Analysis of Vehicle Customer Satisfaction Data using the Binary Logistic Regression

Field surveys are frequently used to monitor quality levels in the automotive industry. They generate models that reveal the factors which drive customer satisfaction and purchase intent. In this way, companies can determine the areas which require improvement actions or planning for future products that are better suited for customer expectations.

Unlike standard regression models, the binary logistic regression is appropriate for non-continuous binary responses. It matches customer satisfaction metrics, which can be evaluated as either “satisfied” or “not satisfied”. This paper presents the binary logistic regression as an alternative to construct customer satisfaction models. A case study of the analysis of a vehicle in the Brazilian market is used to illustrate its application.