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Channel

Chief Innovation Channel

@chiefinnovationchannel

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

2,477subscribers

+3 since we began measuring on 6 August 2026

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

Register entry

Telegram ID-1001403619105
TypeChannel
Username@chiefinnovationchannel
Created8 December 2020measured — cross-checked against a third-party dataset (ext.tg_channel)
First recorded6 August 2026
Last confirmed live15 August 2026
Measurements held5
Confirmed unchanged1 time, most recently 15 August 2026
On Telegramt.me/chiefinnovationchannel

Growth

2,4732,4772,4756 August 2026 — 2,474 subscribers6 August 2026 — 2,474 subscribers9 August 2026 — 2,473 subscribers12 August 2026 — 2,476 subscribers15 August 2026 — 2,477 subscribers6 August 202615 August 2026
5 measurements spanning 10 days, net +3. 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 2,472–2,478 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
15 Aug 2026, 15:272,477+1
12 Aug 2026, 12:232,476+3
9 Aug 2026, 21:212,473-1
6 Aug 2026, 05:342,474no change
6 Aug 2026, 03:082,474first reading

Engagement

16 posts held, back to 8 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 2 pagesof Telegram’s post history, 20 posts per page.

ERR · 30 days
21.3%
avg views ÷ 2,477 subscribers
Avg views / post
528
9 posts measured
Reaction rate
0.99%
reactions ÷ views · ER floor
Posts in window
9
of 16 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 8 of 9 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 5 August 2026
Posts held16 (8 July 20265 August 2026)
Views total4,752
Reactions total45
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken7 Aug 2026, 16:28 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
12s
Average length
12s

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

71 reactions across 15 posts, in 8 distinct kinds. The most used accounts for 33.8% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
🔥2433.8%
👍1723.9%
1521.1%
🏆68.45%
🤩45.63%
22.82%
👏22.82%
🦄11.41%

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 15 of the 16 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 71reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 16 most recent posts we hold, published 8 July 2026 to 5 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

5 Aug 2026, 06:01 UTC138 views3 reactionsread 7 August 2026
Photo

▶️Как использовать ИИ в работе с продуктами? Эту тему мы рассматривали на конференции для сотрудников «Ростелекома» «Практическое применение инструментов ИИ в работе с продуктами». Наша команда помогает партнерам не только с запуском программ по развитию продуктовой функции, команд и лидеров, но и в организации внутренних событий. В этот раз мы участвовали в разработке программы мероприятия. ⏩Разбирались вместе с

👏2🔥1

3 Aug 2026, 06:02 UTC206 viewsread 7 August 2026
Photo

Почему продуктовые инициативы не достигают цели? Что мешает развитию продуктовой функции в компаниях и, соответственно, отражается на бизнес-результате? Как связаны продуктовые метрики с бизнес-целями и кто принимает решения? Мы ищем ответы на эти вопросы в нашем ежегодном исследовании продуктового управления в России и странах СНГ. ▶️Наша цель — не только изучить, как устроено продуктовое управление в России и ст

29 Jul 2026, 06:02 UTC251 views2 reactionsread 7 August 2026

❗️ Зачем проводить диагностику продуктовых компетенций: 3 неочевидные причины Крупные компании стали чаще обращаться к нам с запросом оценить компетенции продуктовых команд, которые используют инструменты ИИ. И это не дань трендам. Ирина Бурлова, руководитель направления оценки компетенций Центра корпоративных инноваций и продуктового развития «Акселератора ФРИИ», назвала 3 причины, по которым необходимо проводить

🔥2

27 Jul 2026, 06:00 UTC≈1,020 views3 reactionsread 7 August 2026
Photo

💬 Финтех-стартапы, вам к нам! «Акселератор ФРИИ» и Акселератор ВТБ запускают второе исследование «ДНК Финтеха 2026». Два года назад мы уже анализировали отечественный финтех-рынок — его структуру, ключевые тренды и ниши для инвестиций. ➡️ Скачать исследование «ДНК Финтеха 2024» можно здесь. С 2024 года российский финтех сделал шаг вперед. Мы хотим зафиксировать эту новую реальность. Что будем исследовать: ✅Как и

