Ecommerce Business

This project focuses on analyzing large-scale e-commerce datasets to uncover valuable insights for business decision-making. The analysis includes data cleaning and preprocessing, exploratory data analysis, and visualization of key metrics. By identifying trends and patterns in customer behavior and sales data, the project aims to provide strategic recommendations for improving business performance and enhancing the overall customer experience.

Key aspects of the project include:

• Data Cleaning and Preprocessing: Ensure data quality and consistency by handling missing values, duplicates, and formatting issues.

• Exploratory Data Analysis: Analyze sales performance, customer behavior, and product trends to identify patterns and correlations.

• Data Visualization: Create visualizations, such as charts, graphs, and dashboards, to present key findings and insights in an easily understandable format.

• Insights and Recommendations: Provide actionable insights and recommendations based on the analysis to optimize marketing strategies, inventory management, and customer engagement.

The project aims to demonstrate the use of data analysis and visualization techniques to extract meaningful insights from complex datasets and drive data-driven decision-making in e-commerce businesses.

The objective of this project is to analyze the e-commerce datasets of an international online retailer to gain insights into sales performance, customer behavior, and product trends. By leveraging data analysis and visualization techniques, the goal is to provide actionable insights to optimize marketing strategies, inventory management, and customer engagement, ultimately driving revenue growth and business success.

1. Python: Used for data cleaning, preprocessing. Libraries such as pandas.

2. Looker Studio: Used for data visualization, creating interactive dashboards, and presenting key insights to stakeholders.

3. Jupyter Notebook: Used for developing and sharing code, as well as documenting the analysis process.

4. Git and GitHub: Used for version control and collaboration, allowing for tracking changes and sharing the project with others.

1. Total Revenue: The project analyzed the total revenue generated by the e-commerce business, which amounted to $9.73 million.

2. Total Quantity Sold: A total of 5.16 million units of products were sold during the analyzed period.

3. Total Customer Count: The project identified a total of 4,373 unique customers who made purchases.

4. Top 5 Customers by Revenue & Quantity: The project identified the top 5 customers based on their contribution to revenue and quantity sold.

5. Total Quantity, Avg. Unit Price, and Revenue by Stock Code: The project analyzed the performance of different stock codes based on quantity sold, average unit price, and total revenue generated.

6. Total Sales Revenue/Quantity by Country: The project analyzed sales revenue and quantity sold for each country to identify key markets.

7. Variation of Total Quantity and Revenue over Months: The project analyzed the variation in total quantity sold and revenue generated over different months to identify trends and seasonality in sales.

In conclusion, this project has provided valuable insights into the sales performance, customer behavior, and product trends of an international e-commerce company. Through rigorous data cleaning, analysis, and visualization, we were able to identify key metrics and patterns that can inform strategic decision-making.

The project highlighted the importance of data-driven analysis in optimizing marketing strategies, inventory management, and customer engagement. By leveraging tools such as Python for data cleaning and Looker Studio for visualization, we were able to extract meaningful insights and provide actionable recommendations for business growth.

Overall, this project demonstrates the power of data analysis in driving business success and underscores the value of continuous improvement and optimization based on data-driven insights.