Exploring the Preferences of US YouTube Users and Factors Related to YouTube Uploader’s Revenue

Abstract

YouTube is a mainstream media that is popular with the general public. It has a powerful yet special force that attracts almost all young people who come into contact with it. YouTube permeates every aspect of people’s lives, education, daily life, work, leisure, and entertainment. Everyone can be a professional uploader. It is very important to know how to be a good uploader and what the important elements are related to generating revenue from YouTube. This article uses the top 1000 channels and the top 200 daily YouTube video’s data from September 2020 to January 2022 in the United States to analyze popular categories of channels, and use the YouTube videos and their related data from September 2020 to January 2022 from Ken Jee to analyze uploader characteristics. The dataset of this article is based on YouTube API and the uploader’s release. CRISP-DM method is applicated to exploring the data in Jupyter using Python. In addition, Pearson correlation analysis is used to analyze revenue-related metrics. This paper contains both qualitative and quantitative analyses. In this paper, we found that among the top 1000 most popular uploaders, Entertainment, and Music types are the most popular, with 241 and 222 respectively. The Autos & Vehicles and Travel & Events categories are the least represented, with only 1 on the list. This article also finds out Entertainment, Music, People & Blogs and Gaming type of videos users are most attracted to, the core audience, and their interests. Have strong positive relation with revenue. In addition, watch time (hours), subscribers lost, likes, shares, and dislikes are strongly positively correlated with revenues. Comments are moderately positively correlated with revenues. RPM and CPM almost have no correlation with revenue. This study offers a statistical analysis of the content genre based on a number of categories and details of YouTube users’ interests. This study also demonstrates the variables that might have an impact on YouTube uploaders’ revenue. So that uploaders, researchers, and investors can acquire a rough idea of the impact on YouTube.