ABC Insurance, previously a market leader, is facing a challenging trend of increasing customer churn while customer lifetime value remains stagnant or declines. The CEO emphasized the critical importance of understanding customers to address these trends effectively. The analysis in this project aims to identify patterns within customer data that could help revitalize ABC Insurance's customer engagement and product strategies, ultimately reducing churn and enhancing customer value.
The dataset consists of three key tables provided by the IT department, crucial for our comprehensive analysis:
ABC_df_customer: Includes customer IDs, addresses, policy details, and annual insurance payment amounts.ABC_df_demographics: Contains demographic data like income, family status, home ownership, and credit ratings.ABC_df_termination: Lists customers who have suspended their policies, including the suspension dates.
For detailed structure and variables of each dataset, please refer to the Data Link.
To set up this project for analysis:
- Clone the repository:
git clone https://github.com/yourrepository/abc-insurance.git
- Install required Python packages:
pip install -r requirements.txt
To run the analysis notebook, navigate to the repository folder and launch Jupyter Notebook:
jupyter notebook/data: Contains all the datasets used in the project./notebook: Jupyter notebook for analysis and modeling./src: Source code for custom Python functions used within the notebooks.README.md: Project overview and guide.LICENSE: Details on the usage and redistribution of this project.
This project is licensed under the MIT License - see the LICENSE file for details.