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.

通过对八个来自全球电信组织的通话详单数据集进行不同的关系学习器策略测试，本研究评估了关系分类器与集体推断方法对关系学习器预测能力的影响以及在电信行业中预测客户流失时，关系学习器与传统预测方法相结合的性能。需要注意的是，网络构建对预测模型的结果有影响，并提供了如何在电信行业中应用社交网络分析进行流失预测的最佳方案，从网络架构到模型构建和评估均有所提示。