A data analytics project that analyzes sales performance, evaluates incentive compensation models, and visualizes business insights through an interactive Power BI dashboard.
This project simulates a retail company's sales analytics workflow. It demonstrates how sales data can be processed using Python and SQL, analyzed to extract insights, and visualized through an executive dashboard.
The system evaluates:
- Regional sales performance
- Product category trends
- Sales representative performance
- Incentive compensation effectiveness
- Monthly revenue forecasting
The goal is to simulate a real-world analytics pipeline used by data analysts and business intelligence teams.
- Python
- Pandas
- NumPy
- SQL (SQLite)
- Power BI
- Matplotlib
- Scikit-learn
sales_performance_analytics
│
├─ dashboard
│ └─ sales_dashboard.pbix
│
├─ data
│ ├─ raw
│ ├─ processed
│ └─ sales.db
│
├─ notebooks
│ ├─ data_generation.ipynb
│ └─ sql_analysis.ipynb
│
├─ pipeline
│ └─ build_dataset.py
│
├─ reports
│ ├─ dashboard.png
│ ├─ sales_forecast.png
│ └─ business_insights.md
│
├─ sql
│ └─ analytics_queries.sql
│
├─ requirements.txt
├─ README.md
└─ LICENSE
Synthetic Data Generation
↓
Data Processing (Pandas)
↓
SQL Database Creation (SQLite)
↓
SQL Analytics Queries
↓
Business Metrics & KPI Analysis
↓
Power BI Dashboard Visualization
↓
Sales Forecasting Model
Analyzed sales data to identify revenue distribution across regions, product categories, and sales representatives.
Implemented a tiered incentive structure based on target achievement:
- <70% target → reduced incentive
- 70–100% target → gradually increasing commission
-
100% target → maximum incentive rate
Created SQL queries to compute core business metrics:
- Revenue by region
- Revenue by product category
- Monthly revenue trend
- Top performing sales representatives
- Incentive payout by region
All queries are available in:
sql/analytics_queries.sql
Built a Power BI dashboard containing:
- Total revenue KPI
- Total incentive payout KPI
- Revenue by region
- Revenue by product category
- Monthly sales trend
- Filters for region, category, and month
Dashboard file:
dashboard/sales_dashboard.pbix
Preview:
A simple regression model was implemented to predict future monthly revenue.
Steps:
- Aggregate monthly sales
- Train a linear regression model
- Predict the next month's revenue
- Visualize forecast results
Forecast visualization:
- Total simulated revenue ≈ 125M
- Total incentive payout ≈ 8.37M
- East region generated the highest revenue
- Clothing category contributed the largest share of sales
- Several sales representatives exceeded 100% target achievement
- Revenue trend shows relatively stable monthly sales with occasional peaks
This project demonstrates practical skills used in analytics roles:
- Data cleaning and transformation
- SQL analytics queries
- KPI design and incentive modeling
- Dashboard development
- Business insight generation
- Basic forecasting models
A sample dataset is provided for demonstration:
data/sample_sales_data.csv
The full dataset can be generated using the notebook:
notebooks/data_generation.ipynb
Clone the repository:
git clone https://github.com/yourusername/sales-performance-analytics
Install dependencies:
pip install -r requirements.txt
The project simulates a production-style analytics workflow.
Pipeline steps:
- Generate synthetic sales data
- Process and clean the dataset
- Load data into a SQLite database
- Perform SQL analysis
- Visualize insights using Power BI
Pipeline script:
pipeline/build_dataset.py
Run the data pipeline:
python pipeline/build_dataset.py
This project is licensed under the GNU General Public License v3.0 (GPLv3).
See the LICENSE file for details.
Ahan
Data Analytics / Business Intelligence Project