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BlinkIT Retail
A comprehensive sales and market analysis dashboard developed to optimize grocery item placement, evaluate outlet performance, and identify key drivers of sales volume and customer ratings across different store types and locations.
The Business Problem & Goal
BlinkIT faced challenges in understanding the complex relationship between product characteristics (Item Fat Content, Item Type) and outlet attributes (Outlet Location Type, Outlet Size) on overall sales. The goal was to provide actionable insights to maximize sales volume (Total Sales: 1.2M) and improve the average customer rating (Average Rating: 3.96).
Data Cleaning, Modeling, and Advanced Analysis:
Data Cleaning, Modeling, and Advanced Analysis:
Data Cleaning (Power Query):
The primary challenge was normalizing inconsistent categorical data. Power Query was used to implement over 3000 replacements to standardize fields like "Item Fat Content" (replacing 'Lf', 'reg' with 'Low Fat' and 'Regular').
Analytical Toolset:
The analysis leveraged Excel Pivot Tables to calculate sales performance across different Outlet Types and Item Types, identifying high-performing segments.
KPIs Developed:
Key Performance Indicators calculated included Total Sales, Average Sales per Item, and Average Rating per Outlet.
The Interactive Solution (Full Dashboard Demo)
A detailed, multi-view dashboard was created to offer deep visibility into sales trends, allowing users to slice data by Outlet Location, Item Type, and Item Fat Content.
The Interactive Solution (Full Dashboard Demo)
A detailed, multi-view dashboard was created to offer deep visibility into sales trends, allowing users to slice data by Outlet Location, Item Type, and Item Fat Content.
Key Insights & Findings
Key Insights & Findings
Outlet Type Dominance:
Supermarket Type1 dominated revenue generation, accounting for a total sales volume of 787,549.89 units, making it the most critical revenue channel.
Item Type Performance:
Snack Foods were the best-selling item type, achieving significantly higher sales than other product categories, indicating strong consumer focus on this category.
Impact of Fat Content:
Products categorized as Low Fat significantly outperformed in sales, achieving 776,319.68 units, confirming consumer preference for lighter/healthier options.
Business Impact & Conclusion
The BlinkIT analysis provided the business with a clear roadmap for inventory management and store strategy. By quantifying the performance drivers, the project led to:
Business Impact & Conclusion
The BlinkIT analysis provided the business with a clear roadmap for inventory management and store strategy. By quantifying the performance drivers, the project led to:
Optimized Inventory Strategy: A renewed focus on stocking Low Fat Snack Foods in high-traffic outlets (Supermarket Type1), ensuring shelf space and promotional efforts target the highest-value segments.
Outlet Strategy: Insights were provided to increase sales volume per item (Average Sales: 140.99 units) in underperforming stores, by implementing best practices observed in leading outlets.
Data Reliability: The robust data cleaning steps (over 3,000 replacements in Power Query) ensured that all strategic decisions were based on reliable and standardized data.
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