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@article{187029,
        author = {Dr.P.Prabaharan and D Sandhya and M. Boobalan},
        title = {A SURVEY PAPER ON ENHANCED LIFETIME VALUE THROUGH AUTOENCODERS FOR CHURN PREDICTION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3506-3510},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187029},
        abstract = {Client churn significantly impacts business revenue, growth, and sustainability. Traditional churn prediction models often struggle to accurately interpret complex customer behaviors due to high-dimensional, noisy data. To address this, we propose an Autoencoder-based framework that extracts latent features from raw customer data while reducing noise and redundancy. These features create a concise, informative representation of customer behavior, improving churn prediction and customer lifetime value (CLV) estimation. Experiments on real-world datasets demonstrate that this approach outperforms conventional methods in accuracy. By providing deeper insights into customer patterns, the framework enables businesses to implement targeted retention strategies, facilitating better decision-making to reduce churn, maximize customer value, and drive long-term profitability in competitive markets.},
        keywords = {Client Churn, Autoencoders, Feature Extraction, Customer Lifetime Value (CLV), Deep Learning, Retention Strategies.},
        month = {November},
        }