This project analyzes historical vehicle sales data from Bright Motors to provide business insights that support strategic decision-making. The analysis focuses on identifying top-performing vehicle brands and models, understanding customer purchasing trends, evaluating regional sales performance, and improving dealership profitability.
The project was completed as part of a Data Analytics Case Study and demonstrates the end-to-end data analytics process, including data cleaning, SQL analysis, dashboard development, and business reporting.
A new Head of Sales has joined Bright Motors with the goal of:
- Expanding dealership performance
- Increasing revenue
- Optimizing inventory management
- Understanding customer purchasing behavior
- Improving profitability
The purpose of this analysis is to provide data-driven recommendations that support these objectives.
| Tool | Purpose |
|---|---|
| SQL | Data Cleaning & Analysis |
| Microsoft Excel | Data Validation & Analysis |
| Power BI | Dashboard Development |
| Miro | Architecture Diagram |
| Canva / PowerPoint | Presentation Development |
| GitHub | Project Documentation & Portfolio |
- Imported Bright Motors car sales dataset.
- Converted Excel data into CSV format for processing.
- Removed duplicates.
- Handled missing values.
- Converted price fields to numeric format.
- Standardized column names.
Created new calculated fields:
Total Revenue
Performance Tier
- High Margin
- Medium Margin
- Low Margin
Performed SQL analysis to identify:
- Revenue by vehicle make and model
- Regional sales performance
- Fuel type distribution
- Average selling price trends
- Profitability metrics
- Customer purchasing patterns
Developed an interactive dashboard with:
- Revenue Analysis
- Sales Trends
- Regional Performance
- Fuel Type Analysis
- Profitability Metrics
Presented findings and recommendations to support strategic sales decisions.
The analysis aimed to answer the following questions:
- Which vehicle makes generate the highest revenue?
- Which models sell the most units?
- Which regions have the strongest sales performance?
- How do fuel preferences vary among customers?
- What pricing trends exist over time?
- Which vehicles deliver the highest profit margins?
The dashboard includes:
- Revenue by Make
- Revenue by Model
- Regional Sales Performance
- Fuel Type Distribution
- Average Selling Price Trends
- Profit Margin Analysis
- Region
- Year
- Fuel Type
Identified the vehicle brands and models contributing the highest revenue.
Highlighted regions with the strongest sales performance and revenue generation.
Analyzed fuel type preferences and purchasing patterns.
Categorized vehicles into High, Medium, and Low Margin groups to support inventory and pricing decisions.
Based on the analysis:
- Increase inventory levels for top-performing vehicle models.
- Focus marketing efforts on high-revenue regions.
- Promote high-margin vehicle categories.
- Monitor pricing trends to maintain competitiveness.
- Use customer purchasing trends to guide future inventory decisions.
- Architecture Diagram
- SQL Analysis Script
- Processed Dataset
- Interactive Dashboard
- Business Presentation
- GitHub Documentation
Rinae Netshilinganedza
Educator | Data Scientist
GitHub: https://github.com/rinaenetshilinganedza-stack
LinkedIn: www.linkedin.com/in/rinae-netshilinganedza-8450561a3
This project demonstrates practical skills in:
- SQL
- Data Cleaning
- Data Transformation
- Data Visualization
- Business Intelligence
- Dashboard Development
- Data Storytelling
- GitHub Project Documentation