📊 Google Pay India Reviews — User Behaviour & Support Performance Analysis

📌 Project Overview

This project analyses real user reviews of Google Pay India collected from the Google Play Store. The goal is not just exploratory data analysis — but to understand:

• When users face payment issues
• How users react to failures
• How effective developer support is
• Whether support improves user trust

This makes the project closer to a Product + Customer Experience Analytics case study rather than a simple EDA.

🎯 Objectives

• Measure user satisfaction using ratings
• Identify peak complaint periods
• Detect reliability patterns in a fintech application
• Evaluate developer response behaviour
• Analyse response time efficiency
• Study the impact of replies on user perception

Additional engineered features:

• Sentiment classification (Positive / Neutral / Negative)
• Review length
• Weekend indicator
• Response time (hours)

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🔍 Key Analysis Areas

1. Product Health

2. Reliability Behaviour

3. Community Agreement

4. Review Behaviour

5. Developer Support Behaviour

6. Support Efficiency

7. Impact of Support

📈 Tools & Technologies

• Python
• Pandas
• NumPy
• Matplotlib
• Seaborn

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🧠 What This Project Demonstrates

This project simulates real product analytics work:

• Fintech reliability monitoring
• Customer experience analysis
• Support performance evaluation
• Behavioural analytics
• Business insight generation

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🏁 Conclusion

The analysis helps determine:

• When the payment system struggles most
• How users react to failures
• Whether support is proactive or reactive
• If developer responses influence community trust

This project highlights how user reviews can act as a powerful operational monitoring tool for digital products.

Project Structure

Google pay India/ │ ├── data/ │ ├── raw/ │ └── GooglePayIndia.csv │ └── gpay.ipynb │ └── visuals.ipynb ├── README.md ├── requirements.txt └── .gitignore

📬 Author

Abhishek Singh Aspiring Data Analyst | Python • SQL • Power BI