RetailPulse: Online Retail Analytics Dashboard

Authors : Dhruv Garg, Farhan Bin Faisal, Gilbert Akuja, Lixuan Lin

Motivation and Purpose

Retail operations managers face challenges in managing inventory, tracking sales trends, and identifying market expansion opportunities. Making informed decisions swiftly is crucial to staying competitive. RetailPulse was created to bridge this gap by providing an interactive, data-driven dashboard that enables retailers to gain real-time insights into their business performance. By leveraging historical transactional data, the dashboard uncovers key patterns in revenue trends, customer retention, and product performance, empowering managers to make strategic, evidence-based decisions that drive business growth.

Data Attribution

This project uses data from the Online Retail Dataset availabe on UC Irvine Machine Learning Repository.

High Level Description of Dashboard Usage

• Monitor Sales Trends – Track sales performance over time with interactive line charts.
• Analyze Customer Retention – Identify repeat customers and assess retention trends.
• Explore Market Expansion Opportunities – Use interactive maps to visualize sales distribution across different regions.
• Evaluate Product Performance – Gain insights into top-performing products through bar charts and word clouds.

Demo

Running the App Locally

1. Clone the repository

git clone https://github.com/UBC-MDS/DSCI-532_2025_15_RetailPulse.git

2. Navigate to the project directory

cd DSCI-532_2025_15_RetailPulse

3. Create a conda environment

conda env create -f environment.yaml

4. Activate the environment

conda activate retailpulse

5. Run the application

python src/app.py

Support

For any issues regarding running the dashboard or feature requests, lease contuct the team using GitHub Issues

Contributing

The team welcome meaningful contributions to the project. Please find more details on how you can contribute in the contribution guidelines

License

The RetailPulse software code contained in this project is licensed under the MIT License. See the LICENSE file for more details.

The project report is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) License. See the license file for details. If reusing any part of this code or report, please provide proper attribution by linking to this repository.

References

• Dataset Source
• Documentation: Dash, Plotly, Pandas