Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2025

Abstract

Predicting consumers’ purchase intention of browsed products enables sellers to implement nuanced promotion strategies to stimulate purchase. But how can we predict consumers’ purchase intention of browsed products? Our research demonstrates that consumers’ eye movement data collected when they browse products can serve this aim. We train and test the prediction model using logistic regression and random forest algorithms. Using data collected in a laboratory experiment, our empirical results show that both algorithms perform much better than a random guess, and the logistic regression performs slightly better than the random forest. Our findings imply that eye movement data enable sellers to predict consumers’ purchase intention of browsed products: the best model achieves a prediction performance of 71.862% using the Area Under the Receiver Operating Characteristic Curve as the evaluation criteria. Furthermore, the best predictor sets overlap, indicating the effectiveness of selected variables in predicting consumers’ purchase intention of browsed products.

Keywords

eye-tracking, browsed products, purchase intention, machine learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems | Sales and Merchandising

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

CSWIM 2025: 18th China Summer Workshop on Information Management, June 28-29, Xi'an: Proceedings

First Page

324

Last Page

329

Publisher

CSWIM

City or Country

China

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