Data analysis project exploring BrightTV user viewership trends, identifying key consumption patterns, and providing actionable insights to drive user engagement and subscription growth.
📌 Project Overview This project analyzes user viewership data for BrightTV, a streaming platform, with the goal of providing data-driven insights to help grow the company’s subscription base.
The analysis focuses on understanding user behavior, identifying viewing trends, and recommending strategies to improve engagement and customer retention.
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🎯 Objectives • Analyze user activity and viewing patterns • Identify key factors influencing content consumption • Detect low engagement periods • Provide recommendations to increase user engagement and subscriptions
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🛠️ Tools & Technologies • SQL – Data querying and analysis • Microsoft Excel – Data cleaning, pivot tables, and visualizations • Power BI / Databricks – Dashboard creation and insights • GitHub – Project documentation and version control
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🔄 Data Processing • Converted timestamps from UTC to South Africa time (SAST) • Cleaned and structured session-level data • Aggregated user activity for trend analysis • Created calculated metrics such as: • Daily Active Users (DAU) • Session counts • Average session duration
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📊 Analysis & Key Insights
📈 User Trends • Peak viewing occurs during evening hours (7 PM – 10 PM) • Higher engagement is observed on weekends • User activity shows consistent patterns across time
🎬 Content Insights • Series content drives higher engagement than movies • Popular genres contribute significantly to total watch time
📉 Low Engagement Periods • Certain weekdays (e.g., Mondays) show lower activity • Opportunity to increase engagement during off-peak periods
🧠 Factors Influencing Consumption • Time of day • Day of week • Content type • User behavior patterns
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🚀 Recommendations
📌 Personalisation • Implement recommendation systems based on user viewing history • Introduce “Because you watched…” features
📌 Content Strategy • Release high-demand content during low engagement periods • Invest in popular genres and series
📌 User Engagement • Send push notifications during peak hours • Highlight trending and new content
📌 Growth Initiatives • Introduce referral programs and free trials • Target inactive users with re-engagement campaigns
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📊 Project Deliverables • 📄 Project Description (PDF) • 📂 Raw Dataset (CSV file) • 🧠 SQL Analysis File • 📊 Excel Analysis (Pivot Tables & Charts) • 📈 Dashboard (Power BI / Databricks) • 🎤 Presentation (PowerPoint) • 🗂️ Project Planning (Miro Flowchart & Gantt Chart)
💼 Business Impact
This analysis provides actionable insights into user behavior and content performance. By implementing the recommended strategies, BrightTV can: • Increase user engagement • Improve customer retention • Optimize content strategy • Drive subscription growth
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🔮 Future Improvements • Build a churn prediction model • Develop a real-time recommendation system • Enhance dashboards with live data integration
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🙌 Author Rinae Netshilinganedza Aspiring Data Scientist | Data Analyst