The Blinkit Grocery Data Analysis project aims to extract meaningful insights from 8,000+ grocery records using Microsoft Excel.
By leveraging pivot tables, charts, and data cleaning techniques, this project provides an in-depth analysis of sales trends, outlet performance, and product visibility.
- ✅ Sales performance of different grocery items.
- ✅ Impact of outlet type & size on total sales.
- ✅ Relationship between item visibility and sales.
- ✅ Best-performing outlets based on sales & customer ratings.
This dataset consists of 13 columns, detailing grocery items, sales, and outlet characteristics.
| Column Name | Description |
|---|---|
| Sr No | Serial number of the entry |
| Item Identifier | Unique ID for each grocery item |
| Item Type | Category/type of grocery item (e.g., Dairy, Bakery, Beverages) |
| Outlet Establishment Year | Year when the outlet was established |
| Outlet Identifier | Unique ID for each outlet |
| Outlet Location Type | Location type of outlet (e.g., Urban, Rural, Tier 1) |
| Outlet Size | Size of the outlet (Small, Medium, Large) |
| Outlet Type | Type of outlet (Supermarket, Grocery Store, Hypermarket) |
| Item Visibility | Visibility score of the item in the store |
| Item Weight | Weight of the item in kilograms |
| Total Sales | Total sales revenue generated from the item |
| Rating | Customer rating of the product |
Using Pivot Tables, Charts, and Filters, the following insights were derived:
✔️ Top-selling product categories: Dairy, Beverages, and Snacks.
✔️ Supermarkets contribute the highest sales; small grocery stores have lower revenue.
✔️ Urban outlets have higher demand compared to rural areas.
✔️ Large-sized outlets generate higher sales revenue than small-sized ones.
✔️ Newer outlets (established after 2000) perform better in terms of sales.
✔️ Hypermarkets had the highest total sales.
✔️ Higher product visibility tends to improve sales.
✔️ Some low-visibility items still sell well due to customer demand.
✔️ Items weighing 1kg-5kg had the highest sales volume.
✔️ Heavy items (above 10kg) were purchased less frequently.
✔️ Higher-rated items had better sales, showing customer satisfaction impacts revenue.
✔️ Low-rated products had less demand, especially in premium outlets.
- Removed duplicates.
- Handled missing values (e.g., replacing missing weights).
- Standardized category names.
- Created pivot tables for sales trends, outlet performance, and item categories.
- Used slicers and filters to analyze different segments.
- 📊 Bar Charts: Total sales across different item categories.
- 🥧 Pie Charts: Outlet distribution.
- 📈 Line Charts: Sales trends over time.
- 🔎 Scatter Plots: Item visibility vs. sales.
- Highlighted top-performing products in terms of sales.
- Marked low-rated products in red for better visibility.
📌 Below are key visualizations used in the project:
- Sales by Item Type 📊 (Bar chart showing revenue by category)
- Outlet Performance by Size 🥧 (Pie chart comparing sales by outlet size)
- Item Visibility vs. Total Sales 🔎 (Scatter plot for visibility impact on sales)
- Customer Ratings Distribution ⭐ (Histogram showing rating distribution)
🖼 (Add screenshots of your pivot tables & charts here)
Follow these steps to explore the dataset & insights:
sh
git clone https://github.com/your-username/blinkit-grocery-analysis.git
Follow these steps to explore the dataset and insights:
- Use Microsoft Excel to access the dataset.
- Explore Pivot Tables & Graphs for detailed insights.
- Modify Filters & Slicers to analyze different segments.
✔️ Microsoft Excel – Data Cleaning, Pivot Tables, Charts
✔️ Data Visualization Techniques
✔️ Exploratory Data Analysis (EDA)
🚀 Expanding the analysis using Python (Pandas, Matplotlib, Seaborn) for advanced visualizations.
📈 Exploring Power BI/Tableau for interactive dashboards.
🔹 Author: Vaibhav Anand
🔹 Email: [email protected]
📢 If you found this project helpful, ⭐ star this repository and contribute with suggestions!