📊 Ecommerce Sales and Returns Analysis

📌 Overview

This Power BI dashboard provides a comprehensive analysis of ecommerce performance, focusing on sales trends, return behavior, customer segmentation, and payment method impact. The goal is to help businesses make data-driven decisions regarding marketing, inventory, and customer retention strategies.

🧩 Dataset Information

• Size: 50,000+ rows
• Fields: Order Date, Product Category, Customer ID, Payment Method, Quantity, Unit Price, Total Amount, Returned, Region

✅ Key Business Questions Answered

1. Which product categories generate the most revenue?

→ Clothing and Sports are the top-performing categories, each generating over 5.3M in revenue.
📌 Recommendation: Focus marketing campaigns and stock management efforts on these two categories. Consider expanding the product line within these segments to maximize ROI.

2. How does revenue fluctuate across months?

→ A clear seasonal trend is observed, with revenue peaking in summer and declining during early-year months.
📌 Recommendation: Launch targeted promotions during low-sales months and adjust operational planning (budget, workforce, logistics) based on expected demand cycles.

3. Which region has the highest return rate?

→ The West region shows a significantly higher return rate at 25.75%.
📌 Recommendation: Conduct deeper analysis in that region to identify underlying issues (e.g. delivery time, product quality, miscommunication). Consider deploying surveys or customer service reviews.

4. What are the most used payment methods?

→ Bank Transfer is the most used, followed closely by Credit Card and Cash on Delivery.
📌 Recommendation: Keep all popular methods available but consider optimizing the checkout process for the top two to reduce cart abandonment and friction.

5. Is there a relationship between payment method and return rate?

→ Bank Transfer customers have the highest return rate (5.25%), while PayPal customers have the lowest (4.74%).
📌 Recommendation: Monitor purchasing behavior based on payment method and consider stricter validation or follow-up communication for higher-risk methods.

6. Who are the top 10 customers by spend?

→ Customers spending over 12K individually were identified.
📌 Recommendation: Create a loyalty strategy tailored to these high-value customers (e.g., early access to sales, VIP perks), as they represent strong retention and upsell opportunities.

7. What is the average order value and typical customer behavior?

→ The average order value is 628.61, with consistent behavior across customer segments.
📌 Recommendation: Use this metric as a benchmark in future marketing efforts. Promote bundles or discounts that encourage customers to raise their AOV above this threshold.

🛠️ Tools Used

• Power BI: Data modeling, interactive visuals, DAX measures, dashboard design
• SQL: Data exploration, calculated fields, and preprocessing
• GitHub: Version control and project organization
• Custom Branding: Personalized logo and dashboard layout for professional presentation

📷 Dashboard Preview

🚀 How to Use

1. Clone or download this repository
2. Open the .pbix file in Power BI Desktop
3. Use interactive slicers to filter the data by Month, Year, Payment Method, and Product Category
4. Hover over visuals to explore additional tooltips and insights
5. Review summary KPIs and visual breakdowns to answer key business questions