This project analyzes Blinkit Grocery Sales data to uncover patterns in customer preferences, product demand, and outlet performance.
The insights are presented through an interactive dashboard that highlights key KPIs, sales trends, and outlet characteristics.
The goal is to enable data-driven decision-making for product strategy, outlet expansion, and marketing campaigns.
The dataset contains sales and outlet-related attributes, which were analyzed to build the dashboard.
| Column Name | Description |
|---|---|
Item_Identifier |
Unique ID for each product |
Item_Weight |
Weight of the product |
Item_Fat_Content |
Fat level of the product (e.g., Low Fat, Regular) |
Item_Visibility |
Percentage of product visibility across stores |
Item_Type |
Category of the product (e.g., Snacks, Dairy, Fruits & Vegetables) |
Item_MRP |
Maximum Retail Price of the product |
Outlet_Identifier |
Unique ID for each outlet |
Outlet_Establishment_Year |
Year the outlet was established |
Outlet_Size |
Outlet size (Small / Medium / Large) |
Outlet_Location_Type |
Location tier (Tier 1, Tier 2, Tier 3) |
Outlet_Type |
Type of outlet (Grocery Store, Supermarket Type1/2/3) |
Item_Outlet_Sales |
Sales value of the item in the outlet |
Outlet_Sales (derived) |
Total sales of the outlet |
Rating (derived) |
Customer average rating for outlets |
The Power BI dashboard visualizes the dataset using multiple KPI cards, charts, and filters.
- Total Sales : Overall revenue across outlets
- Average Sales per Item : Typical sales performance
- Number of Unique Items : Total unique products sold
- Average Customer Rating : User satisfaction index
- Outlet Establishment Analysis → Sales vs. Year of establishment
- Item Fat Content Analysis → Sales contribution of Low Fat vs Regular items
- Item Type Breakdown → Top-performing categories (e.g., Fruits & Vegetables, Snacks)
- Outlet Size & Location → Performance comparison across outlet sizes and city tiers
- Outlet Type Comparison → Supermarkets vs Grocery Stores in terms of sales & visibility
- Interactive Filters → Filter by outlet type, size, and item category
From the analysis, some key insights are:
- Low Fat products generate higher sales than Regular fat products.
- Fruits & Vegetables and Snacks dominate item sales.
- Medium-sized outlets in Tier 3 locations perform the best.
- Supermarkets Type1 show the highest overall sales compared to Grocery Stores.
- Power BI → Interactive dashboard creation
- Excel/CSV → Data cleaning & transformation
- (Optional) Python/SQL → Preprocessing & exploratory analysis
- Clone the repository:
git clone https://github.com/SamyukthaaAnand/Blinkit-Grocery-Sales-Outlet-Analysis.git
- Open the dashboard.pbix file in Power BI Desktop.
- Explore the visuals and interact with filters to gain insights.
Samyuktha Anand
🔗 GitHub Profile
This project is licensed under the MIT License – feel free to use and adapt.