⚡ Zepto Operations & Sales Analysis

This project involves a deep dive into Zepto's operational data to analyze delivery performance, product category popularity, and customer purchasing trends using advanced SQL queries.

📌 Project Overview

The goal of this analysis is to extract actionable insights from the Zepto dataset, focusing on:

• Order Efficiency: Analyzing delivery times and bottleneck periods.
• Product Performance: Identifying top-selling categories and revenue contributors.
• Customer Behavior: Segmenting users based on order frequency and average order value (AOV).

💡 Key Business Insights

• Top Category: Grocery & Staples contributed the highest revenue among all product segments.
• Delivery Lag: A measurable gap was found between expected and actual delivery times during peak hours.
• Customer Segmentation: High-frequency buyers placed significantly more orders with a higher AOV compared to one-time users.
• Revenue Trends: Weekly sales showed consistent growth patterns with spikes during weekends.

🛠️ Tech Stack

• Database: PostgreSQL
• Tools: pgAdmin/PostgreSQL
• Data Source: zepto_v2.csv (included in repository)

🔍 Key SQL Insights

The Zepto.sql file contains queries addressing:

1. Total Revenue Trends: Monthly and weekly sales growth.
2. Category Deep-dive: Which grocery segments have the highest turnover?
3. Delivery Lag: Calculation of the difference between "Expected" vs "Actual" delivery times.

Sample Query: Top Categories by Revenue

SELECT 
    category_name, 
    SUM(order_amount) AS total_revenue,
    COUNT(order_id) AS total_orders
FROM zepto_sales
GROUP BY category_name
ORDER BY total_revenue DESC
LIMIT 5;

🚀 Getting Started

1. Clone the Repository

   git clone https://github.com/bsanghvi78/Zepto.git
   cd Zepto

2. Import the Dataset

   • Use the zepto_v2.csv file to populate your SQL database table.
   • Ensure the table schema matches the dataset structure before importing.

3. Run the SQL Queries

   • Execute the scripts available in Zepto.sql.
   • These queries contain the business logic and analytical insights for the project.

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📁 Repository Structure

• Zepto.sql — All SQL queries including business logic and analytical insights
• zepto_v2.csv — Primary dataset containing sales and delivery-related records

📌 Dataset included in this repository.