Blinkit Sales Analysis - Power BI

This project provides an in-depth analysis of Blinkit Sales Data, exploring trends in Quick Commerce by examining various sales metrics per outlet type, outlet establishment year, location type, customer ratings, and more. The dashboard is designed to facilitate actionable insights into sales performance across different dimensions.

Project Overview

Quick Commerce is emerging as a popular trend compared to traditional E-Commerce. This analysis dashboard leverages Blinkit sales data to provide comprehensive insights across multiple dimensions including outlet types, customer preferences, and regional performance.

Key Metrics

• Total Sales - $1.20 M | Average Sales - $141
• Number of Items - 8,523 | Average Rating - 3.9

Power BI Dashboard

The dashboard uses various visualization techniques for a clear and actionable analysis of the sales data:

• Donut Charts

  • Fat Content: Distribution between Low Fat and Regular items.
  • Outlet Size: Representation of different outlet sizes.

• Clustered Bar Charts

  • Item Types: Distribution of sales across different item types.
  • Fat by Outlet: Analysis of fat content distribution by outlet type.

• Funnel Chart

  • Outlet Location Type: Analysis of outlet performance by location type (Tier 1, Tier 2, Tier 3 cities).

• Line Chart

  • Outlet Establishment Year by Sales: Trends in sales performance based on the year of establishment.

Filters

The dashboard includes filters to refine the data and facilitate targeted analysis, allowing for a more customized insight into specific parameters.

Technologies Used

• Power BI for data visualization and dashboard creation.
• SQL for data extraction and transformation.

Insights Gained

This project provides valuable insights into:

• The impact of outlet location and establishment year on sales.
• Customer preferences regarding product fat content and outlet sizes.
• Regional performance differences and customer ratings by outlet type.

Usage

1. Exploring Sales by Fat Content: Use the Donut Charts to analyze the proportion of Low Fat and Regular items.
2. Outlet-Specific Performance: Leverage the Funnel Chart to view sales performance across different location types.
3. Historical Sales Trends: Review the Line Chart to understand how outlet establishment year affects sales growth.
4. Custom Filters: Apply filters to narrow down insights based on specific categories and metrics.

Contributing

Contributions are welcome! Please submit a pull request or reach out for suggestions and improvements.

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Project Author: Sujit Mahapatra
Contact: LinkedIn