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

TL;DR

This paper proposes a novel causal analysis method for customer churn using a tensorized latent factor hazard model that captures hidden features and intervention effects, validated through experiments.

Contribution

It introduces a tensorized latent factor hazard model with tensor completion for causal customer churn analysis, addressing binary and temporal data complexities.

Findings

Model effectively captures hidden customer traits.

Outperforms existing methods in experiments.

Provides theoretical guarantees for its estimators.

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.