Welcome to the Amazon Prime Customer Dashboard project! This interactive dashboard is designed to offer deep insights into user engagement, subscription renewal behavior, and content preferences of Amazon Prime members. Our goal is to help Amazon Prime's Customer Engagement and Marketing Teams better understand the key trends that affect user retention and satisfaction. By analyzing engagement metrics and purchase history, the dashboard provides actionable insights that can inform targeted marketing strategies to improve subscription renewal rates and enhance the overall customer experience.
The dashboard allows you to explore and visualize data related to:
- User Engagement: Dive into metrics that show how users interact with Amazon Prime.
- User Preferences: Understand user preferences such as user ratings and product categories of their recent purchases.
- Subscription Renewal Behavior: Understand patterns in subscription renewal and churn.
This tool is perfect for marketing teams and anyone interested in understanding user behavior and making data-driven decisions.
You can view the live dashboard at the following link: Deployed Dashboard
Have a look at the following demo: 
If you encounter any issues or need support, please open an issue on our GitHub repository, and we will be happy to assist you.
If you're interested in running the app locally or contributing to its development, follow these high-level steps:
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Clone the repository to your local machine.
git clone https://github.com/UBC-MDS/DSCI-532_2025_7_amazon_marketing.git
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Install dependencies We have provided a requirements.txt file to help you get started. You can install the dependencies by running:
pip install -r requirements.txt
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Install our conda environment by running the following command-line command:
conda env create -f environment.yml
Activate our conda environment by running the following command-line command:
conda activate amazon-prime
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Run the app Once the dependencies are installed, you can run the dashboard locally:
python -m src.app
Interested in contributing? Check out our Contributing Guidlines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.
DSCI-532_2025_7_amazon_marketing was created by Daduica Julian (@jdaduica), Yixuan Gao (@yixuangaoclara), and Mavis Wong(@MavisWong295). It is licensed under the terms of the MIT license.