Integrating Textual Information into Models of Choice and Scaled Response Data
Authors
Published
Marketing Science,
January (1st Quarter/Winter)
2022
Abstract
This paper proposes a new approach to modeling heterogeneity in choice data that can accommodate fixed-point ratings data and text. Respondent choices, survey responses, and narratives are combined to form latent archetypes that provide an integrated description of respondents in terms of the objects and drivers of their wants. We propose a measure of coherence to assess the value of integrating these data elements and demonstrate the value of integrating text data into an analysis of choice and scaled response data. A conjoint data set is used to illustrate the model where we find that the text data helps clarify the origin of demand.
