neuromarketing thesis topics

Accessing Neuromarketing Scientific Performance: Research Gaps and Emerging Topics

Lucília cardoso.

1 CiTUR-Leiria, 2411-901 Leiria, Portugal; [email protected] (L.C.); [email protected] (F.D.)

Meng-Mei Chen

2 Ecole Hoteliere de Lausanne, HES-SO University of Applied Sciences and Arts Western, 2800 Delémont, Switzerland; [email protected]

Arthur Araújo

3 Transdisciplinary Research Center on Innovation & Entrepreneurship Ecosystems (TRIE), Lusofona University, 1749-024 Lisboa, Portugal; tp.plu.563osm@6075p

Giovana Goretti Feijó de Almeida

Francisco dias, luiz moutinho.

4 Visiting Professor of Marketing, Suffolk Business School, University of Suffolk, Suffolk IP4 1QJ, UK; moc.liamg@ohnituommaziul

(1) Background: Using neuroscience to understand and influence consumer behavior often leads to ethical controversy. Thus, it is necessary to demystify the use of neuroscience for marketing purposes; the present paper, by accessing the worldwide academic performance in this domain, fulfills this objective. (2) Methods: All extant literature on neuromarketing indexed to the Scopus database—318 articles—was subjected to a bibliometric analysis through a mixed-method approach. (3) Results: The results show that Spain leads the ranks of the most productive countries, while Italian researchers clearly dominate in terms of collaboration. Regarding the most prominent topics, the connection between “Neuroscience” and “Advertising” is highlighted. The findings provide a better understanding of the state-of-the-art in neuromarketing studies, research gaps, and emerging research topics, and additionally provide a new methodological contribution by including SciVal topic prominence in the bibliometric analysis. (4) Conclusions: As practical implications, this study provides useful insights for neuromarketing researchers seeking funding opportunities, which are normally associated with topics within the top prominence percentile or emerging topics. In terms of originality, this study is the first to apply SciVal topic prominence to a bibliometric analysis of neuromarketing, and provides a new bibliometric indicator for neuromarketing research.

1. Introduction

As a research field, neuromarketing emerged from the application of neuroscience methods and techniques for marketing purposes, i.e., consumer behavior as a response to a certain stimulus [ 1 ]. In this context, neuromarketing is an interdisciplinary domain with great of potential, as it allows researchers to understand and predict consumer choices and behavior. Therefore, it can lead to new marketing theories and good practices. However, the fulfillment of such potential is often limited by a mystified view of the use of neuroscience methods by marketing researchers. In order to mitigate this problem, Ref. [ 2 ] mapped the extant literature on neuromarketing, documenting the different methods employed and identifying the main researchers on the topic.

Mapping scientific literature through bibliometric studies is a useful method of providing an assessment of scientific production in a specific area over a given period [ 3 ]. Bibliometric studies allow for the assessment of research, including the themes sought, methods employed, and samples used. In this context, Koseoglu [ 4 ] argues that bibliometric studies highlight unknown patterns in research fields or disciplines, helping researchers to develop theories and test hypotheses, concluding that “these structural, dynamic, evaluative, and predictive models can be skillfully applied in the evaluation and prediction of a field” (p. 191).

Aware of this potential, several other studies have mapped scientific production on neuromarketing. Amongst these, Alsharif et al. [ 5 ] analysed studies indexed to the Web of Science database published between 2007 and 2019. In addition to being limited to this database and time period, the study did not include a key-theme analysis. Sousa and Lara [ 6 ], in turn, conducted a bibliometric study to identify the technologies applied in neuromarketing. This study, however, did not include articles indexed in the Scopus database. Finally, Wannyn [ 7 ] analysed studies from Web of Science covering the 2004–2015 period. In sum, to the best of the authors’ knowledge no previous work has mapped neuromarketing research production in the Scopus database. Moreover, no studies dealing with the mapping of co-word analysis and SCival topic prominence in neuromarketing were found. SCival topic prominence is provided by Scopus and combines three metrics that indicate the momentum of a topic: (1) Citation count in year n of papers published in n and n − 1; (2) Scopus view count in year n to papers published in n and n − 1; and (3) Average CiteScore for year n [ 8 , 9 , 10 ].

