AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making

The advent of Artificial Intelligence (AI) has dramatically altered Customer Relationship Management (CRM) by allowing organizations to anticipate customer behavior, customize interactions and automate service delivery. This research introduces an extensive AI-based predictive analytics framework aimed at improving customer engagement, retention and satisfaction using advanced Machine Learning (ML) and Natural Language Processing (NLP) methodologies. By using XGBoost for churn prediction and BERT-based models for sentiment analysis, the system efficiently handles both structured and unstructured customer data. The methodology involves sophisticated feature engineering, customer segmentation via K-Means clustering, and Customer Lifetime Value (CLV) prediction to aid data-driven business strategies. An NLP-driven chatbot offers real-time, personalized support, response time and improving user experience. Evaluation metrics such as accuracy, precision, recall and F1-score demonstrate the better performance of the proposed system compared to conventional CRM approaches. This work also addresses important issues such as data privacy compliance, algorithmic bias and explainability of AI decision-making. Ethical deployment and transparency of AI are emphasized for building confidence in automated CRM systems. Future evolution will tackle the use of reinforcement learning to facilitate learning-based interaction schemes and federated learning for trusted, decentralized management of data. This architecture does not only provide better CRM functionality but also builds a platform towards intelligent, responsible and scalable solutions for customer relations across industries.

@article{Gaidhani2025,
  title     = {AI-Driven Predictive Analytics for CRM to Enhance Retention Personalization and Decision-Making},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {4},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Yashika Gaidhani and Janjhyam Venkata Naga Ramesh and Sanjit Singh and Reetika Dagar and T Subha Mastan Rao and Sanjiv Rao Godla and Yousef A.Baker El-Ebiary},
  doi       = {10.14569/IJACSA.2025.0160456},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160456}
}