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Evaluating the Impact of Various Analytics Metrics on YouTube Viewership Using Statistical Methods

Nahid Hasan Co-Author
East Texas A&M University
 
Numan Ahmad First Author
 
Numan Ahmad Presenting Author
 
Monday, Aug 4: 9:50 AM - 10:05 AM
2185 
Contributed Papers 
Music City Center 
Millions of videos are uploaded to YouTube daily, but only a fraction gain widespread viewership. Understanding key analytics is essential for creators optimizing reach and engagement. This study analyzed YouTube data to identify factors influencing video performance using correlation analysis and multiple linear regression. We examined watch time, average percentage viewed, average view duration, impressions, and click-through rates on video views. Results showed that impressions, click-through rates, and audience retention had little impact in the first 24 hours but gained significance over time, suggesting a shift from engagement-driven to algorithm-driven exposure.

To account for variations among videos, we applied a linear mixed-effects model with a random intercept per video and a random slope for days after posting. This approach captured individual growth patterns, explaining 98% of variance in views. Our findings underscore the role of fixed and random effects in video performance trends, providing actionable insights for creators optimizing long-term reach.

Keywords

Click-through rate (CTR)

YouTube

Audience retention

Watch time

Average percentage viewed

Linear mixed effects model 

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