Global Superstore Sales Profitability Analysis.
Data-driven insights into sales, profitability, and shipping dynamics to enhance customer satisfaction and operational efficiency.
Problem Statement
The retail dataset spanned multiple regions, product categories, and customer segments, but lacked a consolidated framework for analyzing sales performance and profitability. Without deeper insights into revenue drivers and cost inefficiencies, management faced difficulties in optimizing strategy and identifying high-value opportunities.
Technical Solution
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Step 1 – Architecture & Approach:
- Built a hybrid analysis workflow by integrating SQL for structured queries, Pandas/Numpy for transformations, and Power BI for visualization.
- The data pipeline extracted raw sales and shipping data, cleaned it, and organized it into analytical models.
- Enabled profitability and growth tracking through structured and processed datasets.
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Step 2 – Key Algorithms, Libraries & Highlights:
- SQL: Queries for sales by region, profit margins by product category, and shipping cost analysis.
- Python: Pandas for ETL, NumPy for numerical computations.
- Power BI: Interactive dashboards to visualize trends across regions, categories, and customer groups.
- Highlights: Segmented analysis by product category, subcategory, and geography to identify profitability drivers and inefficiencies.
Results & Impact
Enabled executives to design targeted campaigns by region and segment, improving ROI.
Technologies Used
Python (Pandas, NumPy)
Power BI (interactive dashboards)
SQL
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