To contribute to the filling of these research gaps, the present study aims to assess the global performance of neuromarketing studies. To achieve this general goal, the following specific objectives were adopted:

(1) To determine the overall performance of neuromarketing research;
(2) To determine the performance of the most cited articles;
(3) To identify the most prominent topics within the neuromarketing field;
(4) To identify the prominent Scival topics on neuromarketing; and
(5) To identify the best Scival topics on neuromarketing by percentile.

In order to fulfill these specific objectives, all of the extant literature on neuromarketing indexed to the Scopus database was analyzed. Said body of literature included a total of 318 articles published during the 2007–2020 period. The retrieved articles were subjected to a bibliometric analysis through a mixed-method approach, which encompassed a key-word analysis and a SCival topic prominence analysis. The findings revealed the overall structure of the neuromarketing topic as well as research gaps and emerging research areas, which is useful for both researchers and research institutions. Moreover, the authors with highest research performance were identified, and collaboration patterns were revealed.

Regarding the topic’s overall performance, the findings point to the most productive journals, countries, research institutions, and researchers, and provide a general picture of global collaboration. In terms of the most cited articles, the findings highlight the top ten papers in terms of views and citation, amongst which, as in other emergent topics, introductory works and systematic reviews are dominant. In terms of the most prominent topics, the findings point to the connection between “Neuroscience”, “Advertising”, “Television”, and several topics related to neuroscience techniques. The relationship between these topics is addressed in more detail in the conclusions. Finally, regarding the top 1% percentile, the findings show that these studies consist of audience response studies, methodology enhancement studies, and literature reviews. These findings provide a general picture of the most promising topics in neuromarketing, which is particularly useful for individual researchers and research centers aiming to increase their own impact factor and maximize their chances of obtaining funding for their projects.

2. Mapping Neuromarketing Science

2.1. bibliometric studies on neuromarketing.

Neuroscience methods use tools and techniques to measure, map and record brain or neural activity. In doing so, they generate neurological representations of this activity, which allows scientists to understand specific responses in the brain and nervous system upon exposure to a certain stimulus [ 1 ]. Due to their effectiveness in explaining human behavior, these methods are employed in several research fields. Recently, they have begun to be applied in order to “analyze and understand human behavior related to markets and marketing exchange” [ 11 ] (p. 3803), originating a new research field: neuromarketing [ 12 ]. In this context, neuromarketing is an interdisciplinary domain in which theories and methods from neuroscience and marketing, as well as from related disciplines such as economics, psychology and tourism, are combined in order to understand consumer choices and behavior. Neuromarketing is thus a promising field, as findings in the area can lead to new foundations and generate new marketing theories or complement existing ones [ 2 ]. The majority of neuromarketing studies focus on emotions, which play an important role in all human activities. Emotions relate to various affective states, both positive and negative [ 13 ], as well as to the psycho-behavioral aspects of consumers.

As argued by Lim [ 2 ], “neuromarketing, as a method of investigation, is important because it uses neuroscientific theories and methods to gain access to otherwise hidden information” (p. 1). However, the same author argues that neuromarketing needs to be demystified. Although there are many conceptual works and several bibliographical reviews on the topic, few studies have rigorously produced empirical findings. Therefore, Lim [ 2 ] points out a lack of effectiveness in the use of neuroscience measurement techniques to advance marketing theory and address ethical issues. Moreover, neuroscience can intimidate academics due to lack of knowledge regarding the data-gathering methods it uses. In this context, Lim [ 2 ] attempts to enhance understanding of the potential of neuromarketing by documenting the different neuroscientific methods used in neuromarketing, identifying the main researchers on the topic, and addressing their main contributions. In other words, the study mapped the extant literature on neuromarketing through a bibliometric study, the same method employed in the present study. Therefore, the next section is dedicated to addressing the main fundaments and contributions of the application of bibliometric methods to the mapping of research topics.

