Leveraging non-respondent data in customer satisfaction modeling

Abstract Understanding the high likelihood of a dissatisfied customer leaving, customer satisfaction modeling has received significant attention by marketers and academic research. The major challenge in customer satisfaction modeling is the low response rate of surveys and the potential loss of valuable insights from non-respondents. We introduce a modeling framework that allows marketers to leverage existing information about non-respondents for predicting customer satisfaction at a specific time. We design a novel procedure to discover data-driven attributes that effectively represent the interactions of customers. Then, we propose a time-aware model to predict customer satisfaction or dissatisfaction and the time of events. We also design a learn-to-rank model to leverage non-respondents data for building a more accurate customer satisfaction model. A real-world dataset from an insurance company shows that the proposed framework accurately identifies satisfied or dissatisfied customers at a specific time and achieves a significantly better performance compared to extant methods.