Hot spots come unexpectedly, why do other people's 100000+popular products appear frequently?
In the new Internet era, with the generation and dissemination of information and explosive growth, there is also a large user market for high-quality and sustainable content. In the digital society we live in today, the media industry ecology has been greatly enriched. From traditional paper media, radio, television, etc. to new media, multimedia and financial media in the Internet era, the number of media publishing content in online space has shown exponential growth.
In the information environment of massive content, user attention has become a scarce resource, and media content is increasingly difficult to get attention; On the other hand, users' online browsing, likes, forwarding, comments and other behaviors can be recorded and stored in digital form, which provides a data basis for the digital operation of the media platform.
Insight into media content dissemination effect through user behavior data analysis can help media platforms better guide content and user operations. Through the performance of each sub indicator, we can know the exposure, viewing and audience preference of each graphic and text video, and interact with users on the review, sharing and other pages, which can promote communication and improve user stickiness. At the same time, the collected user behavior feedback also provides strategic support for content creation, distribution and adjustment. Through the analysis of user behavior data, in-depth insight into user profiles, consumer psychology and behavior preferences will promote the production of more high-quality graphic videos, improve the stickiness of reading users, and expand the influence of the media itself.
In the active behavior data of content consumers, there are more abundant user insights. Through data burying points on the main business scenarios of the media platform, it can help the platform better analyze and understand user behavior and psychological preferences, and provide strategic support for the subsequent fine operation and continuous explosive content.
Focusing on several main scenarios of user and media platform interface interaction, the key business scenarios we discuss in this article mainly include three scenarios: sharing articles, commenting articles, and watching videos.
Users' active sharing of articles is a key link in the process of communication to form social fission and rapidly expand the scope of influence. Analyze the data generated from relevant operation links, and constantly improve the sharing effect by optimizing the operation process.
In the sharing link, set the burying point on the browsing page, click sharing, select channels, etc. According to the performance of shared data, intuitively check the sharing times of a certain article or a certain type of article, and summarize the characteristics of content with high sharing rate. We can set the funnel of sharing articles through the funnel analysis model of Yiguan Ark, obtain the conversion rate of each node in the user behavior path, understand the user churn in each link, accurately locate the main problems affecting the sharing rate, and carry out targeted optimization and improvement. If it is found that after the user clicks the share button, the location of the target option is not obvious enough, which affects the convenience of the user's sharing, resulting in the user directly exiting the sharing interface.
Article page browsing
Click the Share button
Browse the sharing page and select the sharing channel
Successful sharing
User comments are the most direct way of content quality feedback, and also an important form of UGC content creation. Whether it is the number of comments or the specific content, it can help operators understand the advantages and disadvantages of articles from the perspective of users, and point out the direction for content optimization and improvement.
By setting data burial points in the review article scenario, the media operation department can understand the content interaction, user activity, etc. For example, by comparing and analyzing the review data of different types of articles, we can get the degree of attention of various types of content, and help optimize the content planning and production; By counting the number of comments per capita, the user interaction degree of each platform is compared to provide guidance for content distribution; Users with a large number of comments and high activity have a higher liking and loyalty to media content. They should conduct refined operations, obtain more effective strategic insights from user behavior data, and drive users to co create content with media.
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Long and short videos are popular forms of content consumption in recent years. Optimizing video content creation and distribution based on user behavior data analysis is an important way to maintain the stickiness and competitiveness of media platforms.
Compared with the graphic form, the jumping nodes of video viewing can be more accurately monitored, and the media operation department can gain insight into user behavior preferences based on more abundant user behavior data. For example, through the number of video viewing times and viewers, it can analyze the popularity of different types of videos, and more directionally promote video production; Another example is to learn about video playback in each period, which is conducive to adjusting the push time.
Content page browsing
Click the video play button
Start playing video
End of video playback
The media industry relies on high-quality content to reach a deeper connection with users. In the process of improving content quality and optimizing channel promotion, it needs to fully understand user behavior, dig more potential user feedback from data, and start better content creation and operation.
Author: AnalysysData of Yiguan Ark
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The original link is as follows: https://mp.weixin.qq.com/s/4rHGQf5cLYDjk-wuIN-7sg