Scholarlink

The article addresses the use of multimodal psychophysiological data (eye tracking, EEG) and machine learning to predict consumer choices of sales offers. Classification models were built, key areas of interest (AOI) of offers were identified, and the significance of metrics was assessed. The highest predictive quality was achieved using Random Forest with oversampling. The most important AOIs were those related to the external visualisation of houses and the layout of rooms. The results confirm the usefulness of integrating psychophysiological data and machine learning in marketing.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Predicting Consumer Decision-Making in Sales Offer Selection Using Psychophysiological Data and Machine Learning Methods

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Paweł Ziemba,
  • Mateusz Piwowarski,
  • Kesra Nermend

摘要

The article addresses the use of multimodal psychophysiological data (eye tracking, EEG) and machine learning to predict consumer choices of sales offers. Classification models were built, key areas of interest (AOI) of offers were identified, and the significance of metrics was assessed. The highest predictive quality was achieved using Random Forest with oversampling. The most important AOIs were those related to the external visualisation of houses and the layout of rooms. The results confirm the usefulness of integrating psychophysiological data and machine learning in marketing.