🤩3

23 Jul 2026, 15:22 UTC366 views16 reactionsread 7 August 2026
Photo

🎉 Мы завершили третий поток курса «Продуктовый подход в HR» Это были насыщенные два дня, которые, надеемся, помогли нашим участникам по-новому посмотреть на функцию HR. ▶️Мы разбирали кейсы, делились фреймворками и опытом, учились работать в продуктовой логике, чтобы быстро проверять гипотезы по HR-продуктам и запускать только те инициативы, которые позволят решить задачи сотрудников, внутренних клиентов и компании

7🔥6👍2🦄1

22 Jul 2026, 10:06 UTC354 views3 reactionsread 7 August 2026
Photo

▶️Сник-пик с первого дня HR-курса Разбираемся, что такое продуктовый подход в HR и как с помощью продуктовой логики эффективно решать HR-задачи, какие инструменты могут пригодиться HR-функции и почему большинство запусков ИИ не приносят результата. Скоро вернемся с инсайтами! Расскажем, как не делать то, что не нужно, и экономить ресурсы с помощью продуктового подхода.

2🔥1

22 Jul 2026, 06:58 UTC338 views5 reactionsread 7 August 2026
Video message

Video message, posted without a caption

🏆4👍1

22 Jul 2026, 06:58 UTC319 views2 reactionsread 7 August 2026

⚡️Следующие два дня будут жаркими! Сегодня стартуем курс «Продуктовый подход в HR». Не переключайтесь!

1🤩1

20 Jul 2026, 11:28 UTC≈1,760 views11 reactionsread 7 August 2026
Photo

↗️ В какие стартапы инвестируют корпорации Обсудили этот вопрос на митапе ФРИИ и Московского венчурного фонда. Представители корпоративного венчура рассказали, какие стартапы берут на борт и сколько готовы платить за перспективный проект. Спойлер: вилка большая — от 50 млн до десятков млрд рублей, а стадия развития стартапа не имеет значения. ↗️ На что обращают внимание корпорации — Нам, как стратегу, интересен ст

👍53🔥3

16 Jul 2026, 16:16 UTC366 views5 reactionsread 7 August 2026
Video

👀 Сник-пик с митапа Московского венчурного фонда и ФРИИ Сегодня в кластере «Ломоносов» обсуждаем, в какие стартапы готовы инвестировать корпорации и венчурные фонды. Скоро вернемся с инсайтами! #WeKnowHowToInnovate Связаться | Продукты

👍5

16 Jul 2026, 14:30 UTC358 views2 reactionsread 7 August 2026
Photo

До начала митапа Московского венчурного фонда и ФРИИ осталось всего полчаса. ➡️ Трансляцию можно посмотреть по ссылке.

2

15 Jul 2026, 06:56 UTC387 views5 reactionsread 7 August 2026

🎯 Топ-3 вида исследований для хорошей стратегии Стратегия помогает бизнесу оставаться востребованным, управлять рисками и использовать возможности для роста. За годы работы с корпорациями Кирилл Соснин, руководитель направления исследований и консалтинга Центра корпоративных инноваций и продуктового развития «Акселератора ФРИИ», прочитал не один десяток стратегий и понял, что хорошие объединяет одна вещь: они основ

🏆2👍2🔥1

Showing the 12 most recent of 16 posts we hold for @chiefinnovationchannel. 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 — 8,859 of 1,481,502entries 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 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 15 August 2026 — this entry's latest reading, not the date you are reading this.

“Chief Innovation Channel” (@chiefinnovationchannel), 2,477 subscribers as measured 15 August 2026. Telegram Register, tgregister.com/channel/chiefinnovationchannel.

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.