We describe a study that began as a qualitative research piece, involving a series of depth interviews with a wide spectrum of art collectors. Text data from these interviews were analyzed using Watson, a natural language processing content analysis software that enables an identification of the main personality traits of each respondent. This software produced output percentile scores on the Big Five personality traits. The Big Five is the most widely used personality model and describes how a person engages with the world based on five dimensions: intro-/extraversion, agreeableness, conscientiousness, emotional stability, and openness. This data in turn became the input for a statistical analysis tool, correspondence analysis, which enabled us to group the respondents according to their personality traits and distinguish among different subgroups of art collectors to form more homogenous groups of art collectors based on the personality profiles. Due to the percentile scores of the output, we employed the use of a doubling technique to yield more valid results from the correspondence analysis. The doubling technique involved creating two points for each trait – the positive pole and the negative pole. This technique aided as a visual assessment for the grouping of art collectors into more homogenous groups based on personalities. The interpretation of the “doubled” points is effectively a visual assessment, for the total sample, of the distribution of the participant percentile scores for each of the five personality trait variables, which aided in grouping the respondents into distinct groups. Results showed four distinct groups of art collectors based on the relative personality profiles. The significance and implications of these results are discussed in regards to the four “types” of art collectors. Methodologically, this research addresses a means for marketing managers to uncover more insight into the psychographic traits of consumers.