To conduct a comprehensive analysis of an E-commerce company sales, and product trends using various libraries in Python to identify key insights and opportunities for business optimization.
Tech Stack: Python | Pandas | Plotly | Jupyter.
Key Insights: Monthly sales and profit based on various factors, best-selling products and product category, Sales to profit ratio.
- Calculate the monthly sales of the store and identify which month had the highest sales and which month had the lowest sales.
- Analyze sales based on product categories and determine which category has the lowest sales and which category has the highest sales.
- Sales analysis need to be done based on sub categories.
- Analyze the monthly profit from sales and determine which month had the highest profit.
- Analyze the profit by category and sub-category.
- Analyze the sales and profit by customer segment.
- Analyze the sales to profit ratio.
Since this code uses plotly, please open the jupyter notebook in nbviewer.
- Verify data for any null values and anomalies
- Make sure data is consistent and clean with respect to data type, data format and values used.
- Create suitable charts with the help of pandas and plotly in jupyter notebook.
- Merge all conclusion and give overall result.
- November recorded the highest sales, while February had the lowest.
- The Technology category emerged as the best-selling segment
- Phones and Chairs were the top-selling products.
- December saw the highest profit, whereas January had the lowest
- The Technology category contributed the most to overall profit.
- Copiers were the most profitable sub-category, while Tables and Bookcases incurred losses.
- The Consumer segment generated the highest sales and profit.