A review of research on neuromarketing using content analysis: key approaches and new avenues

Robaina‐Calderín, Lorena; Santana, Josefa Delia Martín

doi:10.1007/s11571-021-09693-y

Cited by 15 publications

(8 citation statements)

References 72 publications

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“…While machine learning presents a promising avenue for neuromarketing research, care should be taken when designing machine learning models. First, due to the complex calculations made, machine learning requires more trials and computational power than traditional statistical models [ 136 , 222 ]. Machine learning models are also vulnerable to ‘overfitting’, where they may show high accuracy rates for the training data used, but perform poorly when predicting out-of-sample values.…”

Section: Discussionmentioning

confidence: 99%

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

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Introduction The present paper discusses the findings of a systematic review of EEG measures in neuromarketing, identifying which EEG measures are the most robust predictor of customer preference in neuromarketing. The review investigated which TF effect (e.g., theta-band power), and ERP component (e.g., N400) was most consistently reflective of self-reported preference. Machine-learning prediction also investigated, along with the use of EEG when combined with physiological measures such as eye-tracking. Methods Search terms ‘neuromarketing’ and ‘consumer neuroscience’ identified papers that used EEG measures. Publications were excluded if they were primarily written in a language other than English or were not published as journal articles (e.g., book chapters). 174 papers were included in the present review. Results Frontal alpha asymmetry (FAA) was the most reliable TF signal of preference and was able to differentiate positive from negative consumer responses. Similarly, the late positive potential (LPP) was the most reliable ERP component, reflecting conscious emotional evaluation of products and advertising. However, there was limited consistency across papers, with each measure showing mixed results when related to preference and purchase behaviour. Conclusions and implications FAA and the LPP were the most consistent markers of emotional responses to marketing stimuli, consumer preference and purchase intention. Predictive accuracy of FAA and the LPP was greatly improved through the use of machine-learning prediction, especially when combined with eye-tracking or facial expression analyses.

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Section: Discussionmentioning

confidence: 99%

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

et al. 2022

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“…To overcome this limitation, the discipline of neuromarketing, which combines neuroscientific techniques, psychology and traditional marketing, has recently been developed in the wine sector to understand the unconscious processes occurring in the minds of consumers (Cruz et al , 2016). The goal of neuromarketing is to establish a solid neuropsychological theory that allows us to understand consumer behavior by combining different neuroscientific methods and techniques specialized in visual attention and cognitive research (Robaina-Calderín and Martín-Santana, 2021). One of the main techniques is eye-tracking, which studies visual behavior (e.g.…”

Section: Introductionmentioning

confidence: 99%

Wine label design proposals: an eye-tracking study to analyze consumers’ visual attention and preferences

Barbierato

Berti

Ranfagni

et al. 2023

IJWBR

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Purpose The main purpose of this study is to analyze how consumers’ visual attention to wine label design correlates with their preferences. Accordingly, this study uses quantitative eye-tracking metrics to understand which design proposal has greater visual salience. A more specific objective was to assess which design proposal was preferred to be marketed. Design/methodology/approach The experiment involved evaluating of three different labeling proposals of an Italian winery. Infrared eye-tracking was used to measure implicit eye movements on the three bottles displayed, simultaneously, on a computer screen. A generalized linear model was used to test how consumers' visual attention to wine label design correlated with their preferences. Findings The design proposals were evaluated significantly differently, with one set being preferred. In general, a strong positive relationship was found between pausing to peruse a specific design proposal and making an explicit choice of the same bottle. Research limitations/implications The main limitation of the experiment concerns the sample interviewed. As the sample is homogeneous, the results may not be generalizable to other segments. Furthermore, the addition of electroencephalographic devices that monitor brain activity could provide crucial information for understanding consumer behavior during the purchase decision-making process. Practical implications Eye-tracking methods could be useful for designers and wine producers during the evaluation process of design projects. Originality/value The use of eye-tracking for evaluating design proposals before placing a product on the market is relatively novel. This method provides objective, quantitative and predictive information on consumer preferences contributing guidelines to designers and marketers during the product conception phase.

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“…In an effort to expand the transdisciplinary field of consumer neuroscience or neuromarketing, recent publications offer a comprehensive overview of the different applications of neuroscientific methods, research designs, and possible ethical issues (Lim, 2018b ). In addition, information on how data should be managed and processed using actual data specific to business research has widened the scope and boundaries of neuromarketing studies (Lim, 2018b ; Robaina-Calderin and Martin-Santana, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Did It Change Your Mind? Neural Substrates of Purchase Intention Change and Product Information

2022

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Price and customer ratings are perhaps the two most important pieces of information consumers rely on when shopping online. This study aimed to elucidate the neural mechanism by which the introduction of these two types of information influences the purchase intention of potential consumers for hedonic products. Participants performed a lip-care product shopping task during functional magnetic resonance imaging, in which they re-disclosed purchase intentions referring to the information of price or rating provided about the products that they had previously disclosed their purchase intentions without any information. Data from 38 young female participants were analyzed to identify the underlying neural regions associated with the intention change and product information. The bilateral frontopolar cortex, bilateral dorsal anterior cingulate cortex (dACC), and left insula activated higher for the unchanged than changed intention condition. The right dACC and bilateral insula also activated more toward the price than the rating condition, whereas the medial prefrontal cortex and bilateral temporoparietal junction responded in the opposite direction. These results seem to reflect the shift to exploratory decision-making strategies and increased salience in maintaining purchase intentions despite referring to provided information and to highlight the involvement of social cognition-related regions in reference to customer ratings rather than price.

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A review of research on neuromarketing using content analysis: key approaches and new avenues

Cited by 15 publications

References 72 publications

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

A systematic review of the prediction of consumer preference using EEG measures and machine-learning in neuromarketing research

Wine label design proposals: an eye-tracking study to analyze consumers’ visual attention and preferences

Did It Change Your Mind? Neural Substrates of Purchase Intention Change and Product Information

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