An end-to-end retail sales analytics project using Python, MySQL, Microsoft Excel, and Power BI to analyze sales performance, customer behaviour, regional trends, and product performance for a fictional retail company, ShopEase Nigeria.
This project analyzes 80,000 retail transactions from ShopEase Nigeria, a fictional mid-sized retail company operating across eight major cities in Nigeria.
The objective was to uncover sales trends, evaluate regional and product performance, understand customer purchasing behaviour, and provide actionable recommendations through data analysis and interactive dashboards.
The project demonstrates an end-to-end analytics workflow—from data cleaning and exploratory analysis to SQL querying and dashboard development.
Although ShopEase Nigeria has experienced consistent revenue growth, management identified that some regions and product categories continue to underperform.
As a Data Analyst, I was tasked with identifying performance gaps, uncovering sales patterns, and recommending strategies to improve profitability and operational efficiency.
This analysis answers the following questions:
- Which regions generate the highest revenue?
- Which product categories contribute the most revenue?
- How does revenue change over time?
- Who are the highest-value customers?
- Which products generate the most revenue?
- Which payment methods are most preferred?
- Which categories experience the highest return rates?
- Does delivery time influence returns?
- Which salespersons contribute the most revenue?
- What actions can improve business performance?
| Attribute | Description |
|---|---|
| Dataset | retail_sales.csv |
| Records | 80,000 |
| Period | 2021–2023 |
| Industry | Retail / E-Commerce |
| Company | ShopEase Nigeria (Fictional) |
| Tool | Purpose |
|---|---|
| Python (Pandas, Matplotlib) | Data Cleaning & Exploratory Data Analysis |
| MySQL | Business KPI Queries |
| Microsoft Excel | Pivot Tables & Dashboard |
| Power BI | Interactive Dashboard |
| Git & GitHub | Version Control & Documentation |
- Data Collection
- Data Cleaning
- Exploratory Data Analysis (Python)
- SQL Business Analysis
- Excel Dashboard Development
- Power BI Dashboard Development
- Business Insights
- Recommendations
- Total Revenue: ₦15.99 Billion
- Total Orders: 80,000
- Average Order Value: ₦199.85K
- Return Rate: 8%
This analysis uncovered the following insights:
- Lagos generated the highest revenue, contributing X% of total sales.
- Electronics was the best-performing category, accounting for X% of total revenue.
- Revenue peaked in Month and was lowest in Month, indicating seasonal demand.
- Customer Name generated the highest lifetime revenue.
- Product Name was the highest-revenue product across all categories.
- Payment Method was the most preferred payment option, representing X% of all transactions.
- Category recorded the highest return rate, exceeding the overall average of X%.
- Orders with longer delivery times experienced significantly higher return rates.
- Salesperson Name generated the highest sales revenue and order volume.
- The business recorded ₦X in revenue from X orders with an average order value of ₦X.
Below are the recommendations made from the analysis:
- Increase inventory and marketing investment in the Electronics category to maximize revenue.
- Replicate high-performing regional sales strategies in lower-performing markets.
- Launch targeted promotions during low-demand months to stabilize seasonal sales.
- Strengthen loyalty programs for high-value customers to improve customer retention.
- Prioritize stocking and promoting top-performing products while reviewing underperformers.
- Optimize the most-used payment channels to improve checkout experience.
- Investigate the root causes of returns in high-return categories and improve quality control.
- Reduce delivery lead times through logistics optimization to minimize product returns.
- Reward top-performing salespersons and provide coaching for lower performers.
- Leverage sales forecasting to optimize inventory, reduce stockouts, and sustain revenue growth.
retail-sales-performance-analysis/
│
├── data/
│ ├── raw/
│ └── cleaned/
├── sql/
├── python/
├── excel-dashboard/
├── powerbi-dashboard/
├── visuals/
├── reports/
└── README.md
- Explore the raw dataset in
data/raw/. - Review the cleaned dataset in
data/cleaned/. - Run the SQL queries in the
sql/folder using MySQL. - Open the Jupyter Notebook in the
python/folder. - Explore the Excel and Power BI dashboards.
Blessing Usieme
Data Analyst - Business Intelligence | Data Visualization and Storytelling