My first project during the Data Analytics Internship at Future Interns, Documenting my learning journey.
Project Discription : Analyze e-commerce data to identify best-selling products, sales trends, and high-revenue categories using Power BI. and built an interactive dashboard with visulas and insights for business decisions.
Skills Gained: Data cleaning, DAX, trend analysis, business storytelling. Data Source : I have been using the kaggle dataset and downloaded in zip file. and after that i have exact the file and then import in excel and used for data cleaning and removing duplicates, after that i have been import that file into the Power BI.
Questions from this project: What are the best-selling products? When do sales peak during the year? Which categories or regions bring the most revenue?
Charts and KPIs : Key KPIs : Sales Revenue, Profit, Discounts, Quantity Top Product Analysis Monthly Sales Trend Regional Sales Category Analysis City Wise Breakdown
Insights and Recommendations : West region & Technology category are our strengths → expand further. Central region & Office Supplies are weak → focus on promotions and reduce discounts.
The sql used to analyze to E-Commerce sales dataset, which supports the insights shown in the power bi dashboard
Who are our top customers by sales, profit, and retention?
How do discounts impact profit (does high discount = low profit)?
Which customers received the highest discounts, and did they generate profit or loss?
How does Average Order Value (AOV) vary by region?
What is the Year-over-Year (YoY) growth (2011 → 2012)?
What are the monthly and quarterly sales trends (seasonal patterns)?
What is the return rate of products, and are high-discount products more likely to be returned?
1 Customer insight Top 10 customers by order quantity
Customers who contributed the highest profit
Retention: customers who purchased across multiple years
2 Discount & Profitability
Effect of discount % on profit (e.g., high discount = low profit?)
Customers with highest discounts and their profit/loss impact
3 Regional Analysis
Compare Average Order Value (AOV) by region
Identify regions with high sales but low profit
4 Time Trends
Year-over-Year sales growth (2011 vs 2012)
Monthly/Quarterly sales to identify seasonal patterns
5 Returns Analysis
% of orders returned overall
Categories with the highest return rate
Are high-discount products returned more often?