The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis

The canonical design of customer satisfaction surveys asks for global satisfaction with a product or service and for evaluations of its distinct attributes. Users of these surveys are often interested in the relationship between global satisfaction and attributes; regression analysis is commonly used to measure the conditional associations. Regression analysis is only appropriate when the global satisfaction measure results from the attribute evaluations and is not appropriate when the covariance of the items lie in a low-dimensional subspace, such as in a factor model. Potential reasons for low-dimensional responses are that responses may be haloed from overall satisfaction and there may be an unintended lack of item specificity. In this paper we develop a Bayesian mixture model that facilitates the empirical distinction between regression models and relatively much lower-dimensional factor models. The model uses the dimensionality of the covariance among items in a survey as the primary classification criterion while accounting for the heterogeneous usage of rating scales. We apply the model to four different customer satisfaction surveys that evaluate hospitals, an academic program, smartphones, and theme parks, respectively. We show that correctly assessing the heterogeneous dimensionality of responses is critical for meaningful inferences by comparing our results to those from regression models