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Channel

可欣业务站

@kexinyewu

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

7,562subscribers

+1,537 since we began measuring on 26 August 2026

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

Register entry

Telegram ID-1004364850818
TypeChannel
Username@kexinyewu
Description可欣导航官网:https://kexindaohang.com 可欣TG:https://t.me/kexinone 机器人:@kexinone_bot 业务群:@kexinone1 项目群:@kexinone2 渠道群:@kexinone3 供需群:@kexinone4 兼职群:@kexinone5 业务站:@kexinyewu 项目站:@kexinxiangmu 渠道站:@kexinqudao 供需站:@kexingongxu 兼职站:@kexinjianzhi 公告站:@kexingonggao
Created10 August 2026 — measured — dated from the channel’s first post
First recorded26 August 2026
Last confirmed live2 November 2026
Measurements held17
Confirmed unchanged1 time, most recently 2 November 2026
On Telegramt.me/kexinyewu

Topic

Software piracy — a classification, not a measurement. An on-box language model (Qwen3.6-35B-A3B-FP8, prompt version 1) read this channel’s own recent posts on 12 September 2026 and assigned it the closest of 31 fixed categories, at 48% confidence. This is a model’s judgement about what the channel is likely to be about, not a fact this register measured the way a subscriber count or a view count is measured — it can be revised on a later pass, and it carries no weight anywhere else on this page. How this classification works, and the other topics.

Growth

5,6089,0747,34126 August 2026 — 6,025 subscribers27 August 2026 — 6,027 subscribers30 August 2026 — 6,026 subscribers3 September 2026 — 6,034 subscribers9 September 2026 — 6,048 subscribers12 September 2026 — 8,383 subscribers15 September 2026 — 8,008 subscribers29 September 2026 — 5,608 subscribers6 October 2026 — 7,642 subscribers12 October 2026 — 7,015 subscribers15 October 2026 — 6,504 subscribers18 October 2026 — 9,074 subscribers21 October 2026 — 8,705 subscribers23 October 2026 — 8,394 subscribers27 October 2026 — 8,093 subscribers30 October 2026 — 7,833 subscribers2 November 2026 — 7,562 subscribers7,56226 August 20262 November 2026
17 measurements spanning 68 days, net +1,537. 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 5,088–9,594 and does not start at zero.
Measurement log — every subscribers count we have recorded
Measured (UTC)SubscribersChange
2 Nov 2026, 17:087,562-271
30 Oct 2026, 08:467,833-260
27 Oct 2026, 00:168,093-301
23 Oct 2026, 23:288,394-311
21 Oct 2026, 08:018,705-369
18 Oct 2026, 00:449,074+2,570
15 Oct 2026, 10:566,504-511
12 Oct 2026, 15:187,015-627
6 Oct 2026, 09:367,642+2,034
29 Sept 2026, 11:155,608-2,400
15 Sept 2026, 22:018,008-375
12 Sept 2026, 15:558,383+2,335
9 Sept 2026, 00:196,048+14
3 Sept 2026, 10:586,034+8
30 Aug 2026, 19:256,026-1
27 Aug 2026, 16:466,027+2
26 Aug 2026, 18:456,025first reading

Engagement

41 posts held, back to 10 August 2026 — the reader has not yet reached the start of this channel’s public history, so older posts may sit further back, unread. Read across 5 pages of Telegram’s post history, 20 posts per page.

ERR · 30 days
10.6%
avg views ÷ 7,562 subscribers
Avg views / post
804
22 posts measured
Reaction rate
14.3%
reactions ÷ views · ER floor
Posts in window
22
of 41 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 4 November 2026
Posts held41 (10 August 2026 – 4 November 2026)
Views total17,685
Reactions total2,529
Forwards / commentsnot exposed by the public surface — not measured, not estimated
Readings taken5 Nov 2026, 00:18 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

Photos
2
Links
1

Lifetime counters from Telegram’s own channel header, read 5 November 2026 — not the date at the top of this page, which is when the subscriber count was last read. Below Telegram’s rounding threshold, so these counts are exact.

Reaction mix

5,142 reactions across 40 posts, in 8 distinct kinds. The most used accounts for 20.0% of them.

