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https://github.com/nafis2508/urban-eats-sql-analysis

SQL business analytics and relational database design project for retail and café operations.
https://github.com/nafis2508/urban-eats-sql-analysis

business-analytics database-design mysql sql sql-analysis

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SQL business analytics and relational database design project for retail and café operations.

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README 

          
# 🍴 SQL Business Analytics & Database Design Project | UrbanEats Café Chain



## 📌 Project Overview



UrbanEats is an end-to-end SQL business analytics and relational database design project built for a multi-branch café chain operating across multiple locations in Sydney, Australia.



This project demonstrates how structured relational databases and SQL-driven analytics can support operational decision-making in a retail hospitality environment.



The project combines:



* Relational database design

* Business-focused SQL analytics

* Operational performance analysis

* Customer behaviour analytics

* Revenue and profitability reporting

* Staff scheduling optimisation

* Branch-level operational efficiency analysis



The analysis simulates real-world café operations and demonstrates how businesses can use SQL and data analytics to improve profitability, customer retention, staffing efficiency, and operational reliability.



---



# 🧰 Tech Stack



* SQL (MySQL)

* MySQL Workbench

* Relational Database Design

* ERD Modelling

* Business Analytics

* Operational Analytics

* Business Intelligence

* Data Modelling

* Database Normalisation

* KPI Analysis



---



# 📂 Repository Structure



```bash

urban-eats-sql-analysis/

│

├── README.md

├── LICENSE

├── .gitignore

│

├── assets/

│   ├── available_products_by_outlet.png

│   ├── failed_payment_and_churn_analysis.png

│   ├── loyal_vs_onetimer_customers.png

│   ├── order_to_revenue_flow_analysis.png

│   ├── product_sales_by_outlet.png

│   ├── reservation_reliability_analysis.png

│   ├── revenue_by_product_category.png

│   ├── role_group_demand_analysis.png

│   ├── shift_utilisation_analysis.png

│   ├── staff_capacity_by_outlet.png

│   ├── total_revenue_by_outlet.png

│   ├── underperforming_categories_analysis.png

│   └── urban_eats_erd.pdf

│

├── diagrams/

│   └── urban_eats_erd.pdf

│

├── reports/

│   ├── urban_eats_report.docx

│   └── urban_eats_report.pdf

│

├── sql/

│   ├── schema/

│   │   └── schema.sql

│   │

│   ├── data_insertion/

│   │   └── data_insertion.sql

│   │

│   └── analysis/

│       └── business_case_analysis.sql

│

└── data/

````



---



# 🗃️ Simulated Operational Dataset



The project uses a synthetic but business realistic dataset simulating day to day café operations across multiple outlets.



The dataset includes:



* 3 café outlets

* 30+ customers

* 30+ menu products

* Staff and shift allocation

* Customer reservations

* Orders and payments

* Product availability by branch

* Revenue and transaction records



The operational data was intentionally designed to simulate:



* Repeat vs one time customers

* Customer churn signals

* Failed and refunded payments

* Reservation no shows

* Staffing inefficiencies

* Branch specific menu strategies

* Operational bottlenecks



---



# 🧠 Database Design & ERD



The relational database schema was designed using proper entity relationships, primary keys, foreign keys, and many to many junction tables.



### Core Entities



* Outlet

* Customer

* Product

* Product_Category

* Orders

* Payments

* Reservation

* Staff

* Shift



### Junction Tables



* Order_Product

* Outlet_Product

* Staff_Shift



The schema supports both transactional processing and business analytics reporting.



---



# 🧩 Entity Relationship Diagram (ERD)





  




---



# 🧠 SQL Concepts Demonstrated



This project demonstrates practical SQL analytics and database engineering concepts including:



* Complex JOIN operations

* Aggregate functions

* CASE statements

* GROUP BY and HAVING clauses

* Revenue calculations

* Customer segmentation

* Operational KPI analysis

* Many to many relationship modelling

* Foreign key constraints

* Relational schema design

* Business rule implementation

* Business focused SQL reporting

* Query optimisation logic



---



# 📈 Key Business Metrics Analysed



The project analyses several operational and commercial KPIs including:



* Revenue by outlet

* Revenue by product category

* Reservation completion rate

* Customer loyalty segmentation

* Failed payment analysis

* Staff utilisation percentage

* Shift efficiency

* Menu item profitability

* Product availability ratio

* Branch operational performance



---



# 📊 Business Concerns & Analytical Insights



---



# 1️⃣ Sales & Profitability Analysis



## Focus



Analyse revenue contribution across outlets and product categories to identify profitability drivers and operational gaps.



## Key Insights



* Urban Eats Central generated the highest overall revenue with balanced sales across meals, beverages, and desserts.

* Harbour performed strongly through its niche strategy focused on cold drinks and desserts.

* Campus significantly underperformed due to high cancellation rates and limited product diversity.

