numbers and monthly active rate are from Qimai and TalkingData respectively. They are professional third-party mobile App data analysis companies in China. The occurrence of an event is recorded from news and Weibo posts posted by official Mobike and OFO accounts. This paper is organized as follows. In Section 2, I will describe how data were collected and the methodology I used; Section 3 focuses on data analysis and key findings of the research; Implications and the limitation of this research will be discussed in Section 4 and Section 5. Research Methodology & Data Data used for this research can be classified into two categories: one is the user usage data; the other is the occurrence of events. Because it is hard to convert the occurrence of an event to an actual number, this research uses crossing contrast, by observing the change of user usage data before and after the occurrence of an event, to study its effect on user usage. For the user data, since Mobike and OFO haven’t released their data of users to the public, I collected data from two third-party mobile App data analysis companies, Qimai and Talking data. Qimai monitors the download ranking of Apps in both iOS and Android app stores, and, it calculates the estimated daily downloads of an App based on its ranking, related keywords, and the popularity of the keywords. TalkingData tracks the frequency of users opening an App, and converts the frequency to the monthly active rate of the App. The estimated daily download number measures how attractive the Apps is to the new user; the active rate represents the user loyalty of old users. For the occurrence of events, this paper records event from Weibo posts posted by the official accounts of Mobike and OFO. The reason why this paper chooses Weibo as the main
numbers and monthly active rate are from Qimai and TalkingData respectively. They are professional third-party mobile App data analysis companies in China. The occurrence of an event is recorded from news and Weibo posts posted by official Mobike and OFO accounts. This paper is organized as follows. In Section 2, I will describe how data were collected and the methodology I used; Section 3 focuses on data analysis and key findings of the research; Implications and the limitation of this research will be discussed in Section 4 and Section 5. Research Methodology & Data Data used for this research can be classified into two categories: one is the user usage data; the other is the occurrence of events. Because it is hard to convert the occurrence of an event to an actual number, this research uses crossing contrast, by observing the change of user usage data before and after the occurrence of an event, to study its effect on user usage. For the user data, since Mobike and OFO haven’t released their data of users to the public, I collected data from two third-party mobile App data analysis companies, Qimai and Talking data. Qimai monitors the download ranking of Apps in both iOS and Android app stores, and, it calculates the estimated daily downloads of an App based on its ranking, related keywords, and the popularity of the keywords. TalkingData tracks the frequency of users opening an App, and converts the frequency to the monthly active rate of the App. The estimated daily download number measures how attractive the Apps is to the new user; the active rate represents the user loyalty of old users. For the occurrence of events, this paper records event from Weibo posts posted by the official accounts of Mobike and OFO. The reason why this paper chooses Weibo as the main
ni_yujia_thesis_nyush_honors_2018.docx