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EDA on E-Commerce Dataset

I took a raw e-commerce dataset and worked through it in Excel and Power BI, digging beyond the obvious numbers to understand what was really happening underneath.

 

I looked at sales, customer behaviour, product performance, payments, and operational timelines to uncover where the business was doing well and where it was quietly losing time or opportunity.

Explore

Project Scope

This Project consists of 2 Dashboards, they are:

  • Business Overview: A quick look at sales, orders, categories, and payment methods.

  • Customer & Operational Insights: A closer look at where orders come from, shipping costs, and how quickly they get approved.

Business Questions

  • Category Performance: Which categories are actually bringing in the sales and orders?

  • Payment Behavior: What are customers paying with, and is the business relying too much on one method?

  • Geographic Demand: Where are most of the orders coming from, and how does shipping cost vary?

  • Approval Speed: A 99.98% approval rate sounds great, but how long are customers actually waiting?

  • Sales Trend: What happened to sales month by month across 2017 and 2018?

Tools Used

  • Advance Excel

  • Power BI

  • Power Queries

Dashboard_1_Business_Overview.png
Business Overview and Sales Performance

This dashboard gives a quick summary of what's going on in the business.
Toys leads by far, $9.8M in sales and 28.7K orders, Sales grew through 2017, peaked in December, then got choppier in 2018 and lastly, Credit cards dominate payments at 73.35%.

Dashboard_2_Customer_Operational_Insights.png
Customer and Operational Insights

A look at where customers are and how smoothly orders move. São Paulo drives nearly half of all orders and the highest shipping costs. The standout find is approval time, a near perfect 99.98% approval rate, yet 20.1K orders took 12 to 24 hours to clear. 

What This Means for Your Business

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Key Insights

  • São Paulo leads: 16.2K orders, 42% of total. Top 3 states make up 67%.

  • Shipping follows volume: São Paulo accounts for $0.70M, 41% of total.

  • Approval takes time: 99.98% approved, but average approval time is 10.5 hours.

Conclusion

The numbers looked good at first, but digging a little deeper told a different story. The 99.98% approval rate is impressive, but waiting 10.5 hours changes how you look at it. The analysis also showed how much the business relies on a few states and how quickly sales can move from one period to another.

Future Scope

The next step is to explore customer value, sales forecasting, profitability, and delivery performance, while making the dashboard more interactive and live.

GitHub Link

Please click on the link for the detailed project on GitHub:
 

https://github.com/Rinitj/E-commerce-Dataset---Exploratory-Data-Analysis-Power-BI-Dashboard

Rinit Jain

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