This project features a comprehensive retail analytics dashboard tailored for Blinkit, India's last-minute grocery delivery app. The dashboard integrates metrics across sales performance, customer satisfaction, and inventory attributes to transform raw e-commerce data into operational insights. Retail stakeholders can dynamically filter data by outlet location, size, and product types to uncover hidden market opportunities and maximize delivery efficiency.
Data Visualization: Microsoft Power BI
Data Engineering: Power Query for handling missing values (e.g., outlet size normalization) and schema alignment
Analytics Engineering: DAX (Data Analysis Expressions) for complex quick-commerce KPIs and market-share measures
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Macro Performance: Driven by highly consistent item demand, the quick-commerce channel maintains robust cumulative sales milestones, balanced average transaction values, and an optimized overall product item count.
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Product Mix Dominance: High-velocity categories such as Fruits & Vegetables, Snack Foods, and Household Essentials generate the largest share of overall consumer revenue.
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Customer Preferences: Regular-fat items pull the highest overall transactional volume; however, healthier Low-Fat variations command a major, fast-growing block of the net market share.
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Geographic Tiers: Tier 3 locations generate the highest collective revenue volume across the delivery network, outpacing Tier 1 and Tier 2 suburban zones.
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Infrastructure Optimization: Small and Medium-sized hyper-local dark stores heavily outperform Large-format fulfillment outlets in overall inventory turnaround and localized order volumes.
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Satisfaction Thresholds: Customer rating matrices indicate tight quality control, with customer satisfaction trends maintaining a stable and positive average score across all active regions.
Insight: Tier 3 regional networks outpace metropolitan zones in gross order volume.
➔ Action: Allocate capital to expand delivery fleet density and broaden dark-store networks in Tier 3 clusters to fully capture surging regional growth.
Insight: High-velocity categories like Fruits, Vegetables, and Snacks bring in roughly 40% of net retail volume.
➔ Action: Implement dynamic, automated inventory replenishment algorithms for top-tier SKUs to eliminate stock-outs during high-demand evening order peaks.
Insight: Small and medium fulfillment dark stores yield higher retail output compared to larger, less agile warehouses.
➔ Action: Pivot real estate strategies away from massive centralized warehouses toward a denser network of smaller, highly localized urban fulfillment centers to lower delivery turnaround times.
Insight: Low-Fat products represent a multi-million market share but lag slightly behind Regular-fat options.
➔ Action: Create "Healthy Living" digital store banners and introduce bundle deals on fresh low-fat dairy or organic items to capture higher average order value from health-conscious consumers.
Insight: Low-performing niches like Breakfast staples, Seafood, and Starchy items show sluggish sales.
➔ Action: Optimize dark-store shelf space by shrinking low-performing SKU variants and replacing them with localized, high-demand snack or convenience items.