Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model

Chenyin Gao, Zhiming Zhang, Shu Yang
Proceedings of the 41st International Conference on Machine Learning, PMLR 235:14920-14953, 2024.

Abstract

This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which incorporates tensor completion methods for a principled causal analysis of customer churn. A crucial element of our approach is the formulation of a 1-bit tensor completion for the parameter tensor. This captures hidden customer characteristics and temporal elements from churn records, effectively addressing the binary nature of churn data and its time-monotonic trends. Our model also uniquely categorizes interventions by their similar impacts, enhancing the precision and practicality of implementing customer retention strategies. For computational efficiency, we apply a projected gradient descent algorithm combined with spectral clustering. We lay down the theoretical groundwork for our model, including its non-asymptotic properties. The efficacy and superiority of our model are further validated through comprehensive experiments on both simulated and real-world applications.

Cite this Paper

BibTeX
@InProceedings{pmlr-v235-gao24q, title = {Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model}, author = {Gao, Chenyin and Zhang, Zhiming and Yang, Shu}, booktitle = {Proceedings of the 41st International Conference on Machine Learning}, pages = {14920--14953}, year = {2024}, editor = {Salakhutdinov, Ruslan and Kolter, Zico and Heller, Katherine and Weller, Adrian and Oliver, Nuria and Scarlett, Jonathan and Berkenkamp, Felix}, volume = {235}, series = {Proceedings of Machine Learning Research}, month = {21--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v235/main/assets/gao24q/gao24q.pdf}, url = {https://proceedings.mlr.press/v235/gao24q.html}, abstract = {This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which incorporates tensor completion methods for a principled causal analysis of customer churn. A crucial element of our approach is the formulation of a 1-bit tensor completion for the parameter tensor. This captures hidden customer characteristics and temporal elements from churn records, effectively addressing the binary nature of churn data and its time-monotonic trends. Our model also uniquely categorizes interventions by their similar impacts, enhancing the precision and practicality of implementing customer retention strategies. For computational efficiency, we apply a projected gradient descent algorithm combined with spectral clustering. We lay down the theoretical groundwork for our model, including its non-asymptotic properties. The efficacy and superiority of our model are further validated through comprehensive experiments on both simulated and real-world applications.} }
Endnote
%0 Conference Paper %T Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model %A Chenyin Gao %A Zhiming Zhang %A Shu Yang %B Proceedings of the 41st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2024 %E Ruslan Salakhutdinov %E Zico Kolter %E Katherine Heller %E Adrian Weller %E Nuria Oliver %E Jonathan Scarlett %E Felix Berkenkamp %F pmlr-v235-gao24q %I PMLR %P 14920--14953 %U https://proceedings.mlr.press/v235/gao24q.html %V 235 %X This study introduces an innovative method for analyzing the impact of various interventions on customer churn, using the potential outcomes framework. We present a new causal model, the tensorized latent factor block hazard model, which incorporates tensor completion methods for a principled causal analysis of customer churn. A crucial element of our approach is the formulation of a 1-bit tensor completion for the parameter tensor. This captures hidden customer characteristics and temporal elements from churn records, effectively addressing the binary nature of churn data and its time-monotonic trends. Our model also uniquely categorizes interventions by their similar impacts, enhancing the precision and practicality of implementing customer retention strategies. For computational efficiency, we apply a projected gradient descent algorithm combined with spectral clustering. We lay down the theoretical groundwork for our model, including its non-asymptotic properties. The efficacy and superiority of our model are further validated through comprehensive experiments on both simulated and real-world applications.
APA
Gao, C., Zhang, Z. & Yang, S.. (2024). Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model. Proceedings of the 41st International Conference on Machine Learning, in Proceedings of Machine Learning Research 235:14920-14953 Available from https://proceedings.mlr.press/v235/gao24q.html.

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