Development of a Comprehensive YouTube Analytics Dashboard for Enhanced Performance Insights

YouTube, as the world's leading video-sharing platform, hosts a vast ecosystem of creators and viewers. For content creators, understanding audience behavior and preferences is crucial for success. While YouTube provides basic analytics tools, there remains a significant gap in delivering comprehensive, actionable insights. This paper presents an advanced YouTube Analytics Dashboard that leverages state-of-the-art machine learning techniques to provide creators with deeper insights and predictive capabilities. Our dashboard integrates data from the YouTube Data API and employs sophisticated algorithms to analyze viewer engagement, content performance, and audience demographics. Key features include sentiment analysis of comments, trend prediction for video topics, and personalized content recommendations. In a case study involving 100 diverse YouTube channels, creators using our dashboard saw an average 27 percent increase in viewer engagement and a 35 percent improvement in subscriber growth rate over six months. The interactive visualizations offered by our dashboard enable creators to easily interpret complex data patterns, facilitating data-driven decision-making. By providing creators with more robust analytical tools, this research contributes to the broader goal of enhancing content quality and user experience on the YouTube platform.

Authors

Institutions

Publication Details

Published
2024-09-04
DOI
https://doi.org/10.1109/globalaisummit62156.2024.10947784
Citations
1
Primary Topic
Technology and Data Analysis
Type
article
Field-Weighted Citation Impact
0.78
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Development of a Comprehensive YouTube Analytics Dashboard for Enhanced Performance Insights

Garima Jain, Mini Jain, Ritesh Rastogi, N. Niharika et al.
1 citations
Technology and Data Analysis
0.78
article

Development of a Comprehensive YouTube Analytics Dashboard for Enhanced Performance Insights

Garima Jain, Mini Jain, Ritesh Rastogi, N. Niharika, Ajay Kumar, Rudra Pratap Singh
article en
1 citations

Abstract

YouTube, as the world's leading video-sharing platform, hosts a vast ecosystem of creators and viewers. For content creators, understanding audience behavior and preferences is crucial for success. While YouTube provides basic analytics tools, there remains a significant gap in delivering comprehensive, actionable insights. This paper presents an advanced YouTube Analytics Dashboard that leverages state-of-the-art machine learning techniques to provide creators with deeper insights and predictive capabilities. Our dashboard integrates data from the YouTube Data API and employs sophisticated algorithms to analyze viewer engagement, content performance, and audience demographics. Key features include sentiment analysis of comments, trend prediction for video topics, and personalized content recommendations. In a case study involving 100 diverse YouTube channels, creators using our dashboard saw an average 27 percent increase in viewer engagement and a 35 percent improvement in subscriber growth rate over six months. The interactive visualizations offered by our dashboard enable creators to easily interpret complex data patterns, facilitating data-driven decision-making. By providing creators with more robust analytical tools, this research contributes to the broader goal of enhancing content quality and user experience on the YouTube platform.

Jaypee Institute of Information Technology (IN), Tata Consultancy Services (India) (IN)
Openalex Percentile: Top 17%
Technology and Data Analysis
0.78
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.