Restaurant Analysis of Swiggy
Transformed fragmented restaurant data into strategic insights using Power BI — accelerating
decisions across vendor onboarding, delivery optimization, and customer engagement.
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI
Dataset :
8,000+ restaurant records
Techniques :
Power Query, DAX, calculated fields, correlation analysis
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.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
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
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 Indian and fast food
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
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
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
github link
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Restaurant Analysis of Swiggy
Transformed fragmented restaurant data into strategic insights using Power BI — accelerating
decisions across vendor onboarding, delivery optimization, and customer engagement.
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI
Dataset :
8,000+ restaurant records
Techniques :
Power Query, DAX, calculated fields, correlation analysis
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.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
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
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 Indian and fast food
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
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
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
github link
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Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
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Delivered strategic insights and recommendations impacting content strategy, marketing, and audience engagement
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BlinkIt – A Retail Intelligence
Completed as a guided Power BI project in a single day — transforming fragmented retail data into actionable insights for smarter decisions across sales, inventory, and customer strategy.
Techniques: Power Query, DAX
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
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Restaurant Analysis of Swiggy
Transformed fragmented restaurant data into strategic insights using Power BI — accelerating
decisions across vendor onboarding, delivery optimization, and customer engagement.
Tools :
Power BI
Techniques :
Power Query, DAX
Dataset :
8,000+ records across multi-outlet retail data
Tools :
Power BI
Dataset :
8,000+ restaurant records
Techniques :
Power Query, DAX, calculated fields, correlation analysis
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.
Transformed fragmented restaurant data into strategic insights using Power BI accelerating decisions across vendor onboarding, delivery optimization, and customer engagement.
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
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 Indian and fast food
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
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
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
github link
More Projects
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Completed as a guided Power BI project in a single day — transforming fragmented retail data into actionable insights for smarter decisions across sales, inventory, and customer strategy.
Techniques: Power Query, DAX
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
Power BI
6,000+ movie titles
Disney+ Hotstar Data Analysis
Uncovered content trends and audience insights through a Power BI dashboard enabling data-driven decisions across content strategy, marketing, and viewer engagement.
Techniques: Power Query, DAX, calculated fields
Outcome:
Delivered strategic insights and recommendations impacting content strategy, marketing, and audience engagement
More Projects
Power BI
8,000+ records across multi-outlet retail data
BlinkIt – A Retail Intelligence
Completed as a guided Power BI project in a single day — transforming fragmented retail data into actionable insights for smarter decisions across sales, inventory, and customer strategy.
Techniques: Power Query, DAX
Outcome:
Delivered strategic insights and recommendations impacting sales, inventory, and customer strategy
Power BI
6,000+ movie titles
Disney+ Hotstar Data Analysis
Uncovered content trends and audience insights through a Power BI dashboard enabling data-driven decisions across content strategy, marketing, and viewer engagement.
Techniques: Power Query, DAX, calculated fields
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Delivered strategic insights and recommendations impacting content strategy, marketing, and audience engagement