Project Description
The e-commerce sector stands out as a rapidly growing and developing field today. In this project, we will analyze the sales data of an e-commerce company and evaluate the performance of the business. Our goal is to examine the distribution of sales over time, determine the best-selling product categories, analyze customer behavior and make strategic decisions to increase the profitability of the business. This analysis will help business owners and managers understand sales trends and optimize business strategies.
Project Usage Areas
This project has several uses for e-commerce businesses:
Analysis of Sales Trends: Determining seasonal trends and reasons for sales increase/decrease by examining the changes in sales within certain periods.
Product Category Analysis: Analyzing best-selling product categories and profitability in these categories.
Customer Behavior: Understanding the shopping behavior of different customer groups by performing customer segmentation.
Marketing Strategies: Developing effective marketing strategies and creating customer loyalty programs using sales data.
Stock Management: Determining which products are in demand and when to manage stocks effectively.
Profit Margin Analysis: Optimizing pricing strategies and reducing costs by analyzing profit margins.
Dataset Description
The data set to be used in this project includes sales data of an e-commerce company. The data set consists of 10,000 rows and various columns in total.
The columns in the data set are:
Orders
OrderID: Order ID
CustomerID: Customer ID
OrderDate: Order date
ShipDate: Shipment date
ShipMode: Shipping mode
Products
ProductID: Product ID
ProductCategory: Product category
ProductName: Product name
Sales
OrderID: Order ID
ProductID: Product ID
Sales: Sales amount
Quantity: Sales quantity
Discount: Discount rate
Profit: Profit
Customers
CustomerID: Customer ID
CustomerName: Customer name
CustomerSegment: Customer segment
Region: Region
city: city
State: State
Country: Country
There are various dirty data problems in this dataset, such as missing data, outlier data, and wrong data type. This is an ideal data set to experience data cleaning and processing processes commonly encountered in real life.
Student Benefits
This project provides many benefits for students:
Data Manipulation: Students develop skills in examining, cleaning, and analyzing data sets.
Using Pandas: They learn to use the data processing and analysis methods of the Pandas library effectively.
Data Cleaning: They gain skills in cleaning missing data, outliers and incorrect data types.
Business Intelligence: By analyzing data sets, they improve their ability to evaluate business performance and make strategic decisions.
Reporting: Provides skills to effectively report and present analysis results.
Real Life Applications: Provides practical information about data problems and analysis processes encountered in real life.
General Analysis Questions:
1. Orders and Sales:
- What is the total sales amount and total profit?
- Which products and categories generate the highest sales and profit?
- What is the historical distribution of orders?
- What is the distribution of orders according to shipping modes?
2. Customers and Orders:
- Who are the customers who place the most orders?
- What is the order distribution according to customer segments?
- How is the order distribution regionally?
3. Products and Sales:
-What are the best selling products?
- What is the sales distribution according to product categories?
- What is the impact of discounts on sales?
4. Outliers and Missing Data:
- In which products and in which orders are outliers observed?
- In which columns and in what proportion is the missing data?
- How can records containing missing and incorrect data types be corrected?
Specific Project Questions:
1. Time Series Analysis:
- What are the historical trends of orders?
- How do sales volumes and profits change over time?
- What are the sales booms during certain periods (e.g. holiday season)?
2. Customer Behavior Analysis:
- What are the purchasing behaviors according to customer segments?
- Who are the most loyal customers and which products do they prefer?
- How do regional customer behaviors differ?
3. Shipping Mode and Delivery Performance:
- What are the effects of different shipping modes on delivery times and customer satisfaction?
- How can cost analysis be done according to shipping modes?
4. Product Performance Analysis:
- Which product categories have the highest sales and profit rates?
- In which categories are there opportunities for new product suggestions?
- What are the effects of outliers on product performance?
5. Discount and Promotion Analysis:
- What is the impact of discounts on sales and profit?
- How can the performance of different discount strategies be evaluated?
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