A Two-Stage Model for Analyzing Customer Service Costs

Abstract

In industrial contexts where managing customer service costs is critical, accurately predicting and analyzing these costs presents a significant challenge, particularly when dealing with zero-inflated count data. This study proposes a Two-Stage Machine Learning approach that extends the traditional hurdle model, offering enhanced flexibility and adaptability to complex data structures without compromising interpretability. Through a real-world case study in the cleaning service sector focused on one-time service purchases, the proposed method identifies key cost drivers and provides actionable insights into customer behavior.
This research advances the field by presenting a highly effective method for analyzing zero-inflated data, outperforming popular models based on Poisson distribution. Simultaneously, it addresses practical business needs by supporting data-driven strategies to optimize operational resources and manage customer costs more effectively.