使用机器学习预测客户流失

Rama Krishna Peddarapu, Sofia Ameena, S. Yashaswini, Nadipelli Shreshta, Muppidi PurnaSahithi
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引用次数: 0

摘要

不同的客户需求和兴趣常常导致订阅取消。因此,运营订阅业务需要一个准确的客户流失预测模型,因为即使是很小的变化也会产生重大影响。如果卖方未被告知客户即将取消订阅,则不会采取任何行动来保留客户。因此,本研究试图设计和开发一个流失预测模型来预测订阅取消,并为特定的订阅者提供保留的激励。这大大节省了成本,并为任何在线业务创造了额外的收入来源。本研究的主要目的是分析不同的预测模型,以获得较高的预测精度。在现有的系统中,服务提供商在客户离开之前跟踪客户,以解决这个问题。本研究通过比较知名的机器学习技术来解决问题,并以更准确的方式预测结果。
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Customer Churn Prediction using Machine Learning
The varying customer requirements and interests often result in subscription cancellation. Hence, running a subscription business necessitates an accurate churn forecasting model as even a minor change will result in a significant impact. If the seller is not informed that the customer is about to cancel the subscription, no action will be taken to retain them. As a result, this research study attempts to design and develop a churn prediction model to predict a subscription cancellation and provide incentives for that particular subscriber to stay back. This results in significant cost savings and generate an additional revenue source for any online business. The primary goal of this research work is to analyze different models for predicting the active churners with high accuracy. In existing systems, the service providers track down the clients before they leave in order to solve this problem. This study has compared the well-known machine learning techniques to solve the problem and also predict the results in a more accurate way.
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