This project focuses on creating a dynamic content analytics dashboard for Amazon Prime Video, providing valuable insights into key metrics that can drive content and marketing decisions.

Aim of the Project 🎯: The aim of this project is to develop a comprehensive content analytics dashboard for Amazon Prime Video, using its extensive media dataset to help visualize and analyze key metrics for strategic decision-making in content and marketing.

Project Description 📃: The project utilizes Power BI and Excel to create a content analytics dashboard that analyzes Amazon Prime Video's dataset, offering insights on content genres, viewer preferences, and geographical content distribution.

Project Phases:

• Data Extraction and Cleaning: Extracted data from Kaggle and cleaned using Excel and Power BI for accurate and actionable insights.
• Content Analysis with Power BI: Developed visualizations to break down content by genres, types, and geographical distribution to identify trends and preferences.
• Dashboard Creation: Built a dynamic dashboard to visualize KPIs such as total titles, ratings, genres, and country-wise content distribution.

Steps involved in the process:

• Data Integration 📊: Integrated datasets from Kaggle into Power BI for visual analysis.
• Content Type Breakdown 🛠️: Analyzed the ratio of movies vs. TV shows and identified the focus of content production.
• Genre Popularity Analysis 🎥: Created visualizations to understand genre trends and assist in content strategy formulation.
• Global Distribution Mapping 🌍: Mapped the total shows by country to highlight global content reach and localization efforts.

Important Insight: The data reveals that Amazon Prime Video has a substantial focus on movies, with a need to potentially expand its TV show offerings to balance viewer preferences.

Conclusion: The Amazon Prime Video Dashboard offers valuable insights into content trends, production focus, and viewer preferences, supporting strategic decisions in content development and marketing efforts.

Key Learnings:

• Data-Driven Content Strategy: Leveraging content data for informed decisions.
• Global Content Reach: Understanding geographical content distribution and localization.
• Genre Trends: Identifying popular genres and areas for content expansion.
• Viewer Preferences: Using data to drive decisions around movies vs. TV shows.

Future Scope:

• TV Show Expansion: Focusing on increasing TV show offerings to balance the content library.
• Enhanced Localization: Analyzing and improving localization efforts for global content reach.
• Genre Diversification: Exploring new genres to cater to evolving viewer tastes.
• Advanced Viewer Insights: Utilizing AI and machine learning to predict viewer preferences and trends.