Hong Kong Hotel Bloggers Cooperation

Amateur notes /KOL recommendation /KOC talent/grass planting soft text/account operation/advertising content marketing logic How to study the click-through rate problem? We can disconnect the mobile phone, then open the little red book and find that the discovery page of the little red book is empty; Keep the little red book discovery page, reconnect to the network, and load the data on the little red book discovery page; Let the phone disconnect again and slide off the screen. It is found that the content pushed by Little Red Book at one time is 10. Where 1 likes, 1 likes below 10, 3 likes from 10 to 100, and 5 likes above 100. After repeated refreshing, the network was disconnected, with 2 likes, below 10/like, between 1 0 and 100, and above 100, with 5 likes. Repeat this operation to get the content distribution law. For the item 10, the click rate of this item 10 is investigated, and the average click rate of each item of this item 10 should be10%; Because five items are explosive content, the click rate may be around 10%, and the two increasing click rates should be significantly higher than10%; Of the remaining three low praise content, there may be two hits in 1%-5%. Then the click rate of your content must reach at least 13% or even 20% before it can be detonated again. Compared with the content PK of 10, if the click rate of your content is less than 1 1%, it will definitely not be recommended again. In some ways, you can test the click rate of content in advance. For example, in the era of WeChat official account, some big V voted to test which title is better by putting multiple titles in a fan base of 500 people. How to study the interaction rate? Some people think that different types of interactions have different weights. For example, CES= likes (1)+ favorites (1)+ comments (4)+ followers (8). This theory is not necessarily correct. According to this theory, the content of big bloggers will have obvious advantages. However, many bloggers with hundreds of thousands of fans and millions of fans have very dull content data. Because the discovery page of Little Red Book will recommend the content of the bloggers I pay attention to, I tend to think that high-viscosity fans will help improve the click-through rate and interaction rate, thus helping to increase the content reading. Therefore, the idea of looking for people to interact casually a few years ago, and the idea of looking for people to praise interaction casually, should be invalid. These tricks will interfere with the content recommendation efficiency of Xiaohongshu, and the platform can easily optimize the algorithm in this respect. The discovery page of Xiaohongshu will not only recommend the content of the bloggers you are concerned about, but also recommend the content of the same city (nearby). Therefore, there is room to explore the idea of improving the opening rate and interaction rate from these two angles. How does the content marketing logic of Xiaohongshu study the opening rate of search results? We think from the standpoint of search users. Users search for keywords with certain needs, find the content that best meets their own needs in the search results, and have the desire to interact when reading some content. What will be displayed in the keyword search results: the first 22 words of the title+the * * of the text, the title, nickname and favorite number. Different from the discovery page push, the content of the discovery page only displays the title, not the body. The search results show that the explosive content is mixed with recent content and previously unpopular content. The keywords in the search results must be in the first 22 words, preferably in the front. Searching for people is more accurate. It is necessary to provide accurate content for these precise people and promote their openness and interaction.