The objective of this project is to conduct a comprehensive analysis of Blinkit’s sales performance, customer satisfaction, and inventory distribution using Microsoft Excel.
This project focuses on identifying key business insights and optimization opportunities by analyzing multiple KPIs and visual dashboards across product attributes, outlet characteristics, and customer ratings in a quick-commerce environment.
- The project uses a Blinkit sales dataset containing item-level and outlet-level sales information.
- Key dataset attributes include:
- Item details and categories
- Fat content classification
- Sales and revenue values
- Customer ratings
- Outlet size, type, location, and establishment year
- The dataset is structured to support multi-dimensional sales and outlet performance analysis.
- 🔗 Dataset Link: Dataset
- Quick-commerce platforms like Blinkit operate with diverse products, outlets, and customer segments. Without structured analysis, it becomes difficult to understand how product attributes, outlet characteristics, and customer satisfaction impact overall sales.
- This project addresses this challenge by using Excel-based KPIs and advanced visualizations to uncover patterns in sales performance and operational efficiency.
- The following key performance indicators were calculated:
- Total Sales – Overall revenue generated from all items sold
- Average Sales – Average revenue per sale
- Number of Items – Total count of distinct items sold
- Average Rating – Mean customer rating across products
- These KPIs provide a holistic view of sales volume, value, and customer satisfaction.
- Chart Type: Donut Chart
- Objective: Analyze the impact of fat content on total sales
- Additional KPIs: Average Sales, Number of Items, Average Rating
- Chart Type: Bar Chart
- Objective: Identify high-performing item categories
- Additional KPIs: Average Sales, Number of Items, Average Rating
- Chart Type: Stacked Column Chart
- Objective: Compare outlet-wise sales segmented by fat content
- Additional KPIs: Average Sales, Number of Items, Average Rating
- Chart Type: Line Chart
- Objective: Evaluate how outlet age or establishment type impacts sales
- Chart Type: Donut / Pie Chart
- Objective: Analyze correlation between outlet size and total sales
- Chart Type: Donut / Pie Chart
- Objective: Assess geographic distribution of sales
- Chart Type: Matrix Card
- Objective: Provide a consolidated view of all KPIs by outlet type
- Imported and cleaned raw Blinkit sales data in Excel
- Applied formulas and aggregations for KPI calculation
- Used pivot tables for multi-dimensional analysis
- Designed interactive and structured dashboards
- Applied consistent formatting for readability and insights
| Component | Technology |
|---|---|
| Tool | Microsoft Excel |
| Data Type | Quick-Commerce Sales Dataset |
| Analysis | KPI Calculation, Aggregation |
| Visualization | Donut, Bar, Line, Stacked Column |
| Reporting | Excel Dashboards |
- Product attributes like fat content significantly influence sales
- Certain item categories outperform others in revenue generation
- Outlet size and location play a key role in sales distribution
- Customer ratings provide insight into satisfaction trends
- Excel enables effective multi-KPI business analysis
This project demonstrates how Microsoft Excel can be used for advanced quick-commerce analytics by combining KPIs, pivot-based analysis, and diverse visualizations.
The insights generated help understand product performance, outlet efficiency, and customer satisfaction—making the analysis highly relevant for retail, operations, and data analytics roles.