2.2. Mapping Research Topics through Bibliometric Studies

Bibliometric studies can analyze several aspects of a body of research, including the productivity of individual scholars and institutions, knowledge flow and social networks, topics’ long-term development trends and emerging topics, journal rankings and journal development, and the most frequently cited scholars and works [ 14 ]. Koseoglu et al. [ 4 ] and Huang et al. [ 15 ] identified three types of bibliometric methods: relational techniques, evaluative techniques, and review studies. Relational techniques explore relationships within research, such as the structure of research fields, the emergence of new research themes and methods, and co-citation and co-authorship patterns. These can be divided into four categories: co-citation analysis, co-word analysis, co-authorship analysis, and bibliographic coupling [ 4 ] (p. 182). Evaluative techniques assess the impact of scholarly work, usually aiming to compare the performance or scientific contributions of two or more individuals or groups. To this end, evaluative techniques employ productivity measures, impact metrics, and hybrid metrics. Finally, review studies simply perform thorough literature reviews on a given topic. Through these three sets of techniques, bibliometric studies allow researchers to analyze research topics and the publications of individuals and/or institutions, as well as to map the structure and dynamics of science [ 15 ]. In sum, they serve to assess research performance.

Research performance is characterized by metrics that measure certain variables considered to define academic excellence. The term is associated with many programs and departments of institutions that are recognized as having high-quality research output [ 16 ]. Quantitatively, research performance is assessed by a range of metrics such as citations per article [ 17 ] and other citation metrics [ 18 ], cooperation indicators [ 19 ], and institutional contribution [ 20 ]. Additionally, it can be assessed through qualitative performance aspects [ 21 ].

When applied to a country, research performance can be defined by the performance of its institutions, which in turn depends on the performance of its researchers and is frequently conveyed through rankings (i.e., authors, affiliations, journal scores, number of articles published) [ 22 , 23 ]. More recently, research productivity has been closely associated with collaboration between authors and institutions [ 23 ], which has been shown to be a tool for knowledge creation, acquisition, and dissemination [ 24 ]. In this context, techniques focused on cooperation, such as network analysis, have been incorporated into bibliometric studies.

Adopting a broader approach to bibliographic analysis, the resent studies of Cardoso et al. [ 8 , 9 , 10 ], and Lima Santos et al. [ 25 ] have added a qualitative aspect to the assessment of research performance. Specifically, these authors combined traditional quantitative variables with analysis of the pioneering authors in a scientific area or research topic, the consistency of research over time, the number of papers with first authorship, and Scival topic prominence (including top quartiles).

In traditional bibliometrics, when authors use citation analysis to assess the impact of an article (e.g., when analysing an author’s or a university’s performance), the main indicator applied is the average number of citations per publication. In topic prominence analysis, the indicator applied is percentile-based [ 26 ]; that is, the higher the percentile, the greater the impact of the article [ 27 ]. In this context, the percentile reflects “the proportion of frequently cited publications, for instance, the proportion of publications that belong to the top 10% most frequently cited of their field” [ 28 ] (p. 372). Scival topic prominence is used to make predictions regarding a field of research. This new research indicator emerges from technology-related areas, and has been employed in the analysis of social science and business studies (i.e., tourism and hospitality) by Cardoso et al. [ 8 , 9 , 10 ]. By identifying research topics and capturing emerging subjects/topics in a specific area, topic prominence acts as a new performance indicator. It is useful in identifying whether a research topic is growing or declining, predicting whether it will grow or decline in the future, and indicating emerging topics of research [ 26 ]. In this context, according to Elsevier Publisher [ 29 ], topic prominence is considered to be a pointer that explains the prevailing momentum of a topic.

Moreover, as it indicates the impact of a paper, article or journal, topic prominence is a useful tool for researchers seeking funding. As argued by Wang and Shapira [ 30 ], high-impact articles (i.e., articles positioned in 90th and 95th percentiles) are far more likely to be associated with acknowledged funding when compared with low-impact articles. Therefore, topic prominence is normally used to support research funding [ 26 ]. Moreover, institutions seek to position themselves and gain notoriety through the impact of their publications. Therefore, topic prominence is a promising metric and has been increasingly employed for mapping scientific performance [ 31 ]. Given the potential of topic prominence, it was deemed an adequate tool for the present investigation.

3. Materials and Methods

The present investigation aimed to assess the global performance of neuromarketing studies. This objective was achieved through a set of methodological procedures, including a systematic search on neuromarketing and a combination of bibliometric analysis techniques. The following sub-sections address each of these procedures in detail.

