Suh Youngjung
LG Electronics Inc, Yeongdeungpo-Gu, Seoul, 07336 South Korea.
J Big Data. 2023;10(1):41. doi: 10.1186/s40537-023-00721-8. Epub 2023 Apr 5.
Customer churn is a major issue for large enterprises. In particular, in the rental business sector, companies are looking for ways to retain their customers because they are their main source of revenue. The main contribution of our work is to analyze the customer behavior information of actual water purifier rental company, where customer churn occurs very frequently, and to develop and verify the churn prediction model. A machine learning algorithm was applied to a large-capacity operating dataset of rental care service in an electronics company in Korea, to learn meaningful features. To measure the performance of the model, the F-measure and area under curve (AUC) were adopted whereby an F1 value of 93% and an AUC of 88% were achieved. The dataset containing approximately 84,000 customers was used for training and testing. Another contribution was to evaluate the inference performance of the predictive model using the contract status of about 250,000 customer data currently in operation, confirming a hit rate of about 80%. Finally, this study identified and calculated the influence of key variables on individual customer churn to enable a business person (rental care customer management staff) to carry out customer-tailored marketing to address the cause of the churn.
客户流失是大企业面临的一个主要问题。特别是在租赁业务领域，企业正在寻找留住客户的方法，因为客户是其主要收入来源。我们工作的主要贡献在于分析实际净水器租赁公司的客户行为信息（该公司客户流失现象非常频繁），并开发和验证客户流失预测模型。将机器学习算法应用于韩国一家电子公司租赁护理服务的大容量运营数据集，以学习有意义的特征。为了衡量模型的性能，采用了F值度量和曲线下面积（AUC），由此实现了93%的F1值和88%的AUC。包含约84000名客户的数据集用于训练和测试。另一个贡献是使用目前正在运营的约250000条客户数据的合同状态来评估预测模型的推理性能，确认命中率约为80%。最后，本研究识别并计算了关键变量对个体客户流失的影响，以使业务人员（租赁护理客户管理人员）能够开展针对客户的营销，以解决客户流失的原因。
