Causal analysis of customer churn using deep learning

David Hason RUDD, Huan HUO, Guandong XU

Research output: Chapter in Book/Report/Conference proceedingChapters

1 Citation (Scopus)

Abstract

Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar- value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investing in customers with churn risk is smart. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and assist enterprises to identify effects and possible causes for churn and subsequently use that knowledge to apply tailored incentives. This paper proposes a framework using a deep feedforward neural network for classification accompanied by a sequential pattern mining method on high-dimensional sparse data. We also propose a causal Bayesian network to predict cause probabilities that lead to customer churn. Evaluation metrics on test data confirm the XGBoost and our deep learning model outperformed previous techniques. Experimental analysis confirms that some independent causal variables representing the level of super guarantee contribution, account growth, and customer tenure were identified as confounding factors for customer churn with a high degree of belief. This paper provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds. Copyright © 2021 IEEE.

Original languageEnglish
Title of host publicationProceedings of 2021 International Conference on Digital Society and Intelligent Systems (DSInS)
Place of PublicationUSA
PublisherIEEE
Pages319-324
ISBN (Electronic)9781665406307
DOIs
Publication statusPublished - 2021

Citation

Rudd, D. H., Huo, H., & Xu, G. (2021). Causal analysis of customer churn using deep learning. In Proceedings of 2021 International Conference on Digital Society and Intelligent Systems (DSInS) (pp. 319-324). IEEE. https://doi.org/10.1109/DSInS54396.2021.9670561

Keywords

  • Churn analysis
  • Causality analysis
  • Deep neural network
  • Data mining

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