Describing Rosé: An Embedding-Based Method for Measuring Preferences

Abstract

Many products and services are best (and typically) described in prose. In extant preference-measurement methods, however, due to the challenge of numerically representing prose in econometric models, products can only be described to participants and portrayed in the utility model as a list of attributes. In this research, the authors develop an embedding-based utility model and preference method that addresses this limitation; in it, products are described to participants in (unstructured) prose. The proposed method provides three benefits: (1) in it, products can be described more completely, (2) it improves study realism, and (3) it enables a more detailed measurement of preferences. The authors employ the proposed method to measure consumer preferences in Australia, New Zealand, and the United States for wines made in 427 wine-growing regions in 44 wine-growing countries, from 708 wine-grape varietals. They find the proposed model has superior in-sample fit and generates better out-of-sample predictions than benchmark models. Importantly, the method is able to capture differences in consumers’ valuation for wines (products) that are observationally equivalent in categorical attributes, and therefore indistinguishable in classical categorical variable-based analysis. The use of the proposed model as a decision support system for marketing activities is demonstrated.