Amazon Sales Data Analysis Dashboard

Project Objective

I developed an interactive dashboard that helps visualize key sales metrics and optimize business strategies.

Dataset used

-Dataset

Key Features

• Sales Performance Analysis: Tracks total revenue, profit margins, and sales trends over time.
• Customer Segmentation: Uses RFM analysis to classify customers based on purchasing behavior.
• Product Insights: Identifies best-selling products, revenue contributors, and underperforming items.
• Geographical Analysis: Maps sales distribution across different regions.

Technologies Used

• SQL: Data extraction and transformation.
• Python (Pandas, NumPy, Matplotlib, Seaborn): Data cleaning, analysis, and predictive modeling.
• Power BI: Data visualization and dashboard creation.
• Excel: Data preprocessing and additional analysis.

Process

• Load the dataset (Amazon sales data in CSV format).
• Verify data for any missing values and anomalies, and sort out the same.
• Run the Python scripts to clean and process the data.
• Open the Power BI dashboard to explore the insights

Dashboard Image

Project Insight

• Revenue Distribution: Consumers contributed to 51.48% of total sales, highlighting the importance of Consumers customers.
• Sales Trends: Sales seem to be Increase every year.
• Product Performance: Identified best-selling products and underperforming items.
• Geographical Insights: Sales were concentrated in certain regions, guiding localized marketing and expansion plans.
• Asia, Europe are the 55% of market capture.

Final Conclusion

• We identified key revenue drivers, customer segments, and sales patterns. These insights can help businesses optimize pricing, inventory, and marketing strategies to maximize revenue and customer retention.