3.1. Indicators and Methods Used

The methodological procedures employed in the present study included a combination of qualitative and quantitative analysis methods employed in different phases of the investigation, as in in previous bibliographic analyses by Cardoso et al. [ 8 , 9 , 10 ] and Ohniwa et al. [ 31 ]. The decision to use this approach was based on the premise that quantitative methods are more appropriate for drawing statistical inferences and comparisons, while qualitative methods are more suitable for discovering and generating theories [ 25 ]. In this context, several indicators, based on the contributions of previous studies [ 8 , 10 ] were employed in order to assess the performance of neuromarketing studies.

One such indicator was Scival topic prominence. As it had not previously been employed in bibliographic studies on the domain of neuromarketing, its application in the present investigation relied on contributions from studies in other research areas such as Klavans and Boyack [ 26 ] and Small et al. [ 32 ]. Other indicators that must be clarified include word growth and worth network; the use of such indicators was based on the work of Small et al. [ 32 ]. The methodological procedures adopted and the specific research questions that these indicators were intended to tackle are summarized in Figure 1 .

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Summary of research questions, indicators, and technics used.

3.2. Data Collection and Organization Procedures

The data collection procedures were carried out on 7 January 2021. The first step consisted of conducting a document search on Scopus for articles containing the exact key word (Limit to: exactkeywork) “neuromarketing” in the title, abstract, or keywords. The Scopus database is one of the largest high-quality abstract and citation datasets of peer-reviewed literature on the web, and is the only database that features Scival topic prominence [ 8 , 9 , 33 ]. Therefore, it was deemed an adequate source for the purposes of the present investigation.

A total of 318 articles were retrieved. The database was downloaded in Bibtex format from Scopus and subsequently screened with the help of R Studio software (version 1.2.5042) in order to eliminate duplicates and create a unified file. The database was later exported to R Bibliometrix 3.0., which was used for the network analysis in the same manner as previous studies [ 22 , 34 ]. The next step consisted of homogenizing the data and creating an Excel file to which SciVal topic prominence information was manually added, as Scopus does not include such data in the initial downloadable output. The Excel file was uploaded to DB Gnosis software, in which the content analysis was carried out. As argued by Cardoso et al. [ 8 , 10 ] when applied to bibliometric studies, content analysis allows for a clear view of the evolution of the literature on a given topic through the mapping of its scientific production. Therefore, it was deemed a useful technique in the context of the present investigation. Table 1 summarizes the main information obtained on the Scopus database.

Description of Scopus neuromarketing data.

3.3. Data Analysis Techniques and Procedures

Overall performance, analogous to the studies of Cardoso et al. [ 8 , 10 ], was accessed through rankings obtained via frequency counting using DB Gnosis software. The only exception to this was collaboration, which was assessed through network analysis carried out in R Bibliometrix, as proposed by Lima Santos et al. [ 25 ]. The performance of the most cited authors was assessed through DB Gnosis using citation frequency as an indicator. This body of literature was interpreted through categorical content analysis, which served to identify and describe the following variables: topic area, application area, neuroscience technology used, and research gaps addressed. The variables of analysis in the literature review analysis followed the results of Alsharif et al. [ 12 , 33 ]. Additionally, the affiliation of each author was checked during content analysis.

Following the methodology previously applied by Lima Santos et al. [ 25 ] and Cardoso et al. [ 10 ], performance of the prominent topics was accessed using two analyses in R Bibliometrix. The authors’ network and authors’ keyword network structures were performed in R Bibliometrix through Biblioshiny, which provides a web interface for R Bibliometrix. The authors’ network parameters used included normalization by inclusion. The cluster algorithm applied was betweenness. The analysis included 22 edges (excluding isolated nodes), and the output figure considered a minimum of two edges. The authors’ keyword network parameter used was normalization by association. Again, the applied cluster algorithm was betweenness, the analysis included 22 edges (excluding isolated nodes), and the output figure considered a minimum of two edges. Finally, Scival topic prominence and the prominence percentile were accessed using the methodology proposed by Cardoso et al. [ 8 , 9 , 10 ], that is, frequency rankings carried out using DB Gnosis.

4.1. Neuromarketing: Overall Research Performance

The retrieved articles are published in 212 different journals, with a high level of dispersion between them. As shown in Table 2 , two journals, “Frontiers in Neuroscience” and “Frontiers in Psychology”, stand out with nine articles each. Accordingly, four publishers stand out for having ten or more papers in neuromarketing field.

