SQL + Excel Dashboard | Decision-Driven Analytics
This project simulates a real-world Business Analyst scenario where large-scale e-commerce product data is analyzed to drive strategic business decisions around pricing, discounting, and category performance.
The solution combines SQL-based data transformation and analysis with an Excel dashboard, enabling stakeholders to monitor KPIs, identify inefficiencies, and make data-driven decisions.
In large e-commerce environments, decision-making is often reactive due to:
- Lack of visibility into drivers of customer engagement
- Over-reliance on discounting without understanding impact
- Poor identification of high-growth vs underperforming categories
- Difficulty translating raw data into actionable insights
The core problem is not lack of data, but lack of structured decision-making frameworks built on data.
- Identify key drivers of product performance and engagement
- Analyze relationships between price, discount, and customer perception
- Detect inefficiencies and growth opportunities
- Build a scalable decision-support system using SQL and dashboards
- Cleaned and standardized raw product data using SQL
- Converted pricing, discount, and rating fields into usable formats
- Created derived fields such as price segments, discount tiers, and rating bands
- Average price
- Average discount percentage
- Average rating
- Total reviews as an engagement proxy
- Category-level performance metrics
Products were segmented by:
- Price segment
- Discount tier
- Rating band
- Category
Advanced SQL techniques used:
- Window functions
- Ranking
- Percentile analysis
- Pareto analysis
- Category performance analysis
The analysis focused on:
- Identifying performance patterns
- Understanding root causes
- Translating findings into business implications
Each insight was converted into:
- Business implication
- Recommended action
- Decision-support value
An Excel dashboard was built to:
- Track key KPIs
- Compare categories and segments
- Highlight performance gaps
- Support faster business decisions
A majority of engagement is concentrated in a small number of categories.
Business Risk:
Over-dependence on limited categories can reduce growth diversification.
Recommended Action:
Improve visibility and investment in underperforming categories through better positioning and discoverability.
Customer ratings remain stable across price ranges.
Business Implication:
Customers evaluate products based on value and experience, not price alone.
Recommended Action:
Focus on product quality, review quality, product content, and listing optimization.
Discounts between 50% and 70% drive strong engagement, while discounts above 70% may reduce perceived quality.
Business Implication:
Excessive discounting may create negative customer perception.
Recommended Action:
Optimize discounting within the most effective range and avoid unnecessary deep discounting.
Mid-range products generate the highest engagement.
Business Implication:
This segment balances affordability and perceived quality.
Recommended Action:
Prioritize this segment for product launches, inventory planning, and marketing.
Some categories have strong supply but low engagement.
Business Implication:
Low engagement may be caused by weak discoverability, poor listing quality, or low conversion.
Recommended Action:
Improve listing content, product images, descriptions, and promotional visibility.
This project supports:
- Pricing optimization
- Category strategy improvement
- Customer engagement analysis
- Product positioning decisions
- Leadership visibility through structured reporting
- Data-driven decision-making
The Excel dashboard provides an interactive view of:
- KPI summary
- Category performance
- Price segment performance
- Discount tier analysis
- Rating and engagement trends
- Performance gaps
- SQL Server
- SSMS
- Microsoft Excel
- Power Query
- Pivot Tables
- Dashboard visualizations
- SQL data cleaning
- SQL transformation
- Advanced SQL analysis
- Window functions
- KPI development
- Data segmentation
- Business analysis
- Dashboard development
- Insight generation
- Recommendation building
sql-amazon-sales-analytics/
│
├── scripts/
│ ├── 00_create_db_and_tables.sql
│ ├── 01_data_cleaning.sql
│ ├── 02_kpi_summary.sql
│ ├── 03_category_performance.sql
│ ├── 04_price_segment_analysis.sql
│ ├── 05_advanced_window_functions.sql
│ ├── 06_discount_analysis.sql
│ ├── 07_part_to_whole.sql
│ └── 08_excel_views.sql
│
├── dashboard/
│ └── Excel_Dashboard.xlsx
│
├── docs/
│ └── Amazon_Sales_Analytics_Project_Documentation.docx
│
└── README.md