Sales Data Analysis

This project analyzes sales, customer behavior, and product performance using Python. The goal is to understand how the business is growing, how customers behave, and which areas contribute most to revenue.

The analysis uses Pandas, NumPy, Matplotlib, and Seaborn for data analysis and visualization.

Project Goals

The main questions explored in this project:

• How are sales changing over time?

• Are customers returning to make more purchases?

• Which customers generate the most revenue?

• Which products drive most of the sales?

• Which markets have the most customers?

Sales Performance

• Orders increase 16.8% per year.
• Each year shows more orders than the previous one, which suggests the business is gradually expanding.

• Revenue increase 27.2% per year.
• The increase appears steady rather than sudden, which suggests stable business growth.

• Profit increase 27.1% per year.
• When revenue increases, profit also increases, showing that the growth is financially meaningful.

Customer Behavior

• The average amount spent by each customer increases over time.
• This suggests customers are either purchasing more products or buying higher value items.

• Customer Repeat Rate in Five Years: 85.6%
• More customers are returning to make additional purchases, which is a good sign for long-term stability.

• Customer lifetime value increases year after year.
• This means each customer is generating more total revenue for the business over time.

• The United States has the largest number of customers.
• Other countries such as Australia, the United Kingdom, France, Canada, and Germany also contribute to the customer base.

Product Performance

• This chart shows which products generate the most revenue.
• A small number of products contribute a large share of total sales.
• These products play an important role in overall business performance.

• This chart shows how frequently each product is purchased.
• Some products sell in higher quantities, while others have lower demand.
• Understanding which products sell the most helps identify the core products that support daily sales.

Sales Analysis – Key Observations

• Orders increase steadily over the years.

• Revenue shows consistent growth.

• Profit follows a similar trend as revenue.

• Monthly sales remain relatively stable.

• Business growth appears gradual rather than sudden.

Customer Analysis – Key Observations

• Repeat customer rate increases over time.

• Customers are purchasing slightly more frequently.

• Average revenue per customer is growing.

• Customer lifetime value increases each year.

• A smaller group of customers contributes a larger share of revenue.

Product Analysis – Key Observations

• A few products generate most of the revenue.

• Some products sell frequently but bring lower revenue per sale.

• High-value products contribute more revenue with fewer sales.

• Product demand varies across the catalog.

• Top products strongly influence total sales.