International Research Journal of Engineering and Technology (IRJET) Volume: 12 Issue: 05 | May 2025
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e-ISSN: 2395-0056
p-ISSN: 2395-0072
Smart Retention: Preventing and Predicting Customer Loss Using AI Niharika N1, Manaswini G2, Nidhi Rajiv3, Pavan G Raiker4 1 2 3 4 U.G Student, Dept. of Information Science Engineering, Dayananda Sagar College of Engineering, Bengaluru,
Karnataka, India. ---------------------------------------------------------------------***--------------------------------------------------------------------relying on historical trends without offering actionable Abstract - Customer churning is a significant challenge for businesses across industries. Maintaining relationships with current customers tends to be more economical than gaining new ones and is essential for long-term financial stability. This paper presents Smart Retention, a comprehensive, web-based AI platform designed to predict and prevent customer loss. Developed using HTML, React, TypeScript, Flask, and Tailwind CSS, the platform also incorporates an advanced conversational agent powered by the Gemini 2.0 Flash API. The platform utilizes behavioral insights and predictive modeling to detect customers likely to churn and initiates proactive engagement through smart interactions. The ultimate aim is to improve business retention rates and customer satisfaction through automation and real-time decision-making applications.
predictions. Moreover, existing systems do not provide personalized recommendations tailored to specific customer behaviors or preferences, leading to missed opportunities to engage with customers before they decide to leave. The absence of an intelligent system capable of forecasting churn risk and automating preventive actions results in significant revenue loss and decreased brand loyalty. Therefore, there is a pressing need for a smart, predictive system that uses machine learning to accurately detect early signs of churn and recommend targeted interventions. This project addresses that gap by designing a scalable, AI-based [3][6][13] retention model tailored to identify at-risk customers and suggest data-driven strategies to improve engagement and loyalty.
Key Words: Customer Churn Prediction, Logistic
1.2 Objectives
Regression, Machine Learning.
The primary objective of this project is to develop a smart system capable of predicting customer churn and assisting businesses in preventing it using artificial intelligence. The key goals are to design and train machine learning models that identify churn-prone customers with high accuracy; to analyze customer behaviour, demographics, and interaction data to detect churn signals; to build a dynamic dashboard for business users to visualize insights and track customer retention metrics; to provide actionable recommendations based[27]on churn probability and customer segmentation applications[26] and to create a flexible, modular architecture that can be easily adapted across various industries. Additionally, the project aims to reduce customer acquisition costs by focusing on personalized retention strategies. The system is designed to empower businesses with data-driven decision-making tools that enhance customer satisfaction and long-term loyalty, ultimately improving overall profitability and operational efficiency.
1.INTRODUCTION Customer retention [1] [8] has emerged as a critical business objective in the digital age. As organizations invest heavily in customer acquisition, it becomes equally important to retain existing customers to ensure profitability and market share. Research indicates that it is much more affordable to keep an existing customer than to bring in a new one. Therefore, early identification of churn risks combined with personalized retention strategies can yield substantial returns. In this context, artificial intelligence offers powerful tools for identifying at-risk customers through behavior analysis and automating responses using chatbots. The Smart Retention system brings together these features in a unified platform that not only identifies potential churn but also takes active steps to prevent it.
2. LITERATURE REVIEW
It provides actionable insights and customer-specific recommendations to support business decisions.
Over the years, extensive research has been conducted in the field of customer churn prediction using a range of machine learning [15][22] and deep learning techniques [21]. The studies summarized in Table I reflect the evolution of approaches across domains such as telecom, e-commerce, and financial services.
1.1 Overview Problem Statement Despite collecting large volumes of customer data, many businesses lack the capability to extract meaningful insights that could prevent customer loss. Traditional methods of analyzing customer churn are often reactive,
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