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A Ticket for Your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers

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  • Samuel B. Barnett
  • Moran Cerf

Abstract

Skilled advertisers often cause a diverse set of consumers to feel similarly about their product. We present a method for measuring neural data to assess the degree of similarity between multiple brains experiencing the same advertisements, and we demonstrate that this similarity can predict important marketing outcomes. Since neural data can be sampled continuously throughout an experience and without effort and conscious reporting biases, our method offers a useful complement to measures requiring active evaluations, such as subjective ratings and willingness-to-pay (WTP) scores. As a case study, we use portable electroencephalography (EEG) systems to record the brain activity of 58 moviegoers in a commercial theater and then calculate the relative levels of neural similarity, cross-brain correlation (CBC), throughout 13 movie trailers. Our initial evidence suggests that CBC predicts future free recall of the movie trailers and population-level sales of the corresponding movies. Additionally, since there are potentially other (i.e., non-neural) sources of physiological similarity (e.g., basic arousal), we illustrate how to use other passive measures, such as cardiac, respiratory, and electrodermal activity levels, to reject alternative hypotheses. Moreover, we show how CBC can be used in conjunction with empirical content analysis (e.g., levels of visual and semantic complexity).

Suggested Citation

  • Samuel B. Barnett & Moran Cerf, 2017. "A Ticket for Your Thoughts: Method for Predicting Content Recall and Sales Using Neural Similarity of Moviegoers," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(1), pages 160-181.
  • Handle: RePEc:oup:jconrs:v:44:y:2017:i:1:p:160-181.
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    File URL: http://hdl.handle.net/10.1093/jcr/ucw083
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    Citations

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    Cited by:

    1. Mateusz Piwowarski & Katarzyna Gadomska-Lila & Kesra Nermend, 2021. "Cognitive Neuroscience Methods in Enhancing Health Literacy," IJERPH, MDPI, vol. 18(10), pages 1-19, May.
    2. Rumpf, Christopher & Boronczyk, Felix & Breuer, Christoph, 2020. "Predicting consumer gaze hits: A simulation model of visual attention to dynamic marketing stimuli," Journal of Business Research, Elsevier, vol. 111(C), pages 208-217.
    3. Jordi McKenzie, 2023. "The economics of movies (revisited): A survey of recent literature," Journal of Economic Surveys, Wiley Blackwell, vol. 37(2), pages 480-525, April.
    4. Waymond Rodgers & Tam Nguyen, 2022. "Advertising Benefits from Ethical Artificial Intelligence Algorithmic Purchase Decision Pathways," Journal of Business Ethics, Springer, vol. 178(4), pages 1043-1061, July.
    5. Shane, Scott & Drover, Will & Clingingsmith, David & Cerf, Moran, 2020. "Founder passion, neural engagement and informal investor interest in startup pitches: An fMRI study," Journal of Business Venturing, Elsevier, vol. 35(4).
    6. Zhiwei Xu & Siqi Liu, 2024. "Decoding consumer purchase decisions: exploring the predictive power of EEG features in online shopping environments using machine learning," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    7. Qiang Zhang & Wenbo Wang & Yuxin Chen, 2020. "Frontiers: In-Consumption Social Listening with Moment-to-Moment Unstructured Data: The Case of Movie Appreciation and Live Comments," Marketing Science, INFORMS, vol. 39(2), pages 285-295, March.
    8. Ronny Behrens & Natasha Zhang Foutz & Michael Franklin & Jannis Funk & Fernanda Gutierrez-Navratil & Julian Hofmann & Ulrike Leibfried, 2021. "Leveraging analytics to produce compelling and profitable film content," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 45(2), pages 171-211, June.
    9. Hakim, Adam & Klorfeld, Shira & Sela, Tal & Friedman, Doron & Shabat-Simon, Maytal & Levy, Dino J., 2021. "Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning," International Journal of Research in Marketing, Elsevier, vol. 38(3), pages 770-791.
    10. Aroa Costa-Feito & Ana M. González-Fernández & Carmen Rodríguez-Santos & Miguel Cervantes-Blanco, 2023. "Electroencephalography in consumer behaviour and marketing: a science mapping approach," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-13, December.
    11. Anna Borawska & Malgorzata Latuszynska, 2020. "Incorporating Neuroscience Data into Agent-Based Simulation Models of Buyer Behavior," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 1197-1212.

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