* Espresso based products showed low profitability contribution compared to higher ticket meal categories.



## Revenue by Product Category





  




## Revenue by Outlet





  




---



# 2️⃣ Customer Retention Analysis



## Focus



Evaluate customer loyalty, churn risk, reservation reliability, and payment behaviour.



## Key Insights



* Customer loyalty exists but is concentrated within limited product categories.

* Failed and refunded payments strongly overlap with reservation no shows.

* Harbour achieved the strongest reservation to order conversion rates.

* Campus demonstrated poor customer reliability and retention performance.



## Loyal vs One Time Customers





  




## Failed Payment & Churn Analysis





  




---



# 3️⃣ Menu Optimisation Analysis



## Focus



Identify underperforming menu items and evaluate outlet level sales distribution.



## Key Insights



* Several products generated consistently low sales volume.

* Harbour performed strongly with desserts and cold beverages.

* Central maintained balanced menu performance.

* Campus lacked sufficient menu diversity to maximise customer demand.



## Underperforming Categories





  




## Product Sales by Outlet





  




---



# 4️⃣ Staff Scheduling & Performance Analysis



## Focus



Assess staffing efficiency, shift utilisation, and role allocation across outlets.



## Key Insights



* Central maintained balanced staffing coverage but showed signs of occasional overstaffing.

* Harbour required stronger barista allocation due to beverage heavy demand.

* Campus suffered from under utilised staffing and unstaffed shifts.

* Several staffing gaps directly impacted service reliability and revenue generation.



## Shift Utilisation Analysis





  




## Staff Capacity by Outlet





  




## Role Group Demand Analysis





  




---



# 5️⃣ Branch Level Operational Efficiency



## Focus



Evaluate operational reliability using menu availability, reservation conversion, and end to end revenue flow.



## Key Insights



* Central operated as the strongest overall branch with balanced operational performance.

* Harbour performed efficiently within its niche strategy.

* Campus showed operational bottlenecks despite having demand potential.

* Product availability and reservation reliability strongly influenced branch performance.



## Product Availability by Outlet





  




## Reservation Reliability Analysis





  




## Order to Revenue Flow Analysis





  




---



# 💼 Business Value Delivered



This project demonstrates how SQL analytics and relational databases can support:



* Operational decision making

* Revenue optimisation

* Customer retention strategy

* Workforce planning

* Service reliability analysis

* Menu engineering

* Branch performance monitoring

* Business intelligence reporting



The project reflects how hospitality and retail organisations can use structured analytics to improve commercial performance and operational efficiency.



---



# ⚙️ How to Run the Project



## Step 1 — Clone Repository



```bash

git clone 

```



---



## Step 2 — Create Database



Create a new MySQL database using MySQL Workbench.



---



## Step 3 — Execute Schema Script



Run:



```sql

sql/schema/schema.sql

```



This creates all relational tables and constraints.



---



## Step 4 — Populate Operational Data



Run:



```sql

sql/data_insertion/data_insertion.sql

```



This inserts the simulated business dataset.



---



## Step 5 — Run Business Analytics Queries



Run:



```sql

sql/analysis/business_case_analysis.sql

```



This executes all business concern analyses and KPI queries.



---



# 🎯 Key Learnings



Through this project, I strengthened my skills in:



* Relational database design

* SQL analytics

* Operational KPI analysis

* Customer behaviour analysis

* Data modelling

* Business focused reporting

* Translating business problems into SQL solutions

* Designing analytical workflows for operational decision making



---



# 🚀 Future Enhancements



Potential future improvements include:



* Power BI dashboard integration

* Tableau visualisations

* Demand forecasting using Python

* Customer churn prediction models

* Automated reporting pipelines

* Cloud deployment using AWS RDS or Azure SQL

* Stored procedures and triggers

* Real time operational monitoring



---



# 📑 Conclusion



UrbanEats demonstrates how relational databases and SQL analytics can be used to solve real world operational and commercial business problems.



The project successfully combines:



* Database engineering

* SQL analytics

* Business intelligence

* Operational analytics

* Strategic business recommendations



The analysis highlights clear operational differences between branches while providing actionable insights for improving profitability, staffing efficiency, customer retention, and service reliability.



This project reflects a practical, business focused approach to data analytics and demonstrates industry relevant SQL and database skills applicable to retail, hospitality, and operational analytics environments.



---



# 👨‍💻 Author



### Muntasir Md Nafis



Business Analytics graduate with a Computer Science and Engineering background specialising in SQL analytics, operational analytics, and business intelligence.



### Areas of Interest



* Data Analytics

* Business Intelligence

* SQL Analytics

* Operational Analytics

* Predictive Analytics

* Retail & Customer Analytics

* Data Visualisation

* Business Reporting



GitHub: [https://github.com/nafis2508](https://github.com/nafis2508)



---



# 📜 License



This project is licensed under the MIT License.