Blinkit-Sales-Analysis-Dashboard

📌 Project Overview:Screenshot (70) Screenshot (69) Screenshot (68)

🌐 Live Interactive Dashboard

[👉 Click here to view the live interactive Blinkit Ops Monitor](<iframe title="Salesbi" width="600" height="373.5" src="https://app.powerbi.com/view?r=eyJrIjoiMjc4NzFjZWQtOTQ0Yy00YTgzLWFkZDQtYTZkN2YxYTA1MTg0IiwidCI6ImUxNGU3M2ViLTUyNTEtNDM4OC04ZDY3LThmOWYyZTJkNWE0NiIsImMiOjEwfQ%3D%3D" frameborder="0" allowFullScreen="true"></iframe>)

The Blinkit 360° Ops Monitor is an end-to-end Power BI solution designed to analyze delivery performance, sales trends, and operational bottlenecks. This dashboard transforms raw multi-source CSV/Excel data into actionable insights for Store and Category Managers to optimize fulfillment rates and rider allocation.

🎯Key Business Questions Addressed: How does delivery delay correlate with specific stores or product categories?

Which payment methods are most preferred across different order volumes?

What is the relationship between product pricing (MRP) and total sales volume?

How does customer sentiment impact overall order frequency?

📊 Dashboard Visuals

  1. Operations Overview (Sales & Inventory) Total Sales: ₹5.0M with 5K Total Orders.

AOV (Average Order Value): ₹994.48.

Inventory Insights: Real-time tracking of stock levels (Min/Max) to prevent stock-outs.

  1. Delivery & Logistics Performance Delivery Status: Real-time tracking of “On Time” vs. “Significantly Delayed” orders.

Sentiment Analysis: Categorization of orders into Positive, Negative, and Neutral sentiment.

Payment Split: Analysis of Card, Cash, Wallet, and UPI transaction distribution.

📈 Key Metrics (KPIs) Operational KPIs: Average Delivery Delay (currently 4.44 mins), Fulfillment Success Rate.

Sales KPIs: Total Revenue, Total Orders, Average Unit Price (₹493.16).

Customer KPIs: Average Rating by Category, Sentiment Score by Order Volume.

🛠️ Tech Stack & Skills Tool: Power BI Desktop

Data Modeling: Star Schema with multi-source Excel/CSV integration.

DAX (Data Analysis Expressions): * Dynamic AOV calculation.

Time-series sales forecasting.

Conditional formatting for delivery delay status.

Power Query: Data cleaning, attribute standardizing, and merging logistics and sales tables.

🧪 Documentation & Quality Assurance As shown in the Documentation Page, this project followed a strict testing protocol:

TC-001: Navigation Action (Info icon leads to Project Info).

TC-003: DAX Accuracy (Verified Avg Delivery Delay of 4.44 against Excel Pivot tables).

TC-004: Slicer Sync (Ensured filters on Page 1 apply globally to Page 2 visuals).

💡 Business Recommendations Logistics Optimization: Focus on Store IDs showing consistent “Significant Delays” (e.g., Store 14) to investigate local rider shortages.

Payment Strategy: With UPI and Cards holding a nearly equal share (~25% each), Blinkit should maintain robust integration for both to prevent checkout friction.

Stock Management: Categories like ‘Baby Care’ and ‘Snacks’ show high sales volatility; implementing automated re-order triggers at the 74.75 average stock level is recommended.

🧠 Technical Highlights:

DAX MeasuresTo provide deeper insights, I developed several custom DAX measures.

Here are some of the core calculations used in this dashboard:

Total Sales: $$TotalSales = SUM(‘Blinkit_Data’[Sales])

\(Average Sales per Outlet:\)AvgSales = DIVIDE([TotalSales],

DISTINCTCOUNT(‘Blinkit_Data’[Outlet_Identifier]))$ * Top Rated Items (Conditional): > I used a combination of AVERAGE and RANKX to identify top-performing product categories based on customer feedback and sales volume.

📈 Business Insights & Recommendations:

Based on the dashboard analysis, here are the key takeaways for the Blinkit management team:

Outlet Size Performance: Medium-sized outlets are contributing to the highest percentage of total sales. Small outlets have the highest “Sales per Square Foot” efficiency, suggesting a model for rapid urban expansion.

Item Fat Content Analysis: There is a significant trend towards “Low Fat” items in Tier 1 cities, whereas “Regular” fat content items dominate Tier 3 sales.

Recommendation: Adjust inventory stocks based on city-tier health preferences to reduce wastage.

Item Type Dominance: Fruits and Vegetables show the highest volume but have lower margins compared to Household items.

Recommendation: Implement “Bundle Offers” (e.g., Household cleaner + Fresh produce) to increase the Average Order Value (AOV).Rating vs. Sales Correlation: Surprisingly, items with a rating between 3.5 and 4.2 show higher sales velocity than 5-star items, likely due to price sensitivity.

🛠️ Data Cleaning Process:

Before building the visuals, I performed the following Power Query steps:

Handling Nulls: Replaced missing values in Item_Weight using the average weight for that specific Item_Identifier.

Standardizing Labels: Cleaned the Item_Fat_Content column (e.g., converted “LF” and “low fat” to “Low Fat” for consistency).

Data Type Formatting: Ensured all currency and date fields were properly formatted for time-series analysis.