Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal only 3.73% score above 4.5 — exposing a vendor quality gap across cities and cuisines
Tools :
Power BI
Dataset :
FestivalWorks
Techniques :
Online Food Delivery
Problem
Swiggy’s restaurant performance data — spanning ratings, cuisines, pricing, and delivery metrics — was fragmented across multiple sources. This lack of centralized visibility slowed down strategic decisions and obscured patterns critical to growth, logistics, and customer targeting.
Approach
Cleaned and transformed 8,000+ restaurant records using Power Query
Created calculated columns and measures with DAX to segment by rating bands, price tiers, and cuisine categories
Built a multi-page, interactive Power BI dashboard with slicers for city, cuisine, and price range
Conducted correlation analysis between pricing, ratings, and delivery speed using scatter plots and custom tooltips
Structured visuals to support both strategic and operational teams, with annotated insights and filterable views
Key Insights
Urban Hotspots: Rohini, Chembur, and Kothrud lead in restaurant density; Kolkata and Mumbai top city-wise counts
Cuisine Trends: Chinese dominates demand, followed by North Indian and Indian
Quality Gaps: Only 3.73% of restaurants score above 4.5; select brands show strong engagement
Pricing Impact: Higher prices correlate with better ratings and faster delivery
Growth Potential: Residential zones show untapped expansion opportunities
Impact
Delivered a clean, interactive dashboard that visualizes performance across cities, cuisines, and price bands
Identified high-density zones and top-performing cities to guide vendor onboarding and regional marketing
Flagged delivery delays and pricing mismatches using DAX-driven metrics for logistics optimization
Informed menu curation and promotions based on cuisine demand and brand engagement
Proposed vendor improvements, pricing adjustments, and expansion strategies to support Swiggy’s growth roadmap
More Projects
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal only 3.73% score above 4.5 — exposing a vendor quality gap across cities and cuisines
Tools :
Power BI
Dataset :
FestivalWorks
Techniques :
Online Food Delivery
Problem
Swiggy’s restaurant performance data — spanning ratings, cuisines, pricing, and delivery metrics — was fragmented across multiple sources. This lack of centralized visibility slowed down strategic decisions and obscured patterns critical to growth, logistics, and customer targeting.
Approach
Cleaned and transformed 8,000+ restaurant records using Power Query
Created calculated columns and measures with DAX to segment by rating bands, price tiers, and cuisine categories
Built a multi-page, interactive Power BI dashboard with slicers for city, cuisine, and price range
Conducted correlation analysis between pricing, ratings, and delivery speed using scatter plots and custom tooltips
Structured visuals to support both strategic and operational teams, with annotated insights and filterable views
Key Insights
Urban Hotspots: Rohini, Chembur, and Kothrud lead in restaurant density; Kolkata and Mumbai top city-wise counts
Cuisine Trends: Chinese dominates demand, followed by North Indian and Indian
Quality Gaps: Only 3.73% of restaurants score above 4.5; select brands show strong engagement
Pricing Impact: Higher prices correlate with better ratings and faster delivery
Growth Potential: Residential zones show untapped expansion opportunities
Impact
Delivered a clean, interactive dashboard that visualizes performance across cities, cuisines, and price bands
Identified high-density zones and top-performing cities to guide vendor onboarding and regional marketing
Flagged delivery delays and pricing mismatches using DAX-driven metrics for logistics optimization
Informed menu curation and promotions based on cuisine demand and brand engagement
Proposed vendor improvements, pricing adjustments, and expansion strategies to support Swiggy’s growth roadmap
More Projects
Restaurant Analysis of Swiggy
Analysed 8,680 restaurant records to reveal only 3.73% score above 4.5 — exposing a vendor quality gap across cities and cuisines
Tools :
Power BI
Dataset :
FestivalWorks
Techniques :
Online Food Delivery
Problem
Swiggy’s restaurant performance data — spanning ratings, cuisines, pricing, and delivery metrics — was fragmented across multiple sources. This lack of centralized visibility slowed down strategic decisions and obscured patterns critical to growth, logistics, and customer targeting.
Approach
Cleaned and transformed 8,000+ restaurant records using Power Query
Created calculated columns and measures with DAX to segment by rating bands, price tiers, and cuisine categories
Built a multi-page, interactive Power BI dashboard with slicers for city, cuisine, and price range
Conducted correlation analysis between pricing, ratings, and delivery speed using scatter plots and custom tooltips
Structured visuals to support both strategic and operational teams, with annotated insights and filterable views
Key Insights
Urban Hotspots: Rohini, Chembur, and Kothrud lead in restaurant density; Kolkata and Mumbai top city-wise counts
Cuisine Trends: Chinese dominates demand, followed by North Indian and Indian
Quality Gaps: Only 3.73% of restaurants score above 4.5; select brands show strong engagement
Pricing Impact: Higher prices correlate with better ratings and faster delivery
Growth Potential: Residential zones show untapped expansion opportunities
Impact
Delivered a clean, interactive dashboard that visualizes performance across cities, cuisines, and price bands
Identified high-density zones and top-performing cities to guide vendor onboarding and regional marketing
Flagged delivery delays and pricing mismatches using DAX-driven metrics for logistics optimization
Informed menu curation and promotions based on cuisine demand and brand engagement
Proposed vendor improvements, pricing adjustments, and expansion strategies to support Swiggy’s growth roadmap