Cloud Computing /Deep Learning Customer Churn Prediction for SaaS Platforms
DOI:
https://doi.org/10.15680/IJCTECE.2026.0901012Keywords:
Customer Churn, SaaS Platforms, Cloud Computing, Deep Learning, Artificial Neural Networks, LSTM, Predictive Analytics, Customer RetentionAbstract
Churn prediction is a critical business strategy for Software-as-a-Service (SaaS) platforms, as customer retention is more cost-effective than new customer acquisition. This project leverages the power of deep learning and cloud computing to develop a robust and scalable customer churn prediction system for SaaS businesses. Churn prediction is a critical business strategy for Software-as-a-Service (SaaS) platforms, as customer retention is more cost-effective than new customer acquisition. This project leverages the power of deep learning and cloud computing to develop a robust and scalable customer churn prediction system for SaaS businesses.
In today’s competitive Software-as-a-Service (SaaS) market, keeping customers is more important than gaining new ones. When users stop using a service, it leads to customer churn, which directly affects a company’s revenue. This project, “Customer Churn Prediction for SaaS Platforms using Cloud Computing and Deep Learning,” aims to build a smart system that can predict which customers are likely to leave by studying their behaviour and usage data. The system uses Cloud Computing for storing and processing large amounts of data efficiently, and Deep Learning models such as Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) to find hidden patterns and make accurate predictions. By combining these technologies, the project provides a powerful and scalable solution that helps SaaS companies understand their customers better, take preventive actions, and reduce churn effectively.
The main goal of the “Customer Churn Prediction for SaaS Platforms using Cloud Computing and Deep Learning” project is to develop an intelligent, scalable, and cloud-based predictive system that can accurately identify customers who are likely to discontinue using a SaaS product.
By leveraging deep learning models and cloud computing technologies, the system aims to analyse customer usage patterns, engagement behaviour, and transaction data to forecast churn probability. The ultimate objective is to enable SaaS providers to proactively implement retention strategies, reduce customer loss, and improve overall business performance through data-driven decision-making.
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