The Value of Genetic Data in Predicting Preferences: A Study of Food Taste

Abstract

The exponential expansion of consumer genetic testing has led to an accumulation of massive genomic data sets owned by governments and firms. The prospect of leveraging genetic data for enhancing consumers' health, well-being, and satisfaction through improved personalization, segmentation, and targeting is promising. Nonetheless, this potential has not been studied empirically to date, and it is unknown whether and when firms should invest resources into incorporating genetic data into strategies and processes. The authors address this gap in a study of taste preferences, important drivers of food and beverage consumption. Using a large U.K.-based sample, they find that with sample sizes currently available, genetic data are expected to significantly improve prediction of taste preferences above traditionally used metrics such as demographics, behavioral variables, and even past consumption, especially for tastes that are uncommon in the local diet (e.g., spicy, sour), as they are less expressed behaviorally. The authors conclude that genetic data show immense promise for prediction-based applications when other data sources are limited or uninformative. These findings could have significant implications for public health initiatives, potentially aiding development of personalized nutrition plans and dietary interventions.