Integrating Eye-Tracking, Machine Learning, and Facial Recognition for Objective Consumer Behavior Analysis

Abstract

This pioneering study presents a model that integrates eye-tracking, OpenCV-based computer vision (CV), and machine learning (ML) to evaluate consumer interest in products with greater accuracy and objectivity. Unlike traditional self-reported surveys, often biased by factors such as brand identity, this approach uses data-oriented metrics to directly measure user engagement.

Eye-tracking captures the distribution and duration of visual attention during product interaction, while OpenCV handles essential image-processing tasks—such as detection and localization—allowing ML algorithms to perform the core facial recognition and demographic classification steps. This integration enables more refined market segmentation and targeted marketing strategies, aligned with the diverse preferences of consumers.

Preliminary results suggest that this integrated methodology provides a more authentic representation of user preferences compared to conventional methods, which are often influenced by biases or social norms. By capturing objective behavioral and demographic indicators, the model offers reliable insights that allow companies to optimize product features, improve marketing campaigns, and effectively direct development efforts.

In a competitive marketplace, the use of these technologies facilitates more informed decision-making and long-term strategic advantages. This study highlights the transformative potential of combining eye-tracking, ML, and facial recognition in market analysis. By moving beyond opinion-based methods to real-time, quantifiable insights, companies can better understand and meet consumer expectations, promoting their satisfaction and achieving sustainable success.

M. Llamas-Nistal—Senior Member, IEEE