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

• URL: http://arxiv.org/abs/2405.11377v1
• Date: Sat, 18 May 2024 19:54:14 GMT
• Title: Causal Customer Churn Analysis with Low-rank Tensor Block Hazard Model
• Authors: Chenyin Gao, Zhiming Zhang, Shu Yang,
• Abstract summary: 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.
• Score: 4.694536172504849
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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