Generative Ai for Predictive Customer Churn Immunization

Abstract

Retaining current customers is frequently more cost-effective than obtaining new ones, making customer churn a major risk to a company's viability, particularly in competitive marketplaces. An innovative method for predicting and preventing customer churn, called customer churn immunisation, is discussed in this study. It makes use of Generative Artificial Intelligence (Generative AI). Our approach utilises generative models like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to model customer behaviour, generate synthetic at-risk profiles, and propose proactive retention strategies that are customised to each customer's journey, as opposed to traditional predictive models that only identify customers who are likely to leave. Our integrated pipeline uses these models to predict churn and, using simulated results, recommends the best treatments. Over time, the algorithm becomes better at developing vaccination techniques by learning and adapting to new data. Based on real-world datasets from the telecom and e-commerce industries, our experimental results demonstrate that the generative method not only increases customer retention rates but also surpasses standard classification models in terms of recall and precision. In the age of AI-driven business optimisation, this research shows great promise for intelligent and adaptable client retention solutions.