FoodPanda Customer Review Data Analysis
Martin Sajeeb
FoodPanda Customer Review Analysis
📌 Project Overview
This project analyzes real customer review data to understand customer behavior, service quality issues, and unusual reviewer activity on a food delivery platform.
📊 Dataset Description
Source: Food delivery platform reviews (real data)
Records: ~285,000 reviews
Data cleaned using Microsoft Excel before database import
🛠Tools Used
Excel: Data cleaning & preprocessing
PostgreSQL: Data modeling and SQL analysis
Power BI: Interactive dashboard & insights
🧠Business Questions & Insights
1. When are customers most active in leaving reviews?
Insight: Evening hours generate over half of total reviews
Business Value: Helps plan support staffing and feedback monitoring
2. Are there reviewers with unusual activity patterns?
Insight: Some reviewers posted 10+ reviews within 7 days
Business Value: Possible fake or incentive-driven reviews
3. Why are overall ratings declining?
Insight: Food and delivery ratings both contribute to low overall scores
Business Value: Highlights need for vendor and rider performance improvement
📂 Repository Structure
Schema: Database design
Tables: Cleaned tables
Queries: SQL analysis
Dashboard_photos: Power BI visuals
✅ Key Takeaways
Identified peak review activity windows
Flagged suspicious reviewer behavior
Highlighted cities needing service improvement
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Posted Apr 10, 2026
Analyzed customer reviews for FoodPanda to improve service quality.
