Traditional preference mapping and computational machine learning techniques: A comparative study of approaches to guide product development

Abstract

Preference mapping, a well-known set of multivariate statistical techniques, has become widely adopted due to its demonstrated effectiveness as a powerful tool in guiding the development of new products and enhancing existing ones. Recent advancements in open-source software and computational capabilities have introduced a new set of accessible tools with the potential to address limitations associated with traditional methods. This study introduces an alternative algorithm for building predictive models, employing regularized regression in combination with Multivariate Adaptive Regression Spline (MARS). These methods make fewer assumptions about the relationship between predictors and the target and can easily capture complex and non-linear relationships. Additionally, the study presents a robust and systematic alternative approach for calculating optimum profiles and performing simulations. The paper aims to compare this new set of tools, referred to as computational machine learning techniques, with a well-established and widely recognized method − PrefMap based on Partial Least Squares Regression. The primary intention of the comparison between computational machine learning and one example of a traditional approach is not to determine a winning methodology, but rather to enhance awareness and deepen the understanding of this emerging family of models and techniques now available to sensory and consumer scientists. Results are assessed side by side to reveal their similarities and differences in terms of predictive power, drivers of liking, and the optimal profile aspects, and a list of practical considerations is provided at the end, enabling a better understanding of the trade-offs between the two approaches presented here.