Power-Bi-Dashboard-blinkit-data

BlinkIT Grocery Sales Analysis (Power BI)

Project Overview

This project analyzes BlinkIT grocery sales data to generate business insights using Power BI dashboards.
The goal is to help the business understand sales performance, customer behavior, and product demand trends in order to improve decision-making, optimize inventory, and drive revenue growth.

The repository includes:

• Raw dataset (BlinkIT Grocery Data.xlsx)
• Power BI dashboard file (Blinkit Sales Project.pbix)
• Documentation of insights and findings

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Business Objectives

1. Analyze overall sales performance across categories and time.
2. Identify top-performing and underperforming product categories.
3. Understand customer purchasing patterns by demographics and region.
4. Highlight demand trends to optimize inventory management.
5. Provide actionable insights for marketing campaigns and sales strategy.

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Project Structure

├── BlinkIT Grocery Data.xlsx # Source dataset ├── Blinkit Sales Project.pbix # Power BI dashboard ├── README.md # Documentation

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Data Description

The dataset contains grocery sales transactions, with the following key fields:

• Item_Identifier – Unique product code
• Item_Weight – Weight of product
• Item_Fat_Content – Low Fat / Regular
• Item_Visibility – Percentage of display area allocated
• Item_Type – Category of product (e.g., Dairy, Fruits, Beverages, Snacks, etc.)
• Item_MRP – Maximum Retail Price
• Outlet_Identifier – Store ID
• Outlet_Establishment_Year – Store opening year
• Outlet_Size – Small / Medium / High
• Outlet_Location_Type – Tier 1 / Tier 2 / Tier 3 cities
• Outlet_Type – Supermarket Type1, Type2, Grocery Store, etc.
• Item_Outlet_Sales – Sales amount (target variable for analysis)

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Methodology

1. Data Cleaning & Preparation

   • Handled missing values in Item_Weight and Outlet_Size
   • Standardized inconsistent values in Item_Fat_Content
   • Derived additional features such as item age and store age

2. Exploratory Data Analysis

   • Univariate and bivariate analysis of product sales
   • Impact of item price (MRP) on sales
   • Store-level sales performance

3. Power BI Dashboard Development

   • Connected dataset to Power BI
   • Built interactive dashboards with slicers (filters)
   • Created DAX measures for KPIs (Total Sales, Avg. Sales, Growth %)

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Dashboard Features

• Sales Overview

  • Total Sales, Average Sales per Outlet, Growth Rate
  • Year-wise and Month-wise sales trends

• Product Analysis

  • Top 10 selling products
  • Sales by product type (Dairy, Beverages, Fruits, Snacks, etc.)
  • Impact of 'Item_Fat_Content' and 'Item_Visibility'

• Customer & Store Insights

  • Sales by location type (Tier 1, Tier 2, Tier 3)
  • Sales by store size and outlet type
  • Age of store vs performance

• Profitability & Pricing

  • Relationship between MRP and sales volume
  • Contribution of different MRP segments to revenue

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Key Insights

1. Top Categories – Snacks and Dairy contribute the most to sales.
2. Customer Preference – Regular fat content items have higher demand than low-fat ones.
3. Store Performance – Tier 3 locations outperform Tier 1 in average sales.
4. MRP Impact – Items with medium MRP ranges (100–200) have the highest sales.
5. Outlet Type – Supermarket Type1 accounts for the majority of revenue.