FoodPanda Customer Review Data Analysis by Martin Sajeeb FoodPanda Customer Review Data Analysis by Martin Sajeeb

FoodPanda Customer Review Data Analysis

Martin  Sajeeb

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.