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Channel

H0H1: про HR-аналитику

@h0h1_hr_analytics

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

2,263subscribers

-2 since we began measuring on 7 August 2026

Risers and fallers across the register · movement among entries of 1,000–3,162.

Register entry

Telegram ID-1001620977284
TypeChannel
Username@h0h1_hr_analytics
CreatedBetween 1 December 2021 and 30 April 2023— estimated from Telegram’s id allocation, not measured. How this range is calculated.
First recorded7 August 2026
Last confirmed live17 August 2026
Measurements held4
Confirmed unchanged1 time, most recently 17 August 2026
On Telegramt.me/h0h1_hr_analytics

Growth

2,2632,2662,264.57 August 2026 — 2,265 subscribers7 August 2026 — 2,266 subscribers14 August 2026 — 2,264 subscribers17 August 2026 — 2,263 subscribers7 August 202617 August 2026
4 measurements spanning 10 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 2,263–2,266 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
17 Aug 2026, 16:172,263-1
14 Aug 2026, 09:492,264-2
7 Aug 2026, 23:382,266+1
7 Aug 2026, 09:462,265first reading

Engagement

14 posts held, back to 16 January 2026the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
14.2%
avg views ÷ 2,263 subscribers
Avg views / post
321
1 post measured
Reaction rate
6.85%
reactions ÷ views · ER floor
Posts in window
1
of 14 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.

What these figures were computed from
WindowRolling 30 days · latest post in window 2 August 2026
Posts held14 (16 January 20262 August 2026)
Views total321
Reactions total22
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 16:44 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.

Reaction mix

216 reactions across 14 posts, in 8 distinct kinds. The most used accounts for 41.7% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
9041.7%
🔥6530.1%
👍4018.5%
👏94.17%
❤‍🔥73.24%
😱20.926%
🤔20.926%
🤯10.463%

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 14 of the 14 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 216reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 14 most recent posts we hold, published 16 January 2026 to 2 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

2 Aug 2026, 17:20 UTC321 views22 reactionsread 7 August 2026

Способы познания Мои читатели знают о моём философско-аналитическом пути, по которому я иду уже несколько лет. Он начался с байесовской статистики, продолжился причинно-следственным выводом и в итоге привёл меня к эпистемологии. 💎 Встречайте комбо: байесовская эпистемология. Сейчас читаю книгу Майкла Тительбаума по этой теме и зашёл поделиться его лекцией. Сказать, что мне это откликается - ничего не сказать. Одн

16🔥4👍2

26 May 2026, 06:32 UTC672 views34 reactionsread 7 August 2026
Photo

Есть, чем поделиться Habr провел очередной конкурс Технотекст и выложил результаты. Мне чертовски приятно: моя статья «Конфаундинг, или как аналитику попасть в ловушку» победила в номинации «Аналитика» среди частных авторов. Для меня это особенно ценно, потому что статья была не про модный инструмент, не про очередной дашборд и не про «как мы всё посчитали и стали молодцами». Она была про менее удобную, но гораздо

🔥227👏5

16 May 2026, 15:48 UTC785 views24 reactionsread 7 August 2026
Photo

Руководство по каузальному мышлению Сходил в отпуск и дочитал ещё одну книжу по одной из любимых мной тем. Книга Квентина Галлеа — это ещё одна попытка рассказать про современный причинно-следственный анализ без сложных формул и кодинга. Такие материалы, на мой взгляд, важны: они помогают популяризировать тему и постепенно формируют сообщество людей, которые мыслят в каузальной парадигме. Что примечательного в это

19👍5

19 Apr 2026, 11:26 UTC799 views18 reactionsread 7 August 2026
Photo

HR-аналитика: как превратить управление персоналом на основе данных и генеративного ИИ в бизнес-актив В книге People Analytics: Using data-driven HR and Gen AI as a business asset Коул Неппер, опираясь на свой опыт и интервью из подкаста Directional Correct, рисует образ будущего HR-аналитики в эпоху ИИ. Но гораздо интереснее, чем хайповое название, — его философия трансформации функции HR-аналитики. Она мне отклик

🔥11👍6😱1

28 Mar 2026, 10:56 UTC≈1,090 views16 reactionsread 7 August 2026
Photo

Исследование рынка аналитиков 2025 Вышло свежее исследование рынка аналитиков от NEWHR. Всем рекомендую ознакомиться. В опросе — 1493 человека из 14 специализаций аналитики Выделяют 3 сегмента: 🟡дата-, продуктовые, BI- и маркетинговые аналитики 🟡системные и бизнес-аналитики 🟡новое - руководители аналитики Основные выводы 🟠Зарплаты: растут, но рынок — нет. У большинства специалистов доход растёт, но рынок в цел

👍94🔥2🤯1

21 Mar 2026, 10:32 UTC838 views13 reactionsread 7 August 2026
Photo

Читаю очередную книжку про People Analytics, расскажу попозже. Пока поделюсь одной картинкой

8🔥4👍1

16 Mar 2026, 06:09 UTC987 views15 reactionsread 7 August 2026
Photo

Я прочитал второе издание Handbook of Regression Modeling in People Analytics Мой интерес к этой книге был вызван прежде всего новыми главами, посвящёнными байесовской статистике и causal inference. Эти две темы — наряду с вопросами эпистемологии — являются тем, чему я как аналитик уделяю наибольшее внимание в последние несколько лет. Для своего канала я уже писал ряд статей, где рассматривал байесовскую статистику

9🔥6

7 Mar 2026, 10:44 UTC≈1,210 views17 reactionsread 7 August 2026
Photo

Влияние ИИ на рынок труда: новый метод измерения и первые данные Немножко хайповых тем для канала. Anthropic — те самые создатели Claude — выпустили исследование про влияние ИИ на рынок труда и предложили методологию, которая должна позволить отслеживать этот эффект в динамике. Ключевые выводы исследования: 🟣Авторы ввели новый показатель риска вытеснения работников искусственным интеллектом — наблюдаемое воздейст

🔥7👍64

7 Feb 2026, 10:18 UTC717 views2 reactionsread 7 August 2026

Заимствовал с канала Душно про дату. Разделяю содержание поста

2

7 Feb 2026, 10:18 UTC≈1,040 views17 reactionsread 7 August 2026
Forwarded from @voskresenskiiconsultingPhoto

# Прогнозная модель vs Причинная модель Работаю с прогнозированием каждый день и регулярно вижу одну и ту же проблему: бизнес просит инструмент для принятия решений, а команда в ответ обучает прогнозную модель. Кажется логичным, но проблема в том, что это разные задачи. Делюсь своим подходом как я ставлю в inDrive экспертизу по casual forecasting Прогнозная модель отвечает на вопрос «Сколько заказов будет в городе,

👍7🔥54😱1

Showing the 12 most recent of 14 posts we hold for @h0h1_hr_analytics. 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.

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

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

“H0H1: про HR-аналитику” (@h0h1_hr_analytics), 2,263 subscribers as measured 17 August 2026. Telegram Register, tgregister.com/channel/h0h1_hr_analytics.

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.