Streaming platforms need to understand which features drive user satisfaction and retention. This project analyzes user feedback data to identify improvement opportunities and measure the impact of product decisions.

1. Which features have the highest/lowest satisfaction rates?
2. How does user satisfaction vary by demographics and subscription type?
3. Which user segments should product teams prioritize?
4. What insights can guide feature development decisions?

• Survey Responses: 30 user ratings across 3 core features (voice search, recommendations, interface)
• User Demographics: Age groups, subscription types, regions, signup dates
• Rating Scale: 1-5 (1=Very Dissatisfied, 5=Very Satisfied)

• Data Integration: Connecting survey responses with user demographic data
• Statistical Analysis: Calculating satisfaction rates, averages, and NPS-style metrics
• Business Analytics: Translating data insights into actionable product recommendations
• SQL Proficiency: Advanced queries with JOINs, aggregations, and conditional logic

• Voice Search: 60% satisfaction rate - needs improvement
• Recommendations: 70% satisfaction rate - performing well
• Interface: 80% satisfaction rate - top performer
• Premium users show 15% higher satisfaction across all features
• 25-34 age group most satisfied with voice search functionality

1. Investigate voice search issues - lowest satisfaction, especially among free users
2. Leverage interface design success - apply learnings to other features
3. Consider premium feature differentiation - premium users consistently happier
4. Age-specific optimizations - tailor features to different user demographics

Data Files:

• data/survey_responses.csv - User satisfaction ratings and feedback
• data/user_demographics.csv - User profile and subscription information

SQL Analysis:

• sql_queries/01_data_exploration.sql - Basic satisfaction metrics and feature comparison
• sql_queries/02_satisfaction_metrics.sql - Advanced user segmentation analysis

Documentation:

• README.md - Project overview and business insights
• methodology.md - Analytical approach and assumptions

This project demonstrates the exact analytical workflow used by consumer insights teams at streaming platforms:

• Analyzing user feedback at scale
• Connecting behavioral data with survey responses
• Statistical significance and business impact assessment
• Translating data findings into product strategy recommendations

• SQL: Data analysis and statistical calculations
• CSV Data: Realistic streaming platform survey data structure
• GitHub: Project organization and documentation

This project exemplifies how quantitative analysis complements qualitative UX research to provide comprehensive user insights that drive product design improvements.