User Behavior Analysis and A/A/B Testing in a Food-Tech App 📊

This project explores user behavior patterns and evaluates the impact of UI changes through A/A/B testing for a food-tech startup's mobile application. By analyzing event logs and user funnel progression, the project uncovers insights to optimize user engagement and improve conversion rates.

Objectives 🧠

• Analyze user behavior and engagement across the product funnel
• Quantify drop-offs between funnel stages
• Evaluate the effectiveness of UI changes using A/A/B testing
• Provide data-driven recommendations for UX optimization

Key Insights 📈

• Average of 32.33 events per user
• Major user drop-off occurs at the OffersScreenAppear stage
• Only 18.36% of users complete the entire funnel
• Statistically significant differences found across experimental groups using Chi-squared testing

Files in This Repository 📚

• Raw dataset used for the analysis: data/food_app_user_behavior_data.csv
• Main Jupyter Notebook with full analysis: data/food_app_user_behavior_analysis.ipynb

Author

Nabilla Hafsah Caesaredia