Zepto Inventory Data Analysis (SQL Project)

Project Overview

This project analyzes Zepto’s inventory dataset using SQL to extract insights related to pricing strategy, discount patterns, stock availability, and product distribution. The goal was to clean raw data and generate actionable business insights from retail inventory data.

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Dataset Information

The dataset contains product-level inventory information including:

• Category
• Product Name
• MRP
• Discount Percentage
• Available Quantity
• Discounted Selling Price
• Product Weight
• Stock Availability Status

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Data Cleaning Performed

• Checked and handled missing values
• Renamed incorrect column headers
• Removed duplicate product records
• Converted pricing fields from paise to rupees
• Standardized boolean stock availability column
• Added primary key column for row identification

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Data Transformation Steps

• Calculated price per gram for weight-based comparison
• Created product weight segments (Low, Medium, Bulk)
• Converted text-based TRUE/FALSE values into boolean format
• Structured dataset for analytical queries

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Business Questions Solved

The following analytical problems were explored:

1. Top 10 best-value products based on discount percentage
2. Products with high MRP but currently out of stock
3. Estimated revenue contribution by category
4. Products where MRP > ₹500 and discount < 10%
5. Top 5 categories offering highest average discount percentage
6. Price-per-gram comparison for products above 100g
7. Product segmentation into Low, Medium, and Bulk categories
8. Total inventory weight distribution per category

Sample SQL Query

Example: Top 5 categories offering highest average discount percentage

SELECT category, ROUND(AVG(discountPercent),2) AS avg_discount FROM zepto_v2 GROUP BY category ORDER BY avg_discount DESC LIMIT 5;

Key Insights Generated

• Identified categories offering the highest average discounts to support promotion strategy decisions
• Detected premium products currently unavailable in inventory indicating restocking opportunities
• Evaluated pricing efficiency using price-per-gram comparison across product segments
• Estimated category-level revenue contribution for inventory prioritization
• Highlighted stock availability gaps across product categories

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SQL Skills Demonstrated

• Data Cleaning
• Aggregations (SUM, AVG, COUNT)
• Filtering with WHERE conditions
• GROUP BY and HAVING clauses
• CASE statements
• Sorting and ranking insights
• Business-oriented query design

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Tools Used

• MySQL
• SQL
• GitHub (Project Documentation)

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Repository Structure

├── zepto_v2.csv # Raw dataset ├── sql_zepto_project.sql # Cleaning, transformation & analysis queries └── README.md # Project documentation