Skip to content

Rindhujatreesa/customer-lifetime-value-prediction-for-auto-insurance-companies

Repository files navigation

Customer Lifetime Value Prediction for Auto-Insurance Companies

Auto Insurance Customer Lifetime Value Prediction and Analysis

Team 4: Data Walkers

Kumbam Nithin Goud
Rindhuja Treesa Johnson
Prudhvi Yaswanth Mundluri

Business Problem

An Auto Insurance company X in the USA is facing issues in retaining its customers and wants to advertise promotional offers for its loyal customers. They are considering Customer Lifetime Value CLV as a parameter for this purpose.
Customer Lifetime Value (CLV) signifies the worth of a customer to a company across a specified duration. In the insurance sector, where competition is intense, customers consider more than just insurance premiums when making decisions. Customer Lifetime Value (CLV), being centered on the customer, offers a strong foundation for retaining high-value clients, earning more from lower-valued clients, and improving overall customer satisfaction. Effectively leveraging CLV can result in better customer acquisition and retention, decreased churn rates, informed marketing budget planning, detailed ad performance measurement, and numerous other advantages.

Project Goal

To address customer retention challenges faced by a US auto insurance company X by leveraging Customer Lifetime Value (CLV) prediction and analysis.

Blank diagram

Methodology

Data Analysis with Python: Utilized Python libraries for data cleaning and pre-processing, ensuring data quality for accurate model training and analysis.

Machine Learning for CLV Prediction: Developed and implemented a machine learning model to predict individual customer lifetime value, enabling the identification of high-value customers for targeted retention strategies.

Interactive Visualizations with Power BI: Developed interactive dashboards using Power BI to visualize key CLV insights and trends, facilitating data-driven decision making for marketing and customer retention initiatives.
dashbord

Q&A LLM Model with Gemini: Built a question-answering system using the Gemini large language model on the insurance database, enabling Auto insurance company to easily retrieve specific information and gain deeper understanding of the data.
Input interface

Impact

  • Improved Customer Retention: Identified high-value customers for targeted promotional offers and loyalty programs, leading to increased customer retention and reduced churn rates.
  • Enhanced Marketing Effectiveness: Enabled data-driven allocation of marketing resources towards high-value customer segments, maximizing return on investment.
  • Data-driven Decision Making: Empowered stakeholders with CLV insights and interactive visualizations, facilitating informed decisions regarding customer acquisition, retention, and overall business strategy.
  • Enhanced User Experience: Provided a user-friendly Q&A interface for easy access to information, promoting data democratization and knowledge sharing within the organization reducing the time required for developing efficient SQL queries..

Technical Skills Demonstrated

Programming Language: Python
Scripting Languages: HTML, CSS, JS
Databases: MySQL
Tools and Technologies: AWS, MS Power​BI, MS Excel, GitHub
IDE: Jupyter ​Notebook, Visual ​Studio ​Code
API: Gemini LLM


This project showcases expertise in leveraging data science and machine learning to solve real-world business problems, specifically within the auto insurance industry. The developed solution provides a comprehensive framework for understanding, predicting, and utilizing customer lifetime value to improve customer retention and drive business growth.

Dataset Description

The dataset represents Customer lifetime value of an Auto Insurance industry in the United States, it includes over 24 features and 9134 records to analyze the lifetime value of Customer.

Data Source and References

About

Predicted clv making it easier for the auto-insurance companies to decide on the premiums of their incoming clients and thus balance the total risk in the market

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages