https://github.com/caesaredia/food-app-user-behavior-analysis
Analyze user behavior and optimize app experience in a food-tech startup through funnel analysis and A/A/B testing. Includes data prep, visualization, and statistical testing in Python.
https://github.com/caesaredia/food-app-user-behavior-analysis
a-b-testing chi-square data-analysis data-visualization funnel-analysis python statistical-testing user-behavior
Last synced: about 1 month ago
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Analyze user behavior and optimize app experience in a food-tech startup through funnel analysis and A/A/B testing. Includes data prep, visualization, and statistical testing in Python.
- Host: GitHub
- URL: https://github.com/caesaredia/food-app-user-behavior-analysis
- Owner: caesaredia
- Created: 2025-04-10T05:16:55.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-04-10T05:42:44.000Z (about 1 year ago)
- Last Synced: 2025-04-11T05:39:54.369Z (about 1 year ago)
- Topics: a-b-testing, chi-square, data-analysis, data-visualization, funnel-analysis, python, statistical-testing, user-behavior
- Language: Jupyter Notebook
- Homepage:
- Size: 2.06 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# 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 ðŸ§
- 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`](./data/food_app_user_behavior_data.csv)
- Main Jupyter Notebook with full analysis: [`data/food_app_user_behavior_analysis.ipynb`](./data/food_app_user_behavior_analysis.ipynb)
# Author
Nabilla Hafsah Caesaredia