視頻號可以沉淀私域流量

Video accounts can accumulate private domain traffic

這些所有的社交媒體都是企業(yè)品牌連接客戶的觸點和制造流量的端口，終的收口,大多都以微信作為載體,形成有效的私域流量。

All of these social media platforms serve as touchpoints for corporate brands to connect with customers and create traffic, with WeChat serving as the carrier for effective private traffic.

助力公眾號引流

Helping official account channel

視頻號發(fā)布出來的內(nèi)容下方，可以支持添加公眾號文章鏈接，在公眾號推文持續(xù)走低的情況下，視頻號內(nèi)容可以成為公眾號推文有效的引流渠道。

Below the content released by the video number, you can add official account article links. In the case of official account tweets continuing to decline, the video number content can become an effective channel for official account tweets.

助力公眾號內(nèi)容

Help official account content

在這個看視頻的年代，在充當企業(yè)微官網(wǎng)作用的公眾號內(nèi)容越來越難創(chuàng)新和增粉的情況下，視頻號無疑是可以充實公眾號的內(nèi)容。這樣用一個短視頻的內(nèi)容嵌入視頻號直接可以生成公眾號的推文，也是節(jié)省內(nèi)容創(chuàng)作成本，同時增加內(nèi)容可看性的好方法。

In this era of watching videos, when it is increasingly difficult to innovate and increase the content of the official account that serves as the enterprise's micro official website, the video account can undoubtedly enrich the content of the official account. In this way, the content of a short video can be embedded into the video number to directly generate official account tweets, which is also a good way to save the cost of content creation and increase the visibility of content.

1)精準關鍵詞匹配

1) Accurate keyword matching

視頻號模型會根據(jù)用戶的地理位置、瀏覽喜好、關注話題等維度進行。所以在視頻內(nèi)容、標題文案、話題標簽、地理位置等方面添加對應關鍵詞更有利于系統(tǒng)的精準。

The video account recommendation model will recommend based on dimensions such as user's geographic location, browsing preferences, and topics of interest. Therefore, adding corresponding keywords in video content, title copy, topic tags, geographical location, and other aspects is more conducive to the system's accurate recommendation.

2)符合平臺邏輯

2) Consistent with platform recommendation logic

基于用戶的瀏覽記錄、關注的視頻號、點贊評論分享的內(nèi)容以及不喜歡的內(nèi)容等多維度建模。核心關注互動方面(點贊率、評論率、分享率、完播率)，其中完播率占比較大，一半20S以內(nèi)視頻低于10%就很難被。

Multi dimensional modeling based on user browsing history, followed video accounts, likes, comments, and shared content, as well as disliked content. The core focus is on interactive aspects (like rate, comment rate, sharing rate, completion rate), with completion rate accounting for a relatively large proportion. Half of the videos within 20 seconds are difficult to recommend if they are less than 10%.

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