Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
<|reference_start|>Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model: 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.<|reference_end|>
@article{gao2024causal,
    title={Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model},
    author={Chenyin Gao, Zhiming Zhang, Shu Yang},
    journal={arXiv preprint arXiv:2405.11377},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.11377},
    primaryClass={stat.ML cs.LG stat.ME}
}