Behavioral Analytics of Consumer Complaints

Abstract

In the current study, the author implemented cohort analysis methodology of machine learning (ML) to assess the rate of consumers' complaint retention behavior toward firms. The author obtained a three-year range of data on customer complaints from the Consumer Financial Protection Bureau's website. After removing the missing data from the customer complaints dataset, the current study also uses the cohort analysis approach of ML to assess the rate of consumer complaints retained by businesses. According to the findings, organizations retain a significant portion of complaints. This study adds to the body of knowledge on consumer complaining behavior (CCB), especially by creating and deploying unsupervised machine learning-based technique to conceptualize consumers' complaint behavior in the financial service providers industry. Moreover, professionals will benefit greatly from this research.