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Predictive Customer Intelligence: A Synthetic Data-Driven Evaluation of Machine Learning and NLP Integration for CRM Churn Prediction and Lifetime Value Forecasting
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 56 |
| Year of Publication: 2025 |
| Authors: Nidhi Sharma |
10.5120/ijca2025925991
|
Nidhi Sharma . Predictive Customer Intelligence: A Synthetic Data-Driven Evaluation of Machine Learning and NLP Integration for CRM Churn Prediction and Lifetime Value Forecasting. International Journal of Computer Applications. 187, 56 ( Nov 2025), 64-70. DOI=10.5120/ijca2025925991
Abstract
In today's data-driven business landscape, organizations are drowning in customer data but starving for actionable insights. While Customer Relationship Management (CRM) systems collect vast amounts of customer information from multiple touchpoints, most companies struggle to transform this raw data into strategic intelligence that can predict customer behavior, prevent churn, and optimize customer lifetime value. This gap between data collection and meaningful analysis represents a critical business challenge that costs organizations millions in lost revenue, inefficient marketing spend, and missed opportunities for customer retention. Traditional analytical approaches fall short when dealing with the volume, variety, and velocity of modern CRM data, particularly unstructured text from emails, support tickets, and social media interactions. This paper investigates whether Artificial Intelligence (AI) can meaningfully enhance Customer Relationship Management (CRM) systems by converting large volumes of raw, unstructured, and semi-structured data into high-value, actionable insights to improve customer interactions and drive strategic decision-making. The study simulates real-world CRM operations using a 451-record dataset that replicates typical customer behaviors, such as purchase history, support requests, social messages, and email exchanges, collected within a business environment utilizing CRM. An AI platform is proposed for processing, analyzing, and deriving predictive insights from this data, including sentiment analysis, churn prediction, and Customer Lifetime Value (CLV) forecasts. The technical implementation employs Python-based tools like Scikit-learn, NLTK, and TensorFlow. Results indicate substantial improvements in the accuracy of key CRM metrics, with the model achieving 89% accuracy for churn prediction and 92% in CLV forecasting, demonstrating the practical value of integrating AI into operational CRM settings.
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