  Recent advancements in computational power and algorithms have enabled
unabridged data (e.g., raw images or audio) to be used as input in some models
(e.g., deep learning). However, the black box nature of such models reduces
their likelihood of adoption by marketing scholars. Our paradigm of analysis,
the Transparent Model of Unabridged Data (TMUD), enables researchers to
investigate the inner workings of such black box models by incorporating an ex
ante filtration module and an ex post experimentation module. We empirically
demonstrate the TMUD by investigating the role of facial components and sexual
dimorphism in face perceptions, which have implications for four marketing
contexts: advertisement (perceptions of approachability, trustworthiness, and
competence), brand (perceptions of whether a face represents a brand's typical
customer), category (perceptions of whether a face represents a category's
typical customer), and customer persona (perceptions of whether a face
represents the persona of a brand's customer segment). Our results reveal new
and useful findings that enrich the existing literature on face perception,
most of which is based on abridged attributes (e.g., width of mouth). The TMUD
has great potential to be a useful paradigm for generating theoretical insights
and may encourage more marketing researchers and practitioners to use
unabridged data.
