Andreea Dumitrache, Monica Mihaela Matei Maer, Stelian Stancu, O. Popescu
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引用次数: 0
Abstract
Abstract The telecommunication industry is growing every day, increasing its competitiveness. In almost all European countries, the market penetration of mobile network users exceeded 100% (for example in Croatia it is over 130%). Acquiring new users is virtually impossible because there are no new users. There are only users of rival companies who are exposed to numerous marketing campaigns carefully designed to try to win them. That’s why customer retention activity and churn prevention is a necessity. The purpose of this paper is to predict customers who are willing to migrate to another Romanian mobile telecommunications company and to determine the strongest factors of influence in the consumer’s decision to leave their current service provider for another provider. Migration behavior analysis is developed for customers with postpaid subscriptions. We applied the ROSE package for re-sampling and decision trees on the dataset to identify decision makers in the migration process. The combination of the two techniques in our study did not significantly improve the performance of the classifier measured by the AUC (Area Under the Curve). After balancing the sample, however, we obtain the optimal value of the AUC coefficient (0.724) for the second cluster, making the correct prediction of the churn phenomenon on the analyzed data set. The study is an addition of Churn Analysis in Romanian Telecommunications Company, M. M. Matei Maer and A. Dumitrache (2018), where ROSE and logistic regression was applied to the same dataset for the same purpose: balancing the sample and churn prediction, but the value of the AUC coefficient was really low, making it difficult to accurately predict the churn phenomenon. Therefore, another purpose of the current paper is to compare the performance of the two techniques used in combination with ROSE on the same set of data.
电信行业的发展日新月异,其竞争力日益增强。在几乎所有的欧洲国家,移动网络用户的市场渗透率都超过了100%(例如克罗地亚超过了130%)。获得新用户几乎是不可能的,因为没有新用户。只有竞争对手的用户才会接触到无数精心设计的营销活动,试图赢得他们的青睐。这就是为什么客户留存活动和客户流失预防是必要的。本文的目的是预测愿意迁移到另一家罗马尼亚移动电信公司的客户,并确定影响消费者决定离开当前服务提供商的最强因素。迁移行为分析是为后付费订阅的客户开发的。我们使用ROSE包对数据集进行重新采样,并在数据集上使用决策树来识别迁移过程中的决策者。在我们的研究中,两种技术的结合并没有显著提高AUC(曲线下面积)测量的分类器的性能。然而,在平衡样本后,我们获得了第二聚类的AUC系数的最优值(0.724),从而正确预测了所分析数据集上的流失现象。该研究是对罗马尼亚电信公司M. M. Matei Maer和A. Dumitrache(2018)的流失分析的补充,其中ROSE和逻辑回归应用于同一数据集,目的相同:平衡样本和流失预测,但AUC系数的值非常低,难以准确预测流失现象。因此,本文的另一个目的是比较与ROSE结合使用的两种技术在同一组数据上的性能。