Blinkit_Data_Analysis_In_Excel

Table Of Contents

• Project Overview
• Tools
• Data Analysis
• KPI Values
• Insights
• Recommendations

Project Overview :

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This project aims to analyze Blinkit's sales performance, customer satisfaction, and inventory distribution using Microsoft Excel. By leveraging key performance indicators (KPIs) and data visualizations, the goal is to uncover valuable insights and identify opportunities for optimization. The findings will help improve operational efficiency, enhance customer experience, and drive better decision-making.

KPI's Requirements:-

• Total Sales: The overall revenue generated from all items sold.
• Average Sales: The average revenue per sale.
• Number of items: The total count of different items sold.
• Average Rating: The average customer rating for items sold.

Dashboard :

Tools

• Microsoft Excel - Data Cleaning, Data Analysis, Creating Reports
  • Download here

Data Analysis

• Data Collection:

  • Collected data from a reliable source to ensure comprehensive coverage of sales information.

• Data Loading and Cleaning:

  • Imported the dataset into Microsoft Excel.

• Cleaned the data by:

  • Removing unnecessary columns and duplicate entries.
  • Handling missing values appropriately.
  • Adjusting data types for consistency.

• Data Analysis:

  • Used pivot tables to perform an in-depth analysis of sales, customer trends, and inventory distribution.

KPI Values

• Total Sales: $1.20M
• Average Sales: $141
• Number of Items Sold: 8,523
• Average Customer Rating: 4.0

Insights

• Top-Selling Categories: Fruits & Vegetables and Snack Foods lead sales, generating $0.18M in revenue.
• City-Wise Sales Performance: Tier 3 cities contribute the highest sales, while Tier 1 cities show the lowest performance.
• Health & Hygiene Category: Despite its importance, it offers only 520 products and generates a modest $0.07M in sales.

Recommendations

1. Expand Service Areas:

   • Currently, Blinkit operates only in Type 1 Supermarkets of Tier 2 cities. Expanding to Type 2 & 3 supermarkets and local grocery stores in high-demand areas can boost sales.

2. Leverage High-Demand Trends:

   • Low-fat products are in high demand across all cities. Introducing high-quality, well-stocked options can drive long-term sales growth. Support High-Revenue Stores:

   • Stores established in 2018 show strong revenue potential. Investing in their expansion and support could maximize profitability.

3. Enhance Customer Satisfaction:

   • The average customer rating is 4.0. Conducting detailed customer feedback analysis and implementing improvements can enhance user experience and loyalty.

4. Optimize Inventory in Tier 3 Cities:

   • Sales in Tier 3 city outlets have remained stagnant for eight years. Assessing inventory quality and optimizing product offerings can help drive growth in these locations.