Application of factor analysis to customer survey data processing for generating metrics that underlie customers´ responses

Factor analysis is applied to customer survey data in order to facilitate data processing and to create new metrics that underlie customers responds. It is shown that the method of principal components is the most suitable method for this purpose. It is shown also that from the commonly known property of principal components to accumulate maximum of the variance, follows another important and helpful property of principal components to accumulate maximum of correlation between them and the original data. This property is used for choosing a sufficient number of utilized principal components. The overall metrics that represent the essence of various different metrics rated by customers are created as examples.