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.

使用潜在因子块危险模型和张量完成方法，在分析各种干预对客户流失影响时，提出了一种创新方法。模型考虑了客户流失数据的二元特性和时间单调趋势，并通过独特的干预分类方法增强了实施客户保留策略的精确性和实用性。