BlinkIt - A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Tools :
Power BI
Dataset :
8,000+ records across multi-outlet retail data
Techniques :
Power Query, DAX
Problem
BlinkIt’s retail data — spanning product categories, outlet formats, customer ratings, and geographic performance — was fragmented across multiple sources. This lack of unified visibility slowed strategic decisions around inventory planning, outlet benchmarking, and customer engagement.
Approach
Consolidated and cleaned Blink It’s retail dataset in Power BI, resolving inconsistencies across product and outlet attributes
Created calculated fields (e.g., average sales per transaction, fat content segmentation) to enable deeper analysis
Built a multi-page, interactive dashboard with slicers for product category, outlet format, and region
Designed bar charts, heat maps, scatter plots, and KPI cards aligned to business questions
Mapped customer satisfaction trends, outlet performance, and geographic sales density
Delivered full metric breakdown across outlet types to support strategic planning
Insights
Product Composition Impact: Fat content influenced sales volume, item count, and customer ratings
Top-Performing Categories: Certain item types consistently led in revenue and satisfaction
Outlet Performance Trends: Older outlets outperformed newer ones in total sales
Size vs. Sales Correlation: Larger outlets contributed more to overall revenue
Customer Preferences: Satisfaction patterns linked to product composition and outlet format
Impact
Unified fragmented retail data into a dynamic Power BI dashboard for decision-makers
Identified top-performing products and outlet formats to guide planning
Revealed preferences linked to fat content, enabling targeted product strategies
Highlighted high-performing regions to support geographic growth
Enabled data-driven decisions across sales, inventory, and customer engagement
More Projects
BlinkIt - A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Tools :
Power BI
Dataset :
8,000+ records across multi-outlet retail data
Techniques :
Power Query, DAX
Problem
BlinkIt’s retail data — spanning product categories, outlet formats, customer ratings, and geographic performance — was fragmented across multiple sources. This lack of unified visibility slowed strategic decisions around inventory planning, outlet benchmarking, and customer engagement.
Approach
Consolidated and cleaned Blink It’s retail dataset in Power BI, resolving inconsistencies across product and outlet attributes
Created calculated fields (e.g., average sales per transaction, fat content segmentation) to enable deeper analysis
Built a multi-page, interactive dashboard with slicers for product category, outlet format, and region
Designed bar charts, heat maps, scatter plots, and KPI cards aligned to business questions
Mapped customer satisfaction trends, outlet performance, and geographic sales density
Delivered full metric breakdown across outlet types to support strategic planning
Insights
Product Composition Impact: Fat content influenced sales volume, item count, and customer ratings
Top-Performing Categories: Certain item types consistently led in revenue and satisfaction
Outlet Performance Trends: Older outlets outperformed newer ones in total sales
Size vs. Sales Correlation: Larger outlets contributed more to overall revenue
Customer Preferences: Satisfaction patterns linked to product composition and outlet format
Impact
Unified fragmented retail data into a dynamic Power BI dashboard for decision-makers
Identified top-performing products and outlet formats to guide planning
Revealed preferences linked to fat content, enabling targeted product strategies
Highlighted high-performing regions to support geographic growth
Enabled data-driven decisions across sales, inventory, and customer engagement
More Projects
BlinkIt - A Retail Intelligence
Transformed 8,000+ retail records to find newer outlets generate 68% higher sales, redefining outlet benchmarking and inventory strategy.
Tools :
Power BI
Dataset :
8,000+ records across multi-outlet retail data
Techniques :
Power Query, DAX
Problem
BlinkIt’s retail data — spanning product categories, outlet formats, customer ratings, and geographic performance — was fragmented across multiple sources. This lack of unified visibility slowed strategic decisions around inventory planning, outlet benchmarking, and customer engagement.
Approach
Consolidated and cleaned Blink It’s retail dataset in Power BI, resolving inconsistencies across product and outlet attributes
Created calculated fields (e.g., average sales per transaction, fat content segmentation) to enable deeper analysis
Built a multi-page, interactive dashboard with slicers for product category, outlet format, and region
Designed bar charts, heat maps, scatter plots, and KPI cards aligned to business questions
Mapped customer satisfaction trends, outlet performance, and geographic sales density
Delivered full metric breakdown across outlet types to support strategic planning
Insights
Product Composition Impact: Fat content influenced sales volume, item count, and customer ratings
Top-Performing Categories: Certain item types consistently led in revenue and satisfaction
Outlet Performance Trends: Older outlets outperformed newer ones in total sales
Size vs. Sales Correlation: Larger outlets contributed more to overall revenue
Customer Preferences: Satisfaction patterns linked to product composition and outlet format
Impact
Unified fragmented retail data into a dynamic Power BI dashboard for decision-makers
Identified top-performing products and outlet formats to guide planning
Revealed preferences linked to fat content, enabling targeted product strategies
Highlighted high-performing regions to support geographic growth
Enabled data-driven decisions across sales, inventory, and customer engagement