This repository showcases a comprehensive data analyis on the AmazingMartEU Geographical Sales data. The dataset can be found on SuperDataScience web page. My intial plan was to create an iteractive dashboard which can be found here dashboard but I also wanted to use python and do some analysis and visualisations.
This project is aimed at understanding the different aspects of shipping such as order processing times,shipment delays and customer preferences based on various metrics.At the end of the project,we should be able to get insights that can aid in improving operational efficiency and customer service.
Key areas of analysis include:
Order Processing Times: Identify trends in order processing durations.
Sales and Profit: Identify different areas of customer segments that generate income.
Shipping Delays: Examine reasons for shipment delays and patterns.
Customer Segmentation: Analyze customer data for segmentation based on location, product preferences, etc.
Geographical Analysis: Assess shipping metrics across different locations.
- The analysis provided insights and highlighted the importance of premium products for revenue, despite lower order volumes compared to more frequently ordered office supplies suggesting consumer-driven demand, technology product popularity and regional performance differences are critical factors in this shipment business.
- Moreover,the business might consider strategies such as targeted marketing for high-demand regions, balancing discount levels to maintain profits and promoting high-value products through faster shipping options for premium customers.
- Additionally, understanding customer preferences for small order quantities and economy shipping could help to enhance customer satisfaction and loyalty.
Python: Data processing and analysis
Pandas: Data manipulation
Matplotlib, Plotyl & Seaborn: Data visualization