Customer Subscription, Segmentation, and Retention Using Machine Learning Models

In the current era of data-driven marketing, effectively managing customer subscription, segmentation, and retention has become essential for sustaining business growth and enhancing user engagement. This study introduces a unified, explainable framework that concurrently addresses all three dimensions using a blend of machine learning and deep learning methodologies, tailored for practical deployment across diverse domains. For customer subscription prediction, we utilize the Bank Telemarketing dataset, comparing the performance of traditional ensemble learning models with a proposed deep learning model that incorporates data balancing techniques. The proposed model deserves a significant improvement, reaching with 93.00% prediction accuracy. In customer segmentation, we employ a customer purchase-related dataset and evaluate two clustering techniques KMeans and Gaussian Mixture Model (GMM) where GMM demonstrates the most effective separation of customer behavior clusters. Feature scaling is applied on the LRFMSQ (Length, Recency, Frequency, Monetary, Satisfaction, Quantity) attributes to ensure uniformity and improve clustering performance. For customer retention prediction, Telecom data was used, comparing the performance of the proposed method with the existing. The proposed method outperforms the existing. This proposed method consists of a Generative Adversarial Network (GAN) for class imbalance and ConvLSTM with attention mechanism, and Grey Wolf Optimization (GWO)