug 2026, 10:52 UTC392 views13 reactionsread 12 August 2026
Forwarded from @LisovskiySystem

Аптека зарабатывает не на вас. Она зарабатывает на производителе. Большое интервью. Да, коллеги, Вы уже видели, что мы записали большое интервью об особенностях коммерческого фармацевтического рынка. Получилось очень содержательно и интересно... признаюсь, что сам с интересом переслушал нашу беседу. А еще в видео много ссылок на полезные книги и интересные материалы. Таймкоды: 08:34 — Консалтинг: кто реально экспе

🔥76

10 Aug 2026, 09:38 UTC589 views20 reactionsread 12 August 2026
Forwarded from @digital_in_pharmaPhoto

⚡️От реагента до рекламы. Выпуск №5 В гостях Павел Лисовский, ведущий эксперт-экономист фармрынка, консультант по увеличению прибыльности аптечных сетей и фармпроизводителей, кандидат экономических наук. Поговорили о пути: как из мечты о медицине получилась экономика, почему в фармвузе органической химии оказалось меньше, чем хотелось, и как устроена его модель работы с клиентами — оплата от прироста, а не фикс. Од

14🔥3👍2🏆1

7 Aug 2026, 11:18 UTC830 viewsread 12 August 2026
Forwarded from @LisovskiySystem

Posted without readable text

6 Aug 2026, 11:58 UTC≈1,020 views12 reactionsread 12 August 2026

Posted without readable text

5🔥5👍1🤔1

3 Aug 2026, 09:18 UTC≈1,130 views7 reactionsread 12 August 2026
Forwarded from @LisovskiySystem

Цена фармацевта - важные нюансы. Очень приятно, уважаемые коллеги, читать ваши комментарии, особенно когда они предвосхищают и развивают идею, которую я хотел донести. В заметке "Цена фармацевта: стоимость обучения" я хотел донести идею о том, что стоимость обучения фармацевта в первую очередь зависит от товарооборота, который "через него" проходит. Все остальные параметры вторичны. В следующих постах, я хотел нап

👍61

31 Jul 2026, 10:06 UTC≈1,090 views98 reactionsread 12 August 2026
Forwarded from @digital_in_pharmaVideo

Форматы аптек, к которым вы привыкли, доживают последние годы Так считает Павел Лисовский и в новом выпуске «От реагента до рекламы» объясняет, почему. Пятнадцать лет рынок рос не за счет покупателя. Сначала платили дистрибьюторам. Потом сети сказали «мы сами» и ставки поехали с 3% куда-то далеко вверх. Потом пришли СТМ и дали взрывную доходность. Каждая технология работала. Пока не упиралась в потолок. Сейчас по

🔥894🏆31👍1

30 Jul 2026, 10:13 UTC≈1,420 views8 reactionsread 12 August 2026

Цена фармацевта: сколько стоит провести обучение? На рынке есть понимание "средней" стоимости обучения одного фармацевта в сетях разного уровня. Я сознательно не называю здесь цифры. Вы знаете, как я отношусь к любым "средним значениям" по больнице рынку Да, фармпроизводителям важно рассчитать оптимальную стоимость обучения одного фармацевта. Чтобы планировать бюджет и на переговорах с сетями "не переплачивать" за

💯4🔥4

30 Jul 2026, 09:35 UTC≈1,030 viewsread 12 August 2026
Forwarded from @LisovskiySystem

Posted without readable text

29 Jul 2026, 16:39 UTC≈1,250 views8 reactionsread 12 August 2026
Photo

Выкладка и ценообразование Посмотрите на ценовую линейку на витрине. Это продукция одного бренда. На одинаковые по размеру баночки (покупатель визуально их оценивает, потом смотрит состав, количество капсул и т.д.) разброс цен от 1.050 до 1.850 рублей. Есть интересный ценовой ход: для одного бренда сделать несколько ценовых ступенек. В нашем случае это могут быть, например: ⏹️1.050 руб. ⏹️1.450 руб. ⏹️1.750 руб. ⏹

6👍1🔥1

29 Jul 2026, 11:35 UTC≈1,100 views11 reactionsread 12 August 2026

Posted without readable text

5🔥3👍2😁1

28 Jul 2026, 13:27 UTC996 views11 reactionsread 12 August 2026

Проблемы KPI и мотивации сотрудников "На любое KPI найдется хитроумный сотрудник." "Нам не нужен результат, нам нужно выполнить KPI..." Народно-менеджерские мудрости. Предлагаю для решения живой кейс, ну, или задачку как хотите. Дано: В аптечной сети необходимо сократить уровень неликвидов в аптеках. Для этого приняты решения финансово мотивировать сотрудников: ⏹️на сокращение неликвидных товаров (продажа в аптеке

👍91😁1

27 Jul 2026, 18:11 UTC948 views22 reactionsread 12 August 2026
Forwarded from @LisovskiySystemPhoto

Интересно, подробно, но справедливо! Коллеги из @digital_in_pharma и @slavaNSAID пригласили на большой подкаст "От реагента до рекламы". Получилась большая очень интересная и вдумчивая беседа. Вот только некоторые темы. ▪Будущее коммерческого фармрынка. ▪Слабые места зарубежных и отечественных фармпроизводителей, они отличаются. ▪Почему работу многих аптечных сетей можно описать девизом: Жадность. Глупость. Лень.

🔥19😱21

Showing the 12 most recent of 19 posts we hold for @LisovskiyP. 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 — 171,964 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

Republished by

Channels on the register that have forwarded this channel's posts into their own feed.

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 4 registered channels — 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.

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 13 August 2026 — this entry's latest reading, not the date you are reading this.

“Павел Лисовский. Аптечный и Фармбизнес от первого лица.” (@LisovskiyP), 4,729 subscribers as measured 13 August 2026. Telegram Register, tgregister.com/channel/LisovskiyP.

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.