This project focuses on analyzing retail sales data from a Superstore to understand business performance . The goal is to identify profit drivers, loss-making areas, and the trends using data analysis.
The analysis follows an end-to-end data analytics workflow — starting from raw, unclean data and progressing through cleaning, exploration, insight generation, and business recommendations.
Despite strong overall sales, the Superstore experiences inconsistent profitability across product categories, customer segments, and regions. High discounts, operational costs, and loss-making sub-categories may be negatively impacting business performance.
The objective of this analysis is to:
- Identify areas where high sales do not translate into profit
- Understand the impact of discounts,customer segments on profitability
- Time-series trend analysis
- Provide data-driven recommendations to improve overall business performance
The dataset contains order-level sales data with information on:
- Orders and shipping details
- Customer segments
- Product categories and sub-categories
- Sales, discounts, and profit
- Regional performance
Two datasets are used:
- Raw Dataset: Contains inconsistent formats, and unprocessed columns
- Cleaned Dataset: Processed for analysis by handling correcting data types, and standardizing fields
- Python
- pandas & NumPy for data manipulation
- Matplotlib & Seaborn for data visualization
- Google Colab for analysis and documentation
- Data Cleaning & Preparation
- Exploratory Data Analysis (EDA)
- Sales vs Profit Analysis
- Discount Impact Analysis
- Category & Sub-Category Performance
- Customer Segment Analysis
- Regional Performance Analysis
- Shipping Mode Profitability
- Time-series trend analysis
- Orders with discounts greater than 20% resulted in a total loss of ~$17,400, indicating excessive discounting significantly impacts profitability.
- The top 5 loss-making products account for more than 50% of the total losses, meaning a small number of items cause most of the damage.
- The overall profit margin across all orders is ~10%, which is relatively low compared to total sales volume.
- Discounts above 20% leads to negative average profit, indicating aggressive discounting hurts profitability.
- The Furniture category generates high sales but contributes heavily to overall losses, especially from specific sub-categories.
- Cap discounts at 15–20%, as higher discounts consistently lead to losses.
- Review or discontinue the top loss-making products, which drive nearly one-third of total losses.
- Focus retention and upselling efforts on high-profit customers instead of volume-based discounting.
- Track profit margin alongside sales as a core performance metric.
- Use monthly sales trends and moving averages for short-term demand planning.