E-commerce Sales Analysis

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Objective

To identify trends and patterns in sales and KPIs and customer behavior for various products and regions of an E-commerce website.

Dataset Summary

The provided data includes information about orders made in different months and states. It highlights product categories, payment modes, order statuses, and corresponding order counts. This dataset provides insights into purchasing trends and customer behavior for various products and regions.

Process

The process included analyzing the data in Excel. Multiple pivots (pivot charts) were created using which a dashboard was built. further to this, to analyze data at various cuts, multiple slicers were put in place. Further to this, another dashboard reflecting KPIs was built without help of pivot tables but in a fully dynamic way using SUMIFS formula.

Key Insights

• September and october were highest order contributing months due to festive season (~21%).
• Women Kurtis and Kurta Sets is the biggest category(12% contri) followed by Women Western Wear(10%) and Men's Fashion(9%).
• ~90% orders on the platform have COD as the payment mode, only 10% is prepaid. Also Perpaid share rises during the festive season.
• Uttar Pradesh is the highest grossing state followed by Maharashtra and then Karnataka ( 11.5%, 9%, 7.8% respectively).
• Above metrics for Prepaid orders were always better than that of COD orders.
  • Metrics % : Prepaid vs COD
  • Delivered % : 58% vs 55%
  • RTO % : 2% vs 15 %
  • Cancellation% : 20% vs 19%.
  • Return% : 18% vs 8.8% ( Opposite to expected as prepaid orders are given more preference in return than COD).