This project presents an end-to-end Excel-based analytics case study analyzing restaurant and locality-level customer engagement for a food delivery platform (Swiggy-style dataset).
The goal is to uncover demand patterns, evaluate pricing and rating impact, identify underperforming localities, and provide actionable business insights.
- Identify high-performing and underperforming localities
- Analyze customer engagement across price ranges
- Understand the impact of ratings on demand
- Support data-driven operational and visibility decisions
The dataset includes:
- Restaurant ID & Name
- City & Locality
- Average Price for Two
- Ratings & Review Count
- Customer Engagement / Demand Indicators
All data cleaning, transformation, and analysis were performed using Microsoft Excel only.
- Microsoft Excel
- Data Cleaning & Standardization
- Feature Engineering (Price Bands, Rating Buckets)
- Pivot Tables & Pivot Charts
- KPI Analysis & Dashboard Design
- Total Engagement: 1,359,590
- Average Rating: 3.7
- Total Restaurants Analyzed: 8,680
- Underperforming Localities Identified: 130
- Medium-priced restaurants drive the highest customer engagement, outperforming low and high price segments.
- Customer demand is highly concentrated in a limited number of localities.
- Underperforming localities are commonly associated with lower ratings and limited restaurant visibility.
- Higher-rated restaurants consistently show stronger engagement, confirming ratings as a key demand driver.
- Improve restaurant quality, onboarding, and visibility in underperforming localities.
- Scale operational and promotional support for medium-priced restaurants.
- Use locality-level insights to plan targeted marketing and expansion strategies.
This project demonstrates a real-world data analyst workflow, transforming raw operational data into clear insights and recommendations using Excel dashboards—without reliance on external BI tools.