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Predicting customer churn creates opportunities to target customers with a marketing action or a promotion to prevent them from leaving. In this paper, a hierarchical generative approach will be applied in the context of churn prediction. The dependent variable and the covariates are modeled jointly conditioned on a deep latent structure, which resembles the hidden structure in neural networks. The conditional latent structure is capable of handling missing data and combining heterogeneous data types. Latent structures with multiple layers are non-linear and can model complex dependencies between the independent variables and the risk to churn. The hierarchical generative approach makes use of deep exponential families (DEFs). This class of models is able to extract a hierarchy of dependencies between latent variables. Similar to deep unsupervised feature learning, this analysis can improve predictions and provide extra insights into the nature of the data. The hidden layers in the DEFs enable the exploration of interesting structures in datasets. These patterns could help sales representatives in classifying customers according to their risk of churn, so that companies or managers can take more well-informed decisions.

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Please use this url to cite or link to this publication:

MLA
Tsang, Wai, and Dries Benoit. “Churn Prediction Using Hierarchical Generative Models.” IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : Abstracts, 2017.
APA
Tsang, W., & Benoit, D. (2017). Churn Prediction using hierarchical generative models. In IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : abstracts. Québec City, Canada.
Chicago author-date
Tsang, Wai, and Dries Benoit. 2017. “Churn Prediction Using Hierarchical Generative Models.” In IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : Abstracts.
Chicago author-date (all authors)
Tsang, Wai, and Dries Benoit. 2017. “Churn Prediction Using Hierarchical Generative Models.” In IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : Abstracts.
Vancouver
1.
Tsang W, Benoit D. Churn Prediction using hierarchical generative models. In: IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : abstracts. 2017.
IEEE
[1]
W. Tsang and D. Benoit, “Churn Prediction using hierarchical generative models,” in IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : abstracts, Québec City, Canada, 2017.
@inproceedings{8618207,
  abstract     = {Predicting customer churn creates opportunities to target customers
with a marketing action or a promotion to prevent them from leaving.
In this paper, a hierarchical generative approach will be applied
in the context of churn prediction. The dependent variable and the
covariates are modeled jointly conditioned on a deep latent structure,
which resembles the hidden structure in neural networks. The conditional
latent structure is capable of handling missing data and combining
heterogeneous data types. Latent structures with multiple layers
are non-linear and can model complex dependencies between the independent
variables and the risk to churn. The hierarchical generative
approach makes use of deep exponential families (DEFs). This class
of models is able to extract a hierarchy of dependencies between latent
variables. Similar to deep unsupervised feature learning, this analysis
can improve predictions and provide extra insights into the nature
of the data. The hidden layers in the DEFs enable the exploration of
interesting structures in datasets. These patterns could help sales representatives
in classifying customers according to their risk of churn,
so that companies or managers can take more well-informed decisions.},
  author       = {Tsang, Wai and Benoit, Dries},
  booktitle    = {IFORS 2017 : 20th Conference of the International Federation of Operational Research Societies : abstracts},
  language     = {eng},
  location     = {Québec City, Canada},
  title        = {Churn Prediction using hierarchical generative models},
  year         = {2017},
}