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 the attributes, with regression analysis 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 responses that are haloed from overall satisfaction and an unintended lack of specificity of items. 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 heterogeneous usage of rating scales. We apply the model to four different customer satisfaction surveys evaluating hospitals, an academic program, smart-phones, 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. The Dimensionality of Customer Satisfaction Survey Responses and Implications for Driver Analysis