# Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture

arXiv: https://arxiv.org/abs/2001.06701 · HTML: https://mlsanity.com/paper/2001.06701/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jan 2020.

## Built on by (most cited first, 7 of 7)

- [Data transformation based optimized customer churn prediction model for the telecommunication industry](https://mlsanity.com/paper/2201.04088/) (2022)
- [On the combination of graph data for assessing thin-file borrowers' creditworthiness](https://mlsanity.com/paper/2111.13666/) (2021)
- [Influencer Detection with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2211.09664/) (2022)
- [Privacy-Preserving Customer Churn Prediction Model in the Context of Telecommunication Industry](https://mlsanity.com/paper/2411.01447/) (2024)
- [INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks](https://mlsanity.com/paper/2307.08131/) (2023)
- [Understanding & Predicting User Lifetime with Machine Learning in an Anonymous Location-Based Social Network](https://mlsanity.com/paper/2103.01300/) (2021)
- [Friend Ranking in Online Games via Pre-training Edge Transformers](https://mlsanity.com/paper/2302.10043/) (2023)

## Abstract (arXiv)

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.