Neuromarketing and consumer neuroscience: current understanding and the way forward

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques

Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.

Neural correlates of fanhood: the role of fan identity and team brand strength

Introduction

We analyzed the importance of fan identity and brand strength on fans' neural reactions to different team-related stimuli.

Methods

A total of 53 fMRI scans with fans of two professional sport teams were conducted. Following up on a previous study we focused on the differences between fandom levels as well as the contrast between two team "brand" strength. Neural responses were compared among individuals based on their levels of fan identity. In sum, group comparisons between relatively high and lower identity and between weak and strong teams were made based on the notion that the latter reflects team brand strength (strong brand and weak brand).

Results

Findings indicate that brain activity in emotion regulation, memory, and cognitive control circuits is influenced by the relative level of fan identity.

Discussion

Higher-level identified fans showed increased reactivity to positive stimuli and the under-recruitment of their cognitive appraisal circuits, suggesting more vulnerability to marketers' messages. The strength of the team brand activates different neural mechanisms. Interestingly, the posterior cingulate showed larger recruitment both for weaker brands and lower fan identification, suggesting that visual memory processes are more active in these cases. Neurally processed content depends on the relative brand's strength, highlighting the importance of brand-focused communications.

How does price variance among purchase channels affect consumers’ cognitive process when shopping online?

The rise of a flourishing online shopping market has expanded the range of purchase channels available to consumers. Meanwhile, the competition among channels has become increasingly fierce. In this study, the changes in cognitive processes caused by price variance among channels were investigated using event-related potentials. Several daily necessities with low or high price variance between a self-operated business channel and third-party seller channels were chosen as the study objects from a well-known electronic business platform. Thirty participants’ electroencephalograms were collected while they faced higher or lower price variance during the experiment. The results showed that small price variances between the two channels tended to intensify component N2, while big price variances tended to diminish component P3. These results suggest that N2 may reflect consumers’ identification process for price variance and inhibition of a planned response, while P3 may reflect the activation of attention caused by task difficulty due to price variance. These findings indicate that the changes in ERP components N2 and P3 may act as cognitive indices that measure customers’ identification and attention distribution when considering product price variances among online purchase channels.

An intelligent neuromarketing system for predicting consumers' future choice from electroencephalography signals

Neuromarketing utilizes Brain-Computer Interface (BCI) technologies to provide insight into consumers responses on marketing stimuli. In order to achieve insight information, marketers spend about $400 billion annually on marketing, promotion, and advertisement using traditional marketing research tools. In addition, these tools like personal depth interviews, surveys, focus group discussions, etc. are expensive and frequently criticized for failing to extract actual consumer preferences. Neuromarketing, on the other hand, promises to overcome such constraints. In this work, an EEG-based neuromarketing framework is employed for predicting consumer future choice (affective attitude) while they view E-commerce products. After preprocessing, three types of features, namely, time, frequency, and time-frequency domain features are extracted. Then, wrapper-based Support Vector Machine-Recursive Feature Elimination (SVM-RFE) along with correlation bias reduction is used for feature selection. Lastly, we use SVM for categorizing positive affective attitude and negative affective attitude. Experiments show that the frontal cortex achieves the best accuracy of 98.67±2.98, 98±3.22, and 98.67±3.52 for 5-fold, 10-fold, and leave-one-subject-out (LOSO) respectively. In addition, among all the channels, F_z achieves best accuracy 90±7.81, 90.67±9.53, and 92.67±7.03 for 5-fold, 10-fold, and LOSO respectively. Subsequently, this work opens the door for implementing such a neuromarketing framework using consumer-grade devices in a real-life setting for marketers. As a result, it is evident that EEG-based neuromarketing technologies can assist brands and enterprises in forecasting future consumer preferences accurately. Hence, it will pave the way for the creation of an intelligent marketing assistive system for neuromarketing applications in future.

BCI-Based Consumers' Choice Prediction From EEG Signals: An Intelligent Neuromarketing Framework

Neuromarketing relies on Brain Computer Interface (BCI) technology to gain insight into how customers react to marketing stimuli. Marketers spend about $750 billion annually on traditional marketing camping. They use traditional marketing research procedures such as Personal Depth Interviews, Surveys, Focused Group Discussions, and so on, which are frequently criticized for failing to extract true consumer preferences. On the other hand, Neuromarketing promises to overcome such constraints. This work proposes a machine learning framework for predicting consumers' purchase intention (PI) and affective attitude (AA) from analyzing EEG signals. In this work, EEG signals are collected from 20 healthy participants while administering three advertising stimuli settings: product, endorsement, and promotion. After preprocessing, features are extracted in three domains (time, frequency, and time-frequency). Then, after selecting features using wrapper-based methods Recursive Feature Elimination, Support Vector Machine is used for categorizing positive and negative (AA and PI). The experimental results show that proposed framework achieves an accuracy of 84 and 87.00% for PI and AA ensuring the simulation of real-life results. In addition, AA and PI signals show N200 and N400 components when people tend to take decision after visualizing static advertisement. Moreover, negative AA signals shows more dispersion than positive AA signals. Furthermore, this work paves the way for implementing such a neuromarketing framework using consumer-grade EEG devices in a real-life setting. Therefore, it is evident that BCI-based neuromarketing technology can help brands and businesses effectively predict future consumer preferences. Hence, EEG-based neuromarketing technologies can assist brands and enterprizes in accurately forecasting future consumer preferences.

