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Decoding Youtube Algorithm

Tools used in this project
Decoding Youtube Algorithm

Excel Workbook

About this project

Goal of the project

To identify the criteria that lead to an increase in views and likes of YouTube videos, and provide insights to help new content creators increase their video engagement.

Methodology

The project collected data containing various video attributes such as views, likes, language, subtitles, duration, and other creator-related information. A total of 585 rows of data were collected manually by conducting a survey and asking other people to fill in the data. Three Key Performance Indicators (KPIs) were created: the total number of videos, the sum of likes, and the sum of views. Pivot tables were created to analyze the relationship between subject matter expertise (SME) and views, language and views, and other attributes. The project then performed a detailed analysis of the data to identify trends and patterns.

Insights

The project found that SME does not significantly affect views, and videos that fall under the entertainment and news categories tend to have higher engagement. Videos featuring teams are preferred over individual creators, and video duration is not a significant factor in engagement. The project also found that videos with 15 to 20 tags tend to have more engagement.

Recommendations

New content creators should focus on creating entertaining and informative content in the entertainment and news categories. Collaborating with other creators or featuring a team in videos can help increase engagement. Using 15 to 20 tags can also improve the visibility of videos. Finally, the thumbnail of the video should be trustworthy and accurately reflect the video's content to improve click-through rates.

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