There is a long tradition of museum research assessing visitors' personal background. In this article, we suggest an insightful way to enhance and intensify visitor analyses and adopt a more integrative approach. To this end, we draw attention to Latent Class Analysis (LCA), a classification method that allows us to investigate visitor profiles rather than isolated characteristics while using a mixture of socio-demographic variables and other personal characteristics. We illustrate our suggestion with two examples drawn from a study we conducted at two science museums and two museums of cultural history. The first example shows the use of LCA on variables that are easily assessed and that many museums already have access to through their visitor surveys. The second LCA involves more specific cognitive and motivational visitor variables that are of general importance for learning and that are assumed to be especially important when dealing with conflicting information. Both examples illustrate the additional value of investigating visitor profiles for researchers as well as museum practitioners.