Customer-Analysis-using-SQL

Problem Statement

Many businesses struggle to make data-driven decisions due to the lack of insights from their customer data. Without a clear understanding of sales trends, customer behavior, and business growth, companies risk missing opportunities to optimize their operations, improve customer satisfaction, and increase revenue.

Goal

The goal of this project is to analyze customer data using SQL to uncover actionable insights that inform business decisions. By examining sales performance, customer behavior, and business growth over time, this project aims to provide a comprehensive understanding of the customer dataset and identify opportunities for improvement.

Overview

This project focuses on analyzing customer data using SQL to uncover insights related to sales performance, customer behavior, and business growth over time. The analysis is performed using MySQL, leveraging SQL queries to extract, transform, and visualize key metrics.

Key Features

• Change Over Time Analysis: Examining customer behavior and sales trends over different periods.
• Cumulative Analysis: Aggregating data progressively to assess business growth and decline over time.
• Sales Performance Analysis: Evaluating key sales metrics to identify patterns and opportunities.

Technologies Used

• MySQL: Database management and querying.
• IPython-SQL: Integration of SQL within Jupyter Notebooks.
• PrettyTable: Formatting query results for better readability.

Getting Started

1. Install dependencies: pip install ipython-sql prettytable==0.7.2
2. Connect to your MySQL database: %sql mysql://root:your_password@localhost/your_database
3. Run the SQL queries in the Jupyter Notebook to generate insights.

Dataset

The project assumes a structured customer dataset containing:

• Transaction details (date, amount, customer ID)
• Customer demographics (age, location, gender)
• Product information (category, price, sales volume)

Future Enhancements

• Advanced segmentation analysis to categorize customers based on purchasing behavior.
• Predictive modeling using SQL and Python to forecast future sales trends.

Contributions

Feel free to fork the repository and submit pull requests for improvements or additional features.