Assessing the effectiveness of OTT services, branded apps, and gamified loyalty giveaways on mobile customer churn in the telecom industry: A machine-learning approach
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引用次数: 0
Abstract
Telecom operators allocate a significant amount of resources to retain their customers as the organic growth in the number of customers is slowing down. Gamified loyalty programs, branded apps, and over-the-top (OTT) services emerged as ways to develop customer acquisition and retention strategies. Despite these strategies, some mobile customers still churn; therefore, churn prediction plays an essential role in the sustainable future of telecom businesses. Churn prediction is used both to detect customers with a high propensity to churn and to identify the reasons behind their churn behavior. This study examines several features affecting the churn behavior of mobile customers, including branded apps, gamified loyalty programs, and OTT services. In this study, the secondary data is provided by a telecom operator and contains the attributes of both churner and non-churner mobile customers. Logistic regression and random forest classifiers are compared in terms of their predictive power, and we used the latter as the machine learning algorithm in the churn prediction model. To understand the variable importance, mean decrease in impurity and permutation importance are performed. The key findings of this research reveal that while gamified loyalty giveaways and branded app strategies are effective, OTT service strategies show lower importance in predicting mobile customer churn behavior.
由于客户数量的有机增长正在放缓,电信运营商为留住客户投入了大量资源。游戏化忠诚度计划、品牌应用程序和OTT(Over-the-Top)服务成为发展客户获取和保留战略的途径。尽管采取了这些策略,但仍有一些移动用户流失;因此,流失预测对电信业务的可持续发展起着至关重要的作用。流失预测既可用于检测流失倾向较高的客户,也可用于识别其流失行为背后的原因。本研究探讨了影响移动客户流失行为的几个特征,包括品牌应用程序、游戏化忠诚度计划和 OTT 服务。在本研究中,二手数据由一家电信运营商提供,包含流失和非流失移动客户的属性。我们比较了逻辑回归和随机森林分类器的预测能力,并将后者作为流失预测模型的机器学习算法。为了了解变量的重要性,我们对不纯度的平均降幅和排列重要性进行了分析。本研究的主要发现表明,虽然游戏化忠诚度赠品和品牌应用程序策略是有效的,但 OTT 服务策略在预测移动客户流失行为方面的重要性较低。
期刊介绍:
Telecommunications Policy is concerned with the impact of digitalization in the economy and society. The journal is multidisciplinary, encompassing conceptual, theoretical and empirical studies, quantitative as well as qualitative. The scope includes policy, regulation, and governance; big data, artificial intelligence and data science; new and traditional sectors encompassing new media and the platform economy; management, entrepreneurship, innovation and use. Contributions may explore these topics at national, regional and international levels, including issues confronting both developed and developing countries. The papers accepted by the journal meet high standards of analytical rigor and policy relevance.