有效的健身房客户保留率流失预测模型

Jas Semrl, Alexandru Matei
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引用次数: 8

摘要

在健身行业,滚动健身房会员合同允许客户在不提前通知的情况下终止合同。客户流失预测是机器学习研究中一个众所周知的领域。然而,许多公司在试图将这项研究转化为自己的数据集和IT基础设施时,面临着数据科学技能的差距。在本文中,我们提出了一系列旨在预测客户行为的实验,以提高健身房的利用率和客户保留率。我们使用了两个现成的机器学习平台,这样我们就可以评估这些由非机器学习专家使用的平台是否可以帮助公司改善他们的服务。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Churn prediction model for effective gym customer retention
In the fitness industry, rolling gym membership contracts allow customers to terminate a contract with little advanced notice. Customer churn prediction is a well known area in Machine Learning research. Many companies, however, face a data science skills gap when trying to translate this research onto their own datasets and IT infrastructure. In this paper we present a series of experiments that aim to predict customer behaviour, in order to increase gym utilisation and customer retention. We use two off-the-shelf machine learning platforms, so that we can evaluate whether these platforms, used by non ML experts, can help companies improve their services.
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