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(hù)的觸點(diǎn)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(lái)的內容下方,可以支持添加公眾號文章鏈接,在公眾號推文持續走低的情況下,視頻號內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.
助力公眾號內容
Help official account content
在這個(gè)看視頻的年代,在充當企業(yè)微官網(wǎng)作用的公眾號內容越來(lái)越難創(chuàng )新和增粉的情況下,視頻號無(wú)疑是可以充實(shí)公眾號的內容。這樣用一個(gè)短視頻的內容嵌入視頻號直接可以生成公眾號的推文,也是節省內容創(chuàng )作成本,同時(shí)增加內容可看性的好方法。
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(guān)鍵詞匹配
1) Accurate keyword matching
視頻號模型會(huì )根據用戶(hù)的地理位置、瀏覽喜好、關(guān)注話(huà)題等維度進(jìn)行。所以在視頻內容、標題文案、話(huà)題標簽、地理位置等方面添加對應關(guān)鍵詞更有利于系統的精準。
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(hù)的瀏覽記錄、關(guān)注的視頻號、點(diǎn)贊評論分享的內容以及不喜歡的內容等多維度建模。核心關(guān)注互動(dòng)方面(點(diǎn)贊率、評論率、分享率、完播率),其中完播率占比較大,一半20S以?xún)纫曨l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%.
本文來(lái)自:濟南短視頻運營(yíng)更多的內容請點(diǎn)擊:http://m.hfjtr.cn我們將會(huì )為您提問(wèn)的問(wèn)題提供一個(gè)滿(mǎn)意的服務(wù),歡迎您的來(lái)。
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