Neuromarketing - Predicting commercial success


Consumer neuroscience – applying neuroscience methods to marketing issues – has gained considerable popularity in recent years amongst scholars and practitioners alike (Yoon et al., 2012). 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 marketing response 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 behavior 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.

Recently the potential for brain activity to improve predictions of aggregate level behavior has motivated a new stream of research on neural forecasting. In their recent study Genevsky et al. use neural activity collected via fMRI 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 behavioral 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.

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