Recently, when I was brushing the little red book, I saw a travel guide note with more than 20 keywords at the end. Not only do travel bloggers love to play like this, but adding keywords at the end has even become the trend of little red books. To sum up, if it is a strategy to write Dali, the key word of Dali ancient city is definitely indispensable. Secondly, they will add the regional key word "Yunnan", the food key word "rice noodle", the scenic key word "Erhai Lake", the ancient city building "Literature Building" and the local specialty "Flower Cake". Really think of the system as an artificial mentally retarded robot! This time, I will give you a rumor. Rumor 1: The more keywords, the higher the traffic. To put it bluntly, we want others to see your notes when searching for these keywords and get more traffic. This logic is actually wrong. Little Red Book notes flow comes from two parts, the first is system recommendation, and the second is keyword search. The system recommends this piece, and the more words, the less accurate it is. If you force keywords into your notes, the system will label your notes incorrectly. For example, if you write about Chenhejia Hotpot and the label is Chengdu cuisine, this note will not be pushed to users who are interested in Michael Chen Hotpot. Moreover, the recommended amount of the system is determined according to the number of user interactions. At the beginning, the exposure amount of the notes will be 500~ 1000, and the reading amount and the number of interactions will continue to push the next wave. There are more words, but the users who push them are more inaccurate, and the data is easier to pull. Keyword search is really the more keywords, the better. However, the ranking of notes is based on keywords, article quality, number of interactions and account quality. The search weight with keywords in the title is higher than the content. Think about it, no matter how many keywords are in the notes, it will take ten minutes to find the meaning after sending them. Even if you don't implant keywords in your article, Xiaohongshu can judge it according to the sentence semantics. Take this "Master Bao" note for example, the system judges that it is a food industry word through the brand of "Master Bao", and then analyzes it as an "evaluation" content from "delicious". Then there is the picture part, which gives a positioning of "food display". Therefore, in the keyword field, as long as you bring accurate keywords in the title, you can write less words that the system can't understand, such as QAQ and Xinshui. Regarding the operation of Little Red Book, there are several well-known "hidden rules of Little Red Book". This time, I will give you a rumor together. Proverbs 2: @ official account can get additional recommendation. Xiaohongshu has its own official number in each category, such as wearing potato, daily potato, entertainment potato, and Xiaohongshu's growing assistant and creative school. It is circulated in various operation tutorials of Xiaohongshu: as long as the @ official number, you can get more exposure opportunities. In fact, it is useless to add "I want to be popular" to Tik Tok. The recommended amount of notes is determined by the platform algorithm, and the official number has no right to recommend. These official numbers are mainly used for teaching, advertising, or forwarding some high-quality content to guide everyone to create. However, these official numbers do forward the content of some bloggers, and the probability is almost the same as that of the emperor choosing a concubine. There are also bloggers who participate in designated activities, bloggers who cooperate with the platform, and those who spend money to buy recommendations. Ordinary users should not be superstitious about this. Benchmarking analysis mainly analyzes a batch of benchmarking accounts and a batch of benchmarking contents. We have a special article on benchmarking analysis. Benchmarking accounts are not only direct competing products, but also accounts that are competitive in some aspects, and also accounts with high overlap in interest points, target groups, content styles, etc. The main value of benchmarking analysis is to provide reference for content creation and operation action optimization. In the field of e-commerce, competitive data analysis is often done, but in the field of content, there are many accounts that usually rob you of traffic, not just from your direct competitors, and there is a lack of special data. Therefore, we will not do the data analysis of the benchmark account every week, just pay attention to our own content. If you need to find bloggers to promote, you need to evaluate the data of these bloggers. Simply looking at the number of fans is easy to make a big error. If a good data analysis model can be established, it will be easier to screen bloggers and give the corresponding appropriate pricing. The data with the strongest correlation with the advertising effect is the conversion rate, which needs to be tested several times and gradually accurate. The content posted by the same blogger, even if the reading volume is the same, the conversion rate of different copywriting may be several times different. In addition to the conversion rate, the data with high correlation is the reading volume. The higher the reading volume of a note, we think it usually brings higher sales. However, the conversion rate of different types of content is actually quite different, several times or even ten times. Some notes belong to high click-through rate and low conversion rate, while others belong to low click-through rate and high conversion rate. There are two solutions. The first one is that through the continuous accumulation of data analysis experience, we can divide the notes into several types and compare the notes of the same type, so that the conversion rate of the notes of the same type will at least not be too far apart. This solution requires continuous data analysis and research, which is difficult. The second is to accumulate and measure the average data through multiple cooperation cases, which can reduce the error to a certain extent and is simple to operate. Because bloggers don't necessarily want you to see the real reading volume, or it is not convenient to investigate the reading volume during the preliminary screening process. Therefore, the generally popular strategy is to count the likes. However, the praise rate of some types of notes can reach 10%-20%, and the praise rate of some types of notes is not even 0. 1%. And some bloggers' notes are mainly from a few loyal fans or praise each other. Notes below 50 likes are easy to be faked by behaviors such as mutual praise and buying likes, so there is the possibility of data fraud. But this is not important in the preliminary screening. The way to evaluate the explosion according to the thousand praise standard is also unreliable. Some notes 1000 likes have a reading capacity of about 1w, and some notes 10w have a reading capacity of 100 likes. Therefore, at the stage of screening accounts before establishing cooperation, we should at least count the following data: nicknames, fans, total likes, titles of top posts, likes of top posts, average likes of recent 10 articles or the last two months, likes of the lowest point in the last two months, likes of 30% ranked works, styles of works and forms of works. The lowest praise is used to estimate the traffic from fans. When it is almost not recommended by the system, the praise of the work is in a depression, and almost all the praises at this time come from fans. 30% points of works can be used to predict what kind of optimistic results you can get from your launch. The basic forecasting formula can be referenced as follows, and then the optimization can be adjusted according to the actual data. Expected reading volume = expected praise volume /3% expected sales volume = expected reading volume × expected conversion rate (1%) expected output = expected sales volume × selling price, so that we can preliminarily estimate the expected output brought by this blogger, and then decide how much advertising fees can be paid at most. Note that it is suggested to calculate the expected output repeatedly according to the actual experience, and the initial data we give are only for reference in the case of lack of data. In the early stage, it is suggested to make a conservative estimate first, that is, the expected output will be reduced by 5- 10 times. If you want to know about the launch of the little red book or different opinions, please welcome the comment area or add WeChat communication.