Enhancing Xiaohongshu Note Reach: Tips for Maximizing Exposure and Engagement
Data Analysis on Xiaohongshu | Key Data You Should Know
⚠️ When operating an account, many tend to overlook the aspect of data review! However, regular analysis and review of account data can help us better identify issues with note performance in a timely manner, providing optimization directions for subsequent note topics and content output, in order to better summarize the methodology of account operation and improve account quality.
I. Before conducting data analysis on Xiaohongshu notes, let's first understand the core data of Xiaohongshu:
• Exposure: The number of times notes are displayed on users’ phones, with each display counted as one exposure. The displayed data is the cumulative exposure from note publication to the date of data update.
• Interaction: The total number of likes, saves, and comments, with the displayed data being the cumulative interaction from note publication to the date of data update.
• Views/Reads: When users enter the body of a note, it counts as a read. The displayed data is the cumulative number of reads from note publication to the date of data update.
• Shares: The number of times users share notes, such as sharing to WeChat, etc., with shares being the cumulative number of shares.
• Note Followers Increase: The number of people who become followers by clicking on the profile picture in the note during the statistical period, without excluding the number of users who unfollow.
Note’s Basic Data
• Fan Reading Ratio: The proportion of account followers among note readers.
• Interaction Rate: The interaction rate of notes can be calculated by dividing the interaction volume by the reading volume.
• 5s Play Rate: The proportion of users who watch videos in notes for more than 5 seconds.
• Reading Unit Price: (Blogger Quote + Platform Service Fee) / Reading Volume under all traffic, also known as CPV.
• Interaction Unit Price: (Blogger Quote + Platform Service Fee) / Interaction Volume under all traffic, also known as CPE.
Blogger’s CPV & CPE
II. After understanding the core data, the dissemination effect of Xiaohongshu notes will be analyzed from the following four dimensions:
• Overall Data
The Xiaohongshu professional account backend will provide detailed note data for the past 7 days & 30 days, helping us to objectively see different stages, not only providing absolute values, but also including trend graphs, which can be understood as a refined version of core data overview.
Pro Tip: When analyzing data, remember to separate the analysis of notes with and without investment in traffic, otherwise, it will be meaningless.
• Comment Perspective
This is a function that generates a word cloud based on the comments section of notes. We can understand the core words of user comments through the word cloud, further understanding user concerns. The word cloud is clickable, and clicking on it will display relevant comments.
• User Profile
We can see the user profile of the account and notes through the backend, which can help us better understand the audience of notes and potential target audiences.
III. Xiaohongshu Note Analysis
🌟 Step 1 of Note Analysis: Compare past notes to measure note data
Only by measuring note values can we provide direction for subsequent diagnostic reasons.
📍 Three points need to be measured here:
① Is the exposure of the note higher or lower compared to past notes?
② Is the reading volume of the note higher or lower compared to past notes?
③ Is the interaction rate of the note higher or lower compared to past notes?
🔎 Minor fluctuations in notes are within a reasonable range, while significant fluctuations require further note diagnosis. For example, if the reading volume of past notes is 10-20k, and the reading volume of this note is only 1-2k or even lower, then this note needs further diagnostic reasons.
*In addition to referring to the data of past notes, the backend will also display whether a certain data exceeds/falls below n% of similar bloggers. We can also appropriately refer to this data to understand the performance of notes among similar bloggers.
IV. Xiaohongshu Note Diagnosis
You can refer to the quadrant diagram of exposure & click-through rate to further diagnose notes below, and then combine the problems of notes for note optimization.
V. Xiaohongshu Note Optimization
High Exposure, High Click-through Rate
This indicates that the note has good appeal, but we can further analyze the note from the following dimensions and optimize it:
① Low Interaction Rate: If the likes of the note are low, it is probably because the content of the note itself is not interesting enough to resonate with users; if the save volume of the note is low, it proves that the note’s “altruism” is low, and it does not have value for users to save; if the comments are low, it may be because the value of the note for discussion is not high. We can optimize the content according to different reasons.
② User comments are off-topic: That is, the user comments are not related to the content of the note, and the content of the note needs to be optimized. This situation probably occurs because the core content that the note wants to convey is hidden too deeply/planted too softly, and users cannot accurately receive it. Soft planting objectively recommends products and appears reasonably, rather than being too weak to let users perceive the core content that the note wants to convey.
③ Low 5S play rate of videos: This indicates that the beginning content & cover of the note is not attractive enough, probably because the content introduction is too long, making it difficult for the video content to attract user attention within the first 5 seconds. Properly placing key content in the early stage will be easier to attract users to watch the entire video.
④ Video completion rate: If the video completion rate is low, it proves that the video content may be too long, and users lose patience to watch. For long videos, we can properly add progress bars in the picture to help users find the content they want to watch more quickly. You can also pay attention to the compactness of video content, so that each paragraph has key points, continuously stimulating user interest in watching.
High Exposure, Low Click-through Rate
If the overall click-through rate of notes is high but the exposure is low, it is probably because users find the content unsatisfactory after clicking on the note, so they did not enter a higher traffic pool when the platform was pushed. In this case, we can further analyze the interaction rate and 5S play rate of notes, and then optimize the notes. Refer to the optimization direction of [High Exposure, High Click-through Rate] on the previous page.
Low Exposure, High Click-through Rate
This situation is probably because our note cover, title, or topic direction is not attractive enough. If only the click-through rate is low but the interaction rate is acceptable, we can try to optimize the cover/title of the note first to see the effect; if both the click-through rate and interaction rate are low, then we need to consider the issue of topic selection, and we can refer to the popular and explosive content on the platform to further optimize the subsequent note topics.
Low Exposure, Low Click-through Rate
If the exposure and click-through rate of notes are low, we need to review from the following dimensions:
① Whether the topic selection of the note is appropriate. As mentioned earlier, we can refer to the popular and explosive content on the platform to further optimize the subsequent note topics.
② Whether the content promoted matches the account. For example, if a fashion account promotes food, the effect may be poor.
So the promoted content should match the direction of the account as much as possible.
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