The Brain as Predictor
Consumer neuroscience – applying neuroscience methods and insights to marketing issues – has gained considerable popularity in recent years amongst scholars and practitioners alike (Smidts et al., 2014). As noted by Ariely & Berns (2010), there appears to be good reason for this enthusiasm: brain data is considered less noisy than data obtained through conventional marketing methods. It is thought that data from smaller samples can generate more accurate predictions, making neuroscience methods cheaper and faster than traditional methods.
Although much progress has been made relating brain activity to choice behaviour, evidence that neural measures could actually be useful for predicting market-level responses remains limited. To be of added value, neural measures should significantly increase the accuracy of predicting consumer choices, above and beyond conventional measures.
In this line of research, we set out to investigate this possibility. We obtain both stated preference measures and/or measures of actual purchase behaviour from consumers in combination with neural measures (electroencephalography; EEG or functional magnetic resonance imaging; fMRI) in response to advertisements for commercially released products, to probe their potential to predict individual preferences and sales in the population at large.
Examples of applications concerned the prediction of advertising recall (Chan et al., 2019), commercial success of movies (Boksem & Smidts, 2015), video engagement (Tong et al, 2020), and microlending (Genevsky & Knutson, 2015).
Recently the potential for brain activity to improve predictions of aggregate level behaviour has motivated a new stream of research on neural forecasting. For example, in a study by Genevsky et al. (2017) neural activity collected via fMRI is used to forecast real-world crowdfunding outcomes. They find that neural activity can forecast market success months later and, in fact surpasses predictions made using traditional behavioural methods, such as self-report ratings. In addition to demonstrating the plausibility of neural forecasting, these findings suggest a new perspective on how individual choice might scale to the aggregate level (Knutson & Genevsky, 2018).
Speer, S.P.H., Smidts A., Boksem, M.A.S. (2020). Individual differences in (dis)honesty are represented in the brain’s functional connectivity: Robust out-of-sample prediction of cheating behavior. BioRXiv, doi:10.1101/2020.05.12.091116, preprint
Tong, L., Acikalin, Y., Genevsky, A., Shiv, B., Knutson, B. (2020). Brain activity forecasts video engagement in an internet attention market. PNAS, 117(12), 6936-6941
Chan H.Y., Smidts A., Schoots V.C., Dietvorst R.C. & Boksem M.A.S. (2019). Neural similarity at temporal pole and cerebellum predicts out-of-sample preference and recall for video stimuli. Neuroimage, 197, 391-401. doi: 10.1016/j.neuroimage.2019.04.076
Knutson, B., Genevsky, A., (2018) Neuroforecasting aggregate choice. Current Directions in Psychological Science 27(2), 110-115
Genevsky, A., Yoon, C., Knutson, B., (2017) When brain beats behavior: Neuroforecasting crowdfunding outcomes, Journal of Neuroscience 37(36), 8625-8634
Boksem, M.A.S. & Smidts, A., (2015). A. Brain responses to movie-trailers predict individual preferences for movies and their population-wide commercial success. Journal of Marketing Research 52(4), 482-492.
Genevsky, A., Knutson, B. (2015). Neural affective mechanisms predict market-level microlending. Psychological Science, 26 (9), 1411-1422. doi: 10.1177/0956797615588467
Smidts, A., Hsu, M., Sanfey, A.G., Boksem, M.A.S., Ebstein, R.B., Huettel, S.A., Kable, J.W., Karmarkar, U.R., Kitayama, S., Knutson, B., Liberzon, I., Lohrenz, T., Stallen, M., Yoon, C. (2014). Advancing consumer neuroscience. Marketing Letters, 25(3), 257-267.
Van Diepen, R.M., Boekel, W.E., Eijlers, E., Smidts, A., Boksem, M.A.S. The brain on movies revisited: does EEG predict box office? (working paper)
Couwenberg, L.E., Boksem, M.A.S., Dietvorst, R.C., Worm, L., Verbeke, W.J.M.I. & Smidts, A. (2017). Neural Responses to Functional and Experiential Ad Appeals: Explaining Ad Effectiveness. International Journal of Research in Marketing, 34 (2017) 355-366