Experiential Conjoint Analysis: An Experience-Based Method for Eliciting, Capturing, and Modeling Consumer Preference

Abstract

Traditionally, consumer preference is modeled in terms of preference for the aesthetic and functional features of a product. This paper introduces a new means to model consumer preference that accounts for not only for how a product looks and functions but also how it feels to interact with it. Traditional conjoint-based approaches to preference modeling require a participant to judge preference for a product based upon a static 2D visual representation or a feature list. While the aesthetic forms and functional features of a product are certainly important, the decision to buy or not to buy a product often depends on more, namely, the experience or feel of use. To address the importance of the product experience, we introduce the concept of experiential conjoint analysis, a method to mathematically capture preference for a product through experience-based (experiential) preference judgments. Experiential preference judgments are made based upon the use, or simulated use, of a product. For many products, creating enough physical prototypes to generate a preference model is cost prohibitive. In this work, virtual reality (VR) technologies are used to allow the participant an interactive virtual product experience, provided at little investment. The results of this work show that providing additional interaction-based (interactional) information about a product through a product experience does not affect the predictive ability of the resulting preference models. This work additionally demonstrates that the preference judgments of virtual product representations are more similar to preference judgments of real products than preference judgments of 2D product representations are. When examining similarity of modeled preference, experiential conjoint is found to be superior to visual conjoint with respect to mean absolute error (MAE), but with respect to correlation no significant difference between visual and experiential is found.

Impossible by design? Fairness, strategy, and Arrow’s impossibility theorem

“…Capturing and modelling preference is an active area of research in the engineering design community, and has seen significant growth over the past decade (Hoyle & Chen 2011;Kelly et al 2011;Petiot & Grognet 2006;Reid et al 2013;Sylcott et al 2013;Orsborn et al 2009;Tovares et al 2014). In particular, conjoint and discrete choice analyses (Train 2003) have been popular choices, due to the ability of these methods to allow for decomposition of a product into a set of discrete or continuous attributes, from which a mathematical model of preferences for these attributes can then be determined using a number of techniques.…”

“…Beyond visual conjoint analysis, Tovares, Cagan, and Boatwright (2014) developed experiential conjoint analysis based upon experience based preference judgments (touching, manipulating, etc. ), where again preference could be extrapolated to any point within the design space.…”

“…For this analysis, 3D printed ceramic drinking mugs were used as a stimulus to determine individual preferences (Tovares et al 2014). The mugs were 3D printed in accordance with a predefined experimental design.…”

“…Taking advantage of the continuous nature of the attributes employed in this study, differentiable quadratic utility functions were used to model preference. Quadratic utility functions were chosen to allow for maxima that were not corner solutions, and to remain consistent with prior work (Orsborn et al 2009;Tovares et al 2014). …”

“…First, experiential conjoint analysis was used to query real preferences for a class of products (Tovares et al 2014). Then, using these empirical preferences, simulated voting scenarios were constructed and analysed to determine the probability with which certain aggregation functions (namely plurality, veto, Borda, instant runoff voting, and Copeland) violate specific conditions of Arrow's theorem.…”