Every reaction kind recorded on the sample, most used first
ReactionCountShareShare, drawn
❤1,02920.0%
👍1,00219.5%
🎉99619.4%
🔥99219.3%
😁93518.2%
👏771.50%
🤩631.23%
🥰480.933%

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 40 of the 41 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 5,142 reactions in total: the kind of figure the paragraph above means by “a reaction total printed elsewhere on the page”.

Measured over the 41 most recent posts we hold, published 10 August 2026 to 4 November 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

Telegram’s own ‘edited’ marker: 0 of 22 posts held from the last 30 days (0%) carried it when this register last read them. The marker says a post was changed after publishing, not what it said before — this register holds the earlier wording only where an edit fell between two of its own reads (see the edit ledger and how it is measured).

4 Nov 2026, 19:17 UTC666 views115 reactionsread 5 November 2026

Social Media & Viral Demo Marketing 服务:社交媒体与视觉演示营销 介绍:AI 工具极其依赖“眼见为实”的直观效果。一句话、一段 5 秒钟的对比视频,比千字软文更有说服力。 详情: * X (Twitter) & LinkedIn:B2B / 开发者 / 生产力 AI 工具的主战场。 通过发布“Before vs After(使用 AI 前后的效率对比)”、“10 个能帮你节省 20 小时的 AI 工具”等清单式推文(Threads)迅速爆火吸粉。 * TikTok, Instagram Reels & YouTube Shorts:C 端 / 创意类 AI 工具(如 AI 换脸、AI 音乐、AI 绘画、AI 动漫化)的爆量阵地。 制作夸张的视觉对比短视频(如“用 AI 30 秒把猫变成动漫肌肉男”),配合流行 BGM,极易引发全民模仿(User Generated Content, UGC…

❤28🔥27👍23🎉20😁17

3 Nov 2026, 18:50 UTC696 views115 reactionsread 5 November 2026

Product-Led Growth & Freemium 服务:产品驱动增长与免费试用 介绍:AI 工具的获客核心是“先用后付”,降低用户的首次体验门槛。 详情: * Freemium & Credit 机制(免费额度 / 积分制): 注册即送免费额度(如 50 个 Credit、每日 10 次免费生成)。用户在消耗完免费额度并深度体验到 AI 的“惊艳时刻(Aha Moment)”后,自然触发订阅付费。 * 低门槛体验(No-Sign-Up Sandbox): 免注册直接在首页提供试用框(如 Chat / Image Generation 体验区)。用户生成出满意的结果后,提示“注册/付费下载高清版”,转化率极高。 * 病毒式水印与分享(Watermark & Powered-by Branded Sharing): 免费版生成的图片、视频或导出文档中带有“Powered by [AI Tool]”水印或分享链接,借助…

😁31❤23👍23🔥20🎉18

2 Nov 2026, 18:39 UTC727 views116 reactionsread 5 November 2026

Unit Economics & Traffic Arbitrage 服务:单位经济学与流量买量模型 介绍:短剧本质上是一种“流量套利”(Traffic Arbitrage)模式,极度依赖精细化的 ROI 计算: · ROI = 用户首日充值 (Day 1 IAP) + 广告收益 (IAA) + 后续 LTV / 买量成本 (CAC / CPI) · 快速测试(Test & Kill):一部新剧上线后,先投 $500 - $1000 测试 Day-1 ROI。如果 ROI 低于阈值(如 < 60%-80%),立即停止买量并弃剧;如果 ROI 爆表,迅速加大预算(Scale up),在 7-14 天内吃尽流量红利。 费用:分发cps,带文档ip+短剧私

❤29🔥24🎉23😁21👍19

1 Nov 2026, 18:31 UTC759 views116 reactionsread 5 November 2026

Content Production & Localized Tropes 服务:内容剧本与本地化爽点 介绍:短剧的本质是“情绪消费”,不同国家和地区的痛点与爽点差异极大 详情: * 国内/中文圈:战神赘婿、战神归来、隐形富豪、逆袭打脸、婆媳矛盾、重生复仇。 * 欧美市场(欧美女频为绝对主力):狼人(Werewolf)、吸血鬼(Vampire)、亿万富翁(Billionaire/Mafia)、契约婚姻(Arranged Marriage)、禁忌之恋。 * 东南亚市场:豪门恩怨、虐恋情深、家庭伦理、阶层逆袭。 * 本地化拍摄(Local Production):虽然成本高(单部欧美剧成本约 $10万 - $20万美金),但因为全外籍演员、符合当地视觉习惯,转化率和 ROI 远远高于单纯的翻译剧。 费用:订制化,具体带需求文档私

