Exploratory Data Analysis on Retail Sales Data

 Exploratory Data Analysis (EDA) on Retail Sales Data  

📅 Date: 22/02/2025 📝 Author: Akieni Wright 📊 Project Type: Data Analysis & Business Intelligence 🔍 Tools Used: MySQL & Power BI  

 Executive Summary 

This project analyzes retail sales data to identify sales trends, customer behavior, and profitability across various regions and product categories. Using Python (Pandas, Matplotlib, Seaborn, NumPy, and SciPy), the analysis provides actionable insights to optimize sales strategies and improve profit margins.   

  

📌 Key Questions Answered  

  

1. Which product categories generate the most revenue?   

The analysis showed that furniture had the largest revenue  

 

2.  Which regions contribute the highest sales volume?   

The north was shown the have the highest sales volume  

 

3.  How do discounts impact profitability?  

The analysis shows that discounts affected electronics the most then furniture and showing that it had little to no effects on clothing.   

4.  Are there any seasonal trends in sales performance?  

Our analysis showed that moving from July into August we experienced a decrease in total sales.  

5.  What is the relationship between sales volume and profitability?   

The analysis shows that there is a linear progression indicating the more sales the higher the profitability.   

6.  Are there any outliers in sales, discount, or profit values?   

There were zero outliers in this analysis  

 

7.  Do specific product categories have consistently high or low-profit margins?   

               

Category   Avg Profit    Clothing   25.21 

Furniture    21.51 

Electronics       20.55 

                                   

8.  What is the distribution of sales across different regions? The sales numbers are as follows:    

          

Region     Total Sales 

North  2300 

East    2040  

West   1700 

South    1320 

                                                                   

Technologies Used  

Python (Pandas, NumPy) for data cleaning and manipulation   

Matplotlib, Seaborn for data visualization   

 

Conclusion  

Top Revenue and Profit Drivers: Furniture emerged as the highest revenue-generating product category, while clothing consistently yielded the highest profit margins. The correlation between sales volume and profitability is strong, indicating that higher sales positively impact profitability.  

Regional Performance: The North contributed the highest sales volume, highlighting its significance as a key market. However, disparities exist across regions, with the South showing the lowest sales performance.  

Impact of Discounts and Trends: Discounts significantly affected profitability, particularly for electronics and furniture, while clothing showed resilience. Seasonal trends revealed a dip in sales between July and August, suggesting a potential demand fluctuation during this period.  

  

Recommendations  

Focus on High Revenue and Profit Drivers: Prioritize marketing and stock availability for furniture to maintain revenue leadership. Additionally, leverage clothing's high profit margins by exploring premium pricing strategies or bundling.  

Regional Sales Optimization: Develop targeted strategies to boost sales in the South and capitalize on the North’s strong market presence by introducing region-specific promotions or campaigns.  

Manage Discounts Strategically: Reassess discount strategies, particularly for electronics and furniture, to minimize profit erosion. Consider implementing targeted discount offers for underperforming products or regions.  

Address Seasonal Trends: Investigate the reasons behind the sales decline in late summer and introduce strategies like promotions or limited time offers to stabilize sales during these periods.