# A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform
## 7 Results and Implications  
We investigated the social distancing environment, which is currently a common topic, and examined the changes in a non-face-to-face online home workout service. As previously described, the random forest algorithm discrimination model obtained through machine learning provided accurate discrimination results in testing. Because Keep is China's largest home workout platform and manages a considerable amount of user information, it can utilize machine learning to determine whether its users are continuously using the application.  
The academic implications of this study are as follows. First, we attempted to expand our study methods in the field of home workouts by applying machine learning. As home workout companies have been recently identified, related studies are few, despite the fact that the continuous use of users has become a problem faced by companies. With advances in AI technology, machine learning is efficient in processing information with new research methods and exerts an important role in helping to discriminate and process large amounts of data. In contrast to traditional social science analysis methods, we chose a machine learning approach to make predictions. Machine learning can solve problems faster and more easily, reveal insights, and thus indicate operating plans for similar home workout platform companies and provide competitive advantages.  
Second, it provided indications about the factors influencing continuous exercise participation. We studied the influence of each of the 18 variables that were expected to affect the determination of continuous participation. Among them, the variables that could be reflected had minimal influences, such as the sign-up time, VIP membership status, and the number of likes received. On the contrary, we found that certification, which refers to the verification of the user's identity through peer review, is the most critical factor for continuous use. This indicates that providing factual information verification, such as ID card information, mobile phone verification, or email, highly motivates users to continue working out. The reason is that as the amount of exercise increases, status certification and trust are needed. In this regard, it is believed that the continuous participation of users will increase if companies provide easier methods to gain certification or trust. For home workout companies, machine learning provides operational solutions, such as how to enhance personal certification aiming to increase the continuous use of the platform.  
Third, due to personal privacy reasons, we only collect public data from users, so we cannot collect more personal information. Since its launch in 2015, Keep has more than 300 million users and over 10 million members. However, according to the results, users who did not continue using Keep accounted for a large proportion of all test sets, which was different from the usage rate found in market research. We need to collect more data from more platforms to investigate user adherence to home workout platforms.  
Conceptualization, S.L.; methodology, S.L.; software, Q.C.; validation, Q.C. and S.L.; data curation, Q.C.; writing--original draft preparation, Q.C.; writing--review and editing, S.L.; visualization, Q.C.; supervision, S.L.; project administration, S.L.; funding acquisition, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by supported by the BK21 FOUR Program, funded by the Ministry of Education (MOE, Korea) and the National Research Foundation of Korea (NRF).  
Not applicable.  
This work was supported by an Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2019-0-01343).  
The authors declare no conflict of interest.