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Seeing through the forest: The gaze path to purchase.

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Consumer gaze patterns, including looking away from products, predict purchase choices, especially for complex displays. Consumer characteristics like expertise also influence choices, particularly for simpler displays.

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Area of Science:

  • Consumer behavior research
  • Marketing science
  • Cognitive psychology

Background:

  • Previous eye-tracking studies linked visual attention to point-of-purchase marketing elements with purchase intention.
  • Limited research explored the impact of gaze sequences and consumer-specific factors on product choice.

Purpose of the Study:

  • To investigate the relationship between gaze sequences (including gaze aversion) and consumer characteristics on product choice.
  • To determine how display complexity influences the predictive power of gaze sequences and consumer attributes.
  • To identify specific consumer attributes that enhance prediction of product choice.

Main Methods:

  • An in-lab experiment with 92 participants examined responses to varying display sizes and pricing (sale/non-sale).
  • Eye-tracking data captured gaze sequences, including gaze aversion, during display viewing.
  • Random forest machine learning models analyzed three-unit gaze fixation subsequences and consumer data (demographics, product knowledge, purchase history).

Main Results:

  • Gaze sequence models incorporating gaze aversion demonstrated higher accuracy in predicting product choice for more complex displays.
  • Incorporating consumer characteristics generally improved predictive model performance for less complex displays.
  • Product expertise, ethnicity, and prior purchase history were key consumer attributes that enhanced prediction accuracy.

Conclusions:

  • Gaze aversion within a consumer's viewing sequence is a significant predictor of product choice, particularly in complex retail environments.
  • The influence of consumer-specific attributes on purchase decisions is moderated by display complexity.
  • Integrating eye-tracking data with consumer characteristics offers a more nuanced understanding of in-store decision-making processes.