Effectiveness of Electricity-Saving Communication Campaigns: Neurophysiological Approach

Public communication campaigns are among the tools for promoting electricity saving. A crucial task in the process of creating a campaign is to design a simple message to effectively reach the average consumer. It is a beneficial practice to create alternative messages and pretest them to find the most effective. The research methodology during pretesting includes both quantitative and qualitative methods. However, it is believed that the outcomes obtained with the use of conventional techniques are not fully reliable. Therefore, the following question arises: What additional research methods should be applied at the stage of testing the message of a communication campaign so that its effectiveness can be assessed more reliably and/or improved even before its broadcast? In this study, we aim to present the possibility of applying cognitive neuroscience methods in conjunction with a questionnaire to experimentally check the effectiveness of the message using the example of selected electricity-saving communication campaigns. The key results of this study indicate that merging conscious and subconscious reactions to media messages allows us to gain new knowledge that can be used in the future to improve the communication campaign effectiveness. Our investigation showed the benefits that can be obtained by synergizing traditional research methods with neuroscientific approaches.

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

There is currently a growing interest in a deeper understanding of consumer behaviour. In this context, the union of different disciplines such as neuroscience and marketing has given birth to new fields of knowledge, e.g. neuromarketing. This study is mainly aimed at carrying out a systematic revision of the literature on neuromarketing from a holistic point of view, analysing its definition and processes, as well as more specific aspects such as its ethics and applications. Based on the results of our review, following a combined methodology with a base dictionary and text mining, our study presents both the current lines of research and the future lines of work.

Consumer Neuroscience as a Tool to Monitor the Impact of Aromas on Consumer Emotions When Buying Food

Building a unique USP sales argument (unique selling proposition) through various forms of in-store communication comes to the fore in a challenging competitive environment. Scent as a means to influence the purchase of goods or services has a long history, however, aromachology as field of in-store communication is a matter of the present. This new trend, the importance and use of which has grown in recent years, is the subject of a wide range of research. In order to increase the efficiency of these elements, it is necessary to familiarise ourselves with the factors that affect the customer, whether that be consciously or unconsciously. Consumer neuroscience is addressed in this area. This paper deals with the comprehensive interdisciplinary investigation of the impact of selected aromatic compounds on consumer cognitive and affective processes as well as assessing the effectiveness of their implementation in food retail operations. At the end of the paper, we recommend options for the effective selection and implementation of aromatisation of different premises, by which the retailer can achieve not only a successful form of in-store communication, but also an increase the retail turnover of the store.

How Do Reference Points Influence the Representation of the N200 for Consumer Preference?

Recent studies have suggested that event-related brain potential (ERP) can represent consumer preference, and there is consensus that the N200 is the best indicator of consumer preference. Measurement of reference-dependent consumer preference, in turn, requires a reference point, but it remains largely unknown how reference points modulate the preference-related N200. We designed an experiment to investigate how reference points affect the N200 based on classical paradigms. In the single-reference condition, one product was displayed in each trial; in the conjoined-reference condition, a pair of products was displayed simultaneously. Our results showed that in the single-reference condition, low-preference products elicited more negative N200 than high-preference products, replicating previous results, but the N200 could not distinguish between low‐ and high-preference products when viewing two options of similar subjective value in the conjoined-reference condition. These findings suggest that reference points modulate the representation of the N200 on consumer preference. When only viewing one product, participants make a value judgment based on their expectations. However, when viewing two products simultaneously, both their expectation and the alternative product can serve as reference points, and whether the N200 can represent consumer preference depends on which reference point is dominant. In future research, reference points must be controlled when the N200 is used to explore value-related decision-making.

Neuromarketing and Improved Understanding of Consumer Behaviour through Brain-Based Neuro Activity

Over the past three decades, there has been a growing interest in studying consumer behaviour directly through non-traditional, brain-based, approach using the basic knowledge of human neuroscience. This multidisciplinary approach has evolved into a new marketing branch, known as Neuromarketing, which goes inside the human brain to improve our knowledge of consumer behaviour. Neuromarketing traces neural circuit activities inside the brain using Magnetic Resonance Imaging (MRI) technology. This paper explores the existing literature on Neuromarketing to provide insights into the potential for improving our understanding of consumer behaviour. The paper concludes that Neuromarketing can offer a valuable opportunity to increase precision and validity of measuring consumer reactions to marketing activities, thus improve marketing knowledge of consumer choice behaviour. The paper also addresses the main ethical issues raised by critiques on the unprecedented access to consumers’ mind, and how advocates looked at such criticisms.

Deep Learning for EEG-Based Preference Classification in Neuromarketing

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.