SanoyFresco Sales Analytics is a data analytics and business intelligence project focused on analyzing retail transactional data to extract meaningful insights about sales performance, customer behavior, and product trends.
The project simulates a real-world data analytics workflow, where raw sales data is transformed into insights through:
- SQL data analysis
- Python-based data processing
- Business Intelligence dashboards using Power BI
The goal is to demonstrate how data can support business decision-making in retail environments.
The main goals of this project are:
- Analyze business revenue trends
- Identify top-performing products
- Understand customer purchasing behavior
- Evaluate department and section performance
- Calculate key sales metrics
- Discover product purchase associations
These insights help businesses:
- optimize store layout
- improve cross-selling strategies
- identify high-value customers
- monitor sales performance
The project includes interactive Power BI dashboards to visualize business metrics and product associations.
This dashboard provides an overview of the overall business performance.
| KPI | Description |
|---|---|
| Ventas Totales | Total revenue generated by the business |
| Clientes Totales | Number of unique customers |
| Ticket Medio por Pedido | Average order value |
| Ticket Medio por Cliente | Average spending per customer |
See the full documentation here:
This dashboard focuses on product association analysis.
Market Basket Analysis identifies products that are frequently purchased together.
| Metric | Description |
|---|---|
| Número de Reglas | Total association rules detected |
| Confianza Media | Average probability that product B is purchased when product A is purchased |
| Lift Medio | Strength of association between products |
The table shows detected product relationships:
| Column | Description |
|---|---|
| Antecedente | Product A |
| Consecuente | Product B |
| Lift | Strength of the association |
The dataset represents transactional sales data from a retail system.
The main table analyzed is:
tickets
It includes fields such as:
| Column | Description |
|---|---|
| id_pedido | Order identifier |
| id_cliente | Customer identifier |
| nombre_producto | Product name |
| id_departamento | Department identifier |
| id_seccion | Section identifier |
| cantidad | Quantity sold |
| precio_total | Total price of the transaction |
| fecha | Transaction date |
This structure allows performing multiple types of analysis including product performance, revenue tracking, and customer segmentation.
This project uses:
- SQL
- SQL Server
- POWER BI
- Git & GitHub
- Data Analysis Techniques
Concepts applied:
- Aggregations (
SUM,AVG,COUNT) - Data grouping (
GROUP BY) - Ranking queries (
TOP) - Subqueries
- Revenue analysis
- Customer metrics
The analysis can generate insights such as:
- Monthly sales trends
- Best-selling products
- Most valuable customers
- Department revenue distribution
- Average order value
These metrics are commonly used in business intelligence dashboards and retail analytics systems.
sanoyfresco-sales-analytics
│
├── dashboard/ # dashboard from power BI
├── datasets/ # Dataset used for the analysis
├── sql/ # SQL scripts with analytical queries
│
├── docs/ # Project documentation
│
├── README.md # Project documentation
└── LICENSE
This project is licensed under the MIT License. You are free to use, modify, and share this project with proper attribution.
Hi! I'm Leandro Gallo, a Systems Engineering student from Argentina with a strong interest in:
- Data Engineering
- Data Analytics
- Backend Development
- Cybersecurity
- Software Development
I enjoy building data pipelines, automation tools, and data-driven systems, and I am currently developing projects to strengthen my skills in data architecture, SQL development, and analytics.
This repository is part of my technical portfolio, where I showcase projects related to:
- Data Warehousing
- ETL Pipelines
- Data Modeling
- Analytics
📧 Email
leandrogallo698@gmail.com