Umeng Quarterly Report: Photo-beautification apps are popular during the holidays, and Beijing users love shopping late at night

On December 15, Umeng officially released the China Mobile Internet Trend Report for the second and third quarters of 2015. In the report released by Umeng, user usage scenarios were specially interpreted in detail through data. The report shows that during the short holiday, as people went out and had more gatherings, the opening rate and daily user activity of photo and beautification applications increased significantly. At the same time, 74% of users who use photo-taking and beautification applications are young female users, 88% of whom are unmarried, and 93% of whom do not own a car.

1. During the short and long holidays, users prefer to use camera apps, and users who go to bed late watch more videos

Through the comprehensive data on users’ devices and applications, if we visualize the user’s usage scenarios, we will find some interesting things things. For example, during the May Day holiday, the usage of photo-taking and beautification applications increases, while the usage of video playback applications will increase during the night and late at night.

Among them, during the May Day holiday, the average daily number of users of photo and beautification applications increased by 18.1%. From the perspective of usage scenarios, during the short holiday, many users will gather with friends or go on short trips. In this scenario, users The demand for using photo-taking applications has increased significantly.

2. Young female users Most of them are unmarried and have no cars

However, just knowing the category of the application is not enough. In a specific scenario, what kind of users will use these categories of applications can be obtained through cross calculation with user attribute data and mobile user behavior data.

Still based on the data during the small and long holidays, during the small and long holidays, the general portrait of the user group of photo and beautification applications with the largest increase in active users is as follows:

3. Guangzhou users also take selfies late at night

Similarly, when we focus on people who go to bed late, we will find that users in different regions have different habits of using apps late at night, but in almost all regions there are many users who use video playback during late night hours class application. Considering the usage habits of users, video playback applications are often used before going to bed, and it is easy for users to ignore the time issue. From a scene perspective, it is easier to use continuously from night to late at night.

In other aspects, Beijing users will access e-commerce shopping guide applications late at night, Shanghai users prefer leisure and entertainment, Guangzhou users will use photos to beautify, and Heilongjiang users are accustomed to reading.

4. Lenovo mobile phone users sleep later

Since different brands of mobile phones target different user groups, we can also find that there are differences between mobile phone users of different brands behavioral differences. Umeng cross-computed mobile phone brand data with users' sleep time and found that users of Lenovo, Motorola, Samsung and other mobile phone brands sleep later than users of other brands.

The user scenarios described above are simply descriptions of some common user behavior states. From the perspective of mobile Internet data analysis, this is far from enough.

Umeng’s data has more and richer value by describing user usage scenarios with data. For example, for promoters, understanding the daily gathering places of deep users will enable them to better carry out local promotion activities; for operators, understanding the characteristics of existing users will allow them to carry out content operations in a targeted manner. At the same time, according to users The habits of using the App at different times can also be used more effectively for operational methods such as message push; product personnel can understand users' usage habits in different scenarios through scene data, so as to carry out product updates and iterations in a focused manner; sales personnel can dig out Develop more accurate sales strategies based on the consumption characteristics of different groups of people in various scenarios. Examples of data applications like this leave a lot of room for imagination.

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