😁29🎉27👍22🔥22❤16

31 Oct 2026, 19:02 UTC≈1,320 views111 reactionsread 5 November 2026

AIGC & Tech Stack 服务:AIGC 自动化与技术基础设施 介绍:为了降低制作与出海的成本,AI 深度介入了现代短剧的生产与分发链条。 详情: * AI 翻译、重配音与唇形同步(AI Translation & Lip-Sync): 使用 ElevenLabs、HeyGen 等 AIGC 工具,将国款爆款短剧一键翻译为英语、西班牙语、泰语、印尼语,不仅声音高度还原,还能自动调整演员唇形,实现“一套国产剧,全球低成本卖”。 * AI 剧本与 AI 视频生成(AI Script & Video Generation): 利用 LLM 拆解爆款剧本套路(情绪节点、爽点分布),甚至直接使用 Sora、Runway 等视频生成工具制作特定虚幻特效或纯 AI 短剧,极大地缩短了制作周期(从数月缩短至数天)。 费用:按集,具体带文档私

😁27👍25🎉20🔥20❤19

30 Oct 2026, 19:05 UTC779 views118 reactionsread 5 November 2026

Monetization Models 服务:商业化变现与付费设计 介绍:短剧的变现设计极度参考了手游(Mobile Games)的氪金机制与付费墙(Paywall)设置。 详情: * IAP (In-App Purchase / 内购充值): 前 5-10 集免费,在剧情最抓心挠肝的悬念处(Cliffhanger)弹出付费墙。 采用虚拟金币/钻石计费(如 100 金币看一集,充值 $9.99 赠送 1000 金币)。 高额周卡/月卡/VIP 订阅(Subscriptions):提供“$19.99/周 全场免费看”,极大拉高了用户的自动续费率和用户生命周期价值(LTV)。 * IAA (In-App Advertising / 广告变现): 针对下沉市场或低付费意愿用户,提供“看 30 秒激励视频广告(Incentivized Rewarded Video)解锁下一集”。 * Hybrid (混合变现):前段看广告,后段强充值…

😁30👍29🔥26🎉21❤12

29 Oct 2026, 18:29 UTC788 views117 reactionsread 5 November 2026

Multi-Channel Distribution & Affiliate 服务:多渠道分发与 CPS 切片分销 介绍:短剧行业实现“全网渗透”的社群与全民分销模式(切片带货逻辑) 详情: * 切片分销 / 二创 CPS 联盟(Slice Distribution / Affiliates): 平台(如 DramaBox / 夸克 / 各种短剧小程序)将影视版权授权给成千上万个剪辑创作者(KOC)。创作者二次剪辑加特效、配音后发布在 TikTok、Douyin 或 YouTube Shorts 上,挂上短剧链接/小程序入口。 * 收益分成(RevShare): 用户通过创作者的链接充值后,创作者获得 30%~70% 的高额佣金分成。这种模式让平台获得了几乎“无限”的去中心化免费流量(UGC Organic Traffic)。 费用:ip授权+分成,具体带文档私

🎉26👍26😁25❤21🔥19

28 Oct 2026, 18:43 UTC743 views114 reactionsread 5 November 2026

Social Media Ads & Visual Hooks 服务:社交广告与爆点买量 介绍:短剧是高度依赖信息流买量(Paid Media UA)的行业。它的广告逻辑与传统电影完全不同,采用的是“以剧养剧、切片投流”的爆破模式。 详情: * 黄金3秒与钩子(Visual & Narrative Hooks): 广告素材通常直接截取剧中最具戏剧冲突的“卡点/高潮片段”,如:霸总扇巴掌、婆媳恶斗、战神归来被退婚、捉奸在床、豪门打脸 等。前 3 秒必须完成“建立冲突 + 情绪拉满”。 * 平台矩阵(Meta, TikTok, YouTube Shorts, Kwai): · TikTok / Kwai:冲动型消费主阵地,用户看完 1-3 集投放切片后,情绪被顶到高潮,点击跳转落地页或 App 充值解锁后续剧集。 · Meta (FB/IG):高付费能力人群(如欧美中老年女性、东南亚家庭主妇)的核心获取渠道,跑转化(CPA)效…

