A Sales Performance Analytics & Intelligence Framework designed to provide advanced insights on sales performance, profitability, product mix and territory effectiveness.
This project demonstrates how operational sales data can be transformed into a structured analytical layer using SQL and Business Intelligence tools.
Many organizations use CRM systems primarily for operational tracking (activities, contacts, deals).
However, advanced analytics such as profitability analysis, KPI standardization and territory performance evaluation are often missing.
The SalesOps Intelligence Framework acts as an analytical layer above CRM-like operational data, enabling:
- Sales Performance Monitoring
- Profitability Analysis
- Territory Performance Evaluation
- Product Mix Analysis
- Target vs Actual Performance Tracking
The goal of this project is to demonstrate end-to-end analytical system design including:
- Data Modeling
- SQL KPI Logic
- Analytical Views
- Business Documentation
- Executive BI Dashboards
The project follows a layered analytical architecture:
Raw Data (CSV)
↓
PostgreSQL Data Warehouse
↓
SQL KPI Views
↓
Power BI Dashboard
Architecture documentation can be found in:
0_Docs/Architecture_Overview_SIF.md
The dataset represents a B2B industrial sales environment including:
- Customers
- Products
- Salespersons
- Territories
- Sales Transactions
- Sales Targets
- Costs
Core tables:
| Table | Description |
|---|---|
| customers | Customer master data |
| products | Product catalog |
| salespersons | Sales team |
| territories | Geographic sales regions |
| sales_transactions | Sales transactions |
| targets | Sales targets per salesperson |
| costs | Product cost data |
Entity Relationship Diagram:
0_Docs/ERD_SIF.png
The analytical logic of the system is implemented in SQL.
Main components:
2_SQL/01_schema_ddl.sql
Creates the database schema.
2_SQL/02_load_data.sql
Loads CSV data into the database.
2_SQL/03_kpi_views.sql
Implements core business metrics including:
- Revenue
- Gross Margin
- Gross Margin %
- Sales Contribution
- Salesperson Performance
2_SQL/04_quality_checks.sql
Ensures dataset consistency and integrity.
2_SQL/05_demo_queries.sql
Example analytical queries.
The analytical results are visualized through a Power BI dashboard.
Location:
3_PowerBI/SIF_Report.pbix
Dashboard features include:
- Total Revenue
- Total Gross Margin
- Gross Margin %
- Sales Contribution by Salesperson
Includes drill-through analysis with:
- Revenue Trend
- Product Category Breakdown
- Individual Sales Performance
The project includes structured documentation similar to real enterprise analytics projects.
Available documents:
| Document | Description |
|---|---|
| BRD | Business Requirements Document |
| FRS | Functional Requirements Specification |
| Data Dictionary | Dataset field definitions |
| KPI Dictionary | Definitions of all KPIs |
| Architecture Overview | System design |
Location:
0_Docs/
- PostgreSQL
- SQL
- Docker
- Power BI
- CSV datasets
- Start PostgreSQL with Docker
docker-compose up -d
- Run the SQL scripts in order:
2_SQL/01_schema_ddl.sql
2_SQL/02_load_data.sql
2_SQL/03_kpi_views.sql
- Open the Power BI report:
3_PowerBI/SIF_Report.pbix
SalesOps-Intelligence-Framework-SIF
0_Docs
1_Data
2_SQL
3_PowerBI
docker-compose.yml
README.md
Panagiotis Zois
MSc Information & Communication Technology | Bridging Business & Technology
This project demonstrates Sales Analytics, Data Modeling and Business Intelligence system design.