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透视森林:购买的凝视路径。

Seeing through the forest: The gaze path to purchase.

机构信息

Department of Horticulture, Michigan State University, East Lansing, Michigan, United States of America.

Department of Advertising & Public Relations, College of Communication Arts & Sciences, Michigan State University, East Lansing, Michigan, United States of America.

出版信息

PLoS One. 2020 Oct 9;15(10):e0240179. doi: 10.1371/journal.pone.0240179. eCollection 2020.

Abstract

Eye tracking studies have analyzed the relationship between visual attention to point of purchase marketing elements (price, signage, etc.) and purchase intention. Our study is the first to investigate the relationship between the gaze sequence in which consumers view a display (including gaze aversion away from products) and the influence of consumer (top down) characteristics on product choice. We conducted an in-lab 3 (display size: large, moderate, small) X 2 (price: sale, non-sale) within-subject experiment with 92 persons. After viewing the displays, subjects completed an online survey to provide demographic data, self-reported and actual product knowledge, and past purchase information. We employed a random forest machine learning approach via R software to analyze all possible three-unit subsequences of gaze fixations. Models comparing multiclass F1-macro score and F1-micro score of product choice were analyzed. Gaze sequence models that included gaze aversion more accurately predicted product choice in a lab setting for more complex displays. Inclusion of consumer characteristics generally improved model predictive F1-macro and F1-micro scores for less complex displays with fewer plant sizes Consumer attributes that helped improve model prediction performance were product expertise, ethnicity, and previous plant purchases.

摘要

眼动追踪研究分析了视觉注意力与购买点营销元素(价格、标识等)之间的关系以及购买意向。我们的研究首次调查了消费者观看展示的注视顺序(包括对产品的回避注视)与消费者(自上而下)特征对产品选择的影响之间的关系。我们进行了一项在实验室进行的 3(展示尺寸:大、中、小)X2(价格:销售、非销售)的被试内实验,共 92 人参与。观看展示后,被试者通过在线调查提供人口统计数据、自我报告和实际产品知识以及过往购买信息。我们使用 R 软件中的随机森林机器学习方法来分析所有可能的注视固定的三个单元子序列。通过比较多类 F1-宏评分和产品选择的 F1-微评分分析了模型。在更复杂的展示中,包含回避注视的注视序列模型更准确地预测了实验室环境中的产品选择。在具有较少植物尺寸的不太复杂的显示中,包含消费者特征通常会提高模型预测的 F1-宏和 F1-微评分。有助于提高模型预测性能的消费者属性是产品专业知识、种族和之前的植物购买。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/fd47/7546910/3c781ebbc5b6/pone.0240179.g001.jpg

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