大数据驱动的投诉预测模型

随着电信行业市场竞争的不断加剧,用户对服务质量要求逐步提高,导致用户投诉率不断攀升。在此情况下,通过准确预测用户投诉行为来降低用户投诉率成为运营商关注的重点。目前传统的投诉预测模型仅从分类算法和人工调研特征来讨论,而没有充分利用运营商的大数据。因此,提出了在Hadoop/Spark大数据平台上使用并行随机森林来构建用户预测投诉模型,它不仅用到了业务支持系统数据,而且还用到了运营支持系统数据和客服工单数据,并在此基础上进一步增加了反映用户相互关系的图特征和二阶特征。基于上海市某运营商数据的实验结果表明,利用多来源、高维度的特征来训练用户投诉预测模型的精度会明显高于传统方法,在此基础上有针对性地对目标用户采取安抚措施,可以降低用户投诉率,获得较高的商业价值。

Big Data-driven Complaint Prediction Model

Because of fierce competition in telecommunication (telco) industry,it is crucial to reduce customer complaint rate and improve customer services to improve competitive advantages for telecommunication companies.Thus,accurately predicting the complaint behaviors to reduce the complaint rates becomes one of the most important tasks for telco operators.Traditional complaint prediction models only focus on classification algorithms and artificial feature selection and do not release the full power of telco big data.In this paper,we proposed a big data-driven complaint prediction model on the Hadoop/Spark platform using efficient parallel random forests.To better explore the performance of the proposed method,we performed feature engineering not only on all data from business support system (BSS) and operations support system (OSS),but also on those from the customer service records (CSR).Moreover,several useful graph-based features and second-order features between the relationship of users were designed and used to enhance the predictive performance.Experiment results based on the practical data of the telco operator in Shanghai show that using more data sources and high dimension data to train the complaint prediction models make the prediction accuracy higher than the state-of-the-art algorithms.Based on the result,we took comfort measures on target users,which can make the lo-wer complaint rate of users and bring significant business value to the operator.