Mozer M C, Wolniewicz R, Grimes D B, Johnson E, Kaushansky H
Department of Computer Science, University of Colorado, Boulder, CO 80309-0430, USA.
IEEE Trans Neural Netw. 2000;11(3):690-6. doi: 10.1109/72.846740.
Competition in the wireless telecommunications industry is fierce. To maintain profitability, wireless carriers must control churn, which is the loss of subscribers who switch from one carrier to another.We explore techniques from statistical machine learning to predict churn and, based on these predictions, to determine what incentives should be offered to subscribers to improve retention and maximize profitability to the carrier. The techniques include logit regression, decision trees, neural networks, and boosting. Our experiments are based on a database of nearly 47,000 U.S. domestic subscribers and includes information about their usage, billing, credit, application, and complaint history. Our experiments show that under a wide variety of assumptions concerning the cost of intervention and the retention rate resulting from intervention, using predictive techniques to identify potential churners and offering incentives can yield significant savings to a carrier. We also show the importance of a data representation crafted by domain experts. Finally, we report on a real-world test of the techniques that validate our simulation experiments.
无线电信行业的竞争十分激烈。为保持盈利能力，无线运营商必须控制客户流失率，即用户从一家运营商转网至另一家运营商所造成的用户流失。我们探索运用统计机器学习技术来预测客户流失，并基于这些预测结果，确定应向用户提供何种激励措施，以提高用户留存率并使运营商的盈利能力最大化。这些技术包括逻辑回归、决策树、神经网络和提升算法。我们的实验基于一个包含近47000名美国国内用户的数据库，其中包括他们的使用情况、计费、信用、申请和投诉历史等信息。我们的实验表明，在关于干预成本和干预导致的留存率的各种假设下，使用预测技术识别潜在的流失客户并提供激励措施，可以为运营商节省大量成本。我们还展示了由领域专家精心设计的数据表示的重要性。最后，我们报告了对这些技术的实际测试，验证了我们的模拟实验。
