Service failure monitoring via multivariate multiple linear regression profile schemes with dimensionality reduction

Service failures can be manifested through customer complaints in the form of negative reviews. It is vital for businesses to monitor the occurrence and severity of customer complaints to help balance service failure responses and costs. Existing monitoring schemes can detect complaint anomalies but fail to explain the reasons for these anomalies. We propose new multivariate multiple linear regression profile (MMLRP) based schemes to monitor the relationship between customer complaints and identified explanatory variables. Conventional MMLRP-based schemes suffer performance loss with more than five explanatory variables, and the fixed parameters drawn from a historical process are unsuitable for a changing review-generation process. As such, we apply dimensionality reduction techniques to the explanatory variables and incorporate the variability in new review samples. We use both simulation analysis and an airline service case study to show the effectiveness of our proposed MMLRP-based schemes in customer complaint anomaly detection and diagnosis.