❤29🎉24👍23😁22🔥16

27 Oct 2026, 18:49 UTC749 views116 reactionsread 5 November 2026

Supply Chain & Unit Economics 服务:供应链与微观经济模型 介绍:不同于数字产品的无成本复制,电商的生命力建立在物理履约和财务模型之上: * 单位经济学(Unit Economics):毛利润 = 售价 - COGS (产品成本) - Logistics (头程+尾程物流) - Payment Fee (支付手续费) 详情: · 而在营销层面,必须严格控制 CAC (获客成本) < 1/3 x LTV,或者确保首单能覆盖 CAC + 履约成本(1-day ROAS 损益平衡)。 · CRO (Conversion Rate Optimization / 转化率优化):包括落地页加载速度优化、一键结账(Shop Pay / Apple Pay)、信任徽章(Trust Badges)、社会认同(真实 UGC 评价、照片/视频 Review)以及 Buy Now Pay Later (BNPL,如 K…

😁28🎉27❤24👍24🔥13

26 Oct 2026, 18:58 UTC751 views115 reactionsread 5 November 2026

Native Ads & Retail Media Networks (RMN) 服务:原生广告与零售媒体网络 介绍:随着隐私法案和 Cookie 限制,持有第一方真实购物数据的平台成了广告新巨头。 详情: · RMN (零售媒体网络):如 Walmart Connect、Target Roundel、Amazon Ads。品牌直接在这些零售巨头的官网上投广告,利用它们庞大的实名购买数据做精准推送。 · Advertorials (软文型原生广告 - MGID/Taboola):常用于单品爆款(Drop-shipping/Direct-to-Consumer),通过一篇看起来像科技新闻或健康杂志的软文(如“这种新型枕头如何治好了我的颈椎病”),引发好奇后直接引导至单品购买落地页。 费用:坑位费+预充值cpc,带产品文档私

🎉31🔥28👍22❤21😁13

25 Oct 2026, 18:53 UTC761 views108 reactionsread 5 November 2026

E-commerce SEO & Content Marketing 服务:电商搜索引擎优化 介绍:依靠免费的搜索引擎自然流量,降低长期整体获客成本。 详情: * 商业意图关键词(Commercial Intent SEO):针对产品页(PDP)和集合页(PLP)优化“Best [Category] for [Use Case]”等高转化词。 * 程序化 SEO(Programmatic SEO):利用模版化技术和结构化数据(Schema),批量生成数万个按“颜色/型号/尺寸/场景”组合的长尾关键词页面,实现搜索引擎低成本“抓取+覆盖”。 * GEO & AI 引用(AI 搜优):优化产品的 EEAT(经验、专业、权威、信任)评价,让 AI 智能体在回答“2026年最好的轻量化露营帐篷推荐”时,自动引用并推介该品牌。 费用:按次,具体带产品文档私

🎉29❤21👍21🔥21😁16

24 Oct 2026, 18:53 UTC755 views115 reactionsread 5 November 2026

Intent-Driven Search 服务:高意图搜索:Google & Amazon PPC 介绍:不同于社交媒体的“人找货”,搜索广告捕捉的是“货找人”的强购买意图流量。 详情: * Google Shopping / PMax (性能提升广告):通过 Feed 数据(价格、图片、库存)直接将产品呈现在谷歌搜索结果最上方,转化率极高。 * Google GEO (Generative Engine Optimization / AI 搜索优化):针对基于 AI 引擎(如 Google Overviews / Gemini 搜索)的推荐算法进行优化,确保品牌与产品在 AI 自动生成的购物推荐列表中获得优先展示。 * Amazon / Marketplace PPC:如果在亚马逊等平台经营,站内竞价广告(SP/SB/SD)是必选项,直接争夺类目核心词、出价拦截竞品页面流量(Conquesting)。 费用:按定制需求…

❤28😁28🔥21🎉19👍19

Showing the 12 most recent of 41 posts we hold for @kexinyewu. 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.

Mentions

Named by 2 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.

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 2 November 2026 — this entry's latest reading, not the date you are reading this.

“可欣业务站” (@kexinyewu), 7,562 subscribers as measured 2 November 2026. Telegram Register, tgregister.com/channel/kexinyewu.

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.