Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.

中文翻译：

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电信客户流失预测的社交网络分析：模型构建、评估和网络架构

电信行业正在使用社交网络分析方法来预测客户流失，并取得了巨大成功。特别是已经表明，适应这个特定问题的关系学习器可以提高预测模型的性能。在当前的研究中，我们通过将构建关系学习器的不同策略应用于来自世界各地电信组织的总共八个不同的呼叫详细记录数据集，对构建关系学习器的不同策略进行了基准测试。我们统计评估了关系分类器和集体推理方法对关系学习器预测能力的影响，以及将关系学习器与电信行业预测客户流失的传统方法相结合的模型的性能。最后，我们研究了网络构建对模型性能的影响；我们的发现意味着网络中边和权重的定义确实对预测模型的结果有影响。作为研究的结果，最佳配置是使用二进制权重和无向网络，使用网络变量丰富的非关系学习器，无需集体推理。此外，我们提供了有关如何以最佳方式将社交网络分析应用于电信行业流失预测的指南，范围从网络架构到模型构建和评估。最佳配置是使用二进制权重和无向网络，使用网络变量丰富的非关系学习器，无需集体推理。此外，我们提供了有关如何以最佳方式将社交网络分析应用于电信行业流失预测的指南，范围从网络架构到模型构建和评估。最好的配置是使用二进制权重和无向网络，使用网络变量丰富的非关系学习器，无需集体推理。此外，我们提供了有关如何以最佳方式将社交网络分析应用于电信行业流失预测的指南，范围从网络架构到模型构建和评估。