International Journal of Advanced Research in Computer and Communication Engineering

Abstract: Customer analytics is crucial for data-driven decision-making in today’s cutthroat business environment, but there isn’t a single platform that combines sales forecasting, churn prediction and Customer lifetime value (CLV) assessment. We provide an AI-driven framework that integrates these three essential features into a single system in order to close this gap. Our method uses a Bidirectional Long Short-Term memory (BLSTM) network for sales forecasting to capture intricate temporal patterns, XGBoost for churn prediction to identify at-risk customers, and the Gamma-Gamma model for CLV estimation to predict future customer spending. Through the integration of various models, our system offers a thorough and precise understanding of consumer behavior, empowering companies to maximize customer engagement and revenue expansion. Experimental results demonstrate superior predictive performance over traditional approaches, making this a valuable tool for organizations seeking to enhance their customer analytics capabilities.

Keywords: Customer Lifetime Value (CLV), Churn Prediction, Sales Forecasting, AI-driven analytics, Gamma-Gamma model, XGBoost, Bidirectional Long Short-term memory (BLSTM), Customer retention, Predictive modelling, Business Intelligence.

Downloads: | DOI: 10.17148/IJARCCE.2025.14243