Customer Survival Analysis And Churn Prediction
In this project, I have utilized survival analysis models to see how the likelihood of the customer churn changes over time and to calculate customer LTV. I have also implemented the Random Forest model to predict if a customer is going to churn and deployed a model using the flask web app.
Overview
Understanding customer churn is crucial for businesses in various sectors, including telecommunications, insurance, and subscription services. The Customer Survival Analysis and Churn Prediction App offers a comprehensive way to analyze customer attrition and predict potential churn. By leveraging predictive analytics, this application aims to help organizations retain valuable customers by understanding their behaviors and identifying those at risk of leaving.
The app not only tracks customer churn over time but also analyzes the factors driving this behavior. With a user-friendly interface, it appeals to businesses looking to enhance their customer retention strategies, ultimately saving them the costs associated with acquiring new customers.
Features
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Predictive Analytics: Utilizes churn prediction models to identify customers most likely to leave, allowing businesses to target retention efforts effectively.
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Survival Analysis Tools: Employs non-parametric and semi-parametric methods to analyze customer behavior and their time until churn, offering insights into customer longevity.
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Kaplan-Meier Survival Curve: Visualizes the probability of customer retention over time, helping businesses understand retention rates and patterns.
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Hazard Analysis: Provides estimates of the likelihood that a specific customer will churn based on their characteristics and historical data.
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Customer Lifetime Value Estimation: Calculates the expected lifetime value of customers, assisting businesses in making informed decisions regarding retention investments.
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Group Comparison: Conducts log-rank tests to assess statistical significance between different customer groups, enhancing market targeting.
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User-Friendly Interface: Designed for ease of use, enabling businesses to quickly interpret data and make actionable marketing strategies.
These features make the Customer Survival Analysis and Churn Prediction App an invaluable tool for companies seeking to improve their customer retention and overall business performance.