论文标题
估算订阅类型市场的叛逃:学术出版行业的经验分析
Estimating defection in subscription-type markets: empirical analysis from the scholarly publishing industry
论文作者
论文摘要
我们介绍了有关学术出版行业中客户流失预测的首次实证研究。该研究研究了我们在6。5年内对客户订阅数据预测的建议方法,该方法由主要的学术出版商提供。我们在客户叛逃和建模的背景下探索订阅类型市场,并对此类市场的业务模型进行分析,以及这些市场如何表征学术出版业务。提出的预测方法试图根据客户对提供商资源的重新采样的使用 - 在此上下文,内容下载的音量和频率。我们表明,这种方法在企业对企业的环境中既可以是准确又具有独特性,学术出版业务模型与之共享相似之处。这项工作的主要发现表明,虽然所有研究的预测模型,尤其是机器学习的集合方法,但要实现对搅拌的基本预测,但即使在每个客户可能与每种客户流失的特定行为属性相关的特定行为属性时,也可以实现这一目标。从最小可能的数据中允许对流失的高度准确推断。我们表明,基于重新采样客户在订阅时间上使用资源的使用是一种更好(简化的)方法,而不是考虑通常可以表征消费行为的高粒度时,它是更好的(简化)方法。
We present the first empirical study on customer churn prediction in the scholarly publishing industry. The study examines our proposed method for prediction on a customer subscription data over a period of 6.5 years, which was provided by a major academic publisher. We explore the subscription-type market within the context of customer defection and modelling, and provide analysis of the business model of such markets, and how these characterise the academic publishing business. The proposed method for prediction attempts to provide inference of customer's likelihood of defection on the basis of their re-sampled use of provider resources -in this context, the volume and frequency of content downloads. We show that this approach can be both accurate as well as uniquely useful in the business-to-business context, with which the scholarly publishing business model shares similarities. The main findings of this work suggest that whilst all predictive models examined, especially ensemble methods of machine learning, achieve substantially accurate prediction of churn, nearly a year ahead, this can be furthermore achieved even when the specific behavioural attributes that can be associated to each customer probability to churn are overlooked. Allowing as such highly accurate inference of churn from minimal possible data. We show that modelling churn on the basis of re-sampling customers' use of resources over subscription time is a better (simplified) approach than when considering the high granularity that can often characterise consumption behaviour.