Top ten journals and publishers in neuromarketing research productivity.

In terms of publications by country, as detailed in Table 3 Spain leads the ranking of the most productive countries with 48% of neuromarketing publications, followed by the United States and China. Spanish researchers focus on advertising, particularly online advertising and TV Commercials, as well as on the role of image and sound effects on viewer behavior. The articles published by Spanish research centers use eye tracking, galvanic skin response, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technology. The United States, a close second, is responsible for 39% of all Scopus publications on neuromarketing. American research centers focus on studying TV Commercials. With a considerable gap from the top two countries, China ranks third with 24% of all Scopus publications on neuromarketing. The most-researched topics in Chinese institutions are consumer association and branding strategy, both of which are linked to TV commercials, color association, and tourism.

Top twenty countries in neuromarketing research productivity.

Regarding the most productive Institutions in neuromarketing research, the results are summarized in Table 4 . Complutense Madrid University (Spain) leads the ranking with 21 papers, followed by Roma la Sapienza University (Italy) and Zhejiang University (China) with 19 articles each. As expected, and considering the results shown in Table 3 , in addition to the leading institution Spain has three other universities in the top ten: Granada University is ranked sixth with twelve papers, while Valencia Polytechnic University and Vigo University share the seventh position with two other institutions (eight papers each).

Top ten institutions in neuromarketing research productivity.

Regarding individual authors’ performance (using articles fractionalized/co-authorship and first authorship papers as indicators), as displayed in Table 5 , two authors share the first position in terms of total articles published. The first, Babiloni F., is Professor of Physiology and Director of the Industrial Neuroscience lab in Rome’s Sapienza University, Italy. He has an average fractional article participation items of 1.20 and is a co-author of nine neuromarketing papers (although not being the first author on any), which mostly address TV Commercials. The other author sharing the first position, Ma Q., is a Professor in the School of Management at Zhejiang University, China and a member of the Neuromanagement Lab. He is the first author on six papers, and a co-author of another three. His research encompasses the topics of neuromanagement, branding (consumer behavior), purchase intention, price perception, and emotion.

Top ten authors’ neuromarketing research productivity.

The third most prominent author is Vecchiato G., from Rome University. He is the first author of seven papers and researches TV Commercials, focusing on emotion and attention. The fourth most prominent author is Crespo-Pereira V., from the University of Ecuador. The first author of two papers (and co-author of another seven), his research is about the entertainment industry, mainly TV entertainment and the emotions it arouses in the audience. Regarding first authorships, Vecchiato G. stands out, as he is the first author on seven of the eight papers he published.

The analysis of the worldwide neuromarketing authors’ collaboration network, which is graphically represented in Figure 2 , reveals two strong connections between two groups of four authors. The first group is centered on Babiloni F. and Vecchiato G., both from Rome’s Sapienza University. This group shows a higher degree of connectivity and more consistent and direct connection. Vecchiato G., who is first author on most of the articles, acts as a broker by linking different clusters of Italian authors within the network. Amongst the authors brokered by Vecciato G., two stand out: Maglione A., from the Department of Economics and Marketing of the “IULM” University, and Cherubino P., from BrainSigns, Italy. Both are connected to Babiloni F. as well as with each other. This research group applies neuroelectrical, cognitive, and emotional variables to TV commercials.

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Authors’ collaboration.

The second collaboration group stems from Astolfi L., of the Department of Computer Science at Rome’s Sapienza University. This author connects to Fallani F. from the Paris Brain Institute (ICM), France; Mattia D. from the Department of Neuroscience at the University Tor Vergata, Italy; and Cincotti F. from the Center di Ricerca de La Sapienza Per l’Analisi dei Modelli e dell’Informazione, Italy. This research group studies brain responses to TV commercials. Analogous to the first group, it consists exclusively of Italian researchers.

4.2. Performance of the Most Cited Neuromarketing Articles

Neuromarketing studies aim to better understand consumer preferences by gaining access to potentially unconscious neural and physiological responses [ 35 ]. They aim to predict or even manipulate consumer behavior and decision making [ 36 ]. Neuromarketing has been used for “neuro forecasting” [ 36 ], predicting marketplace responses [ 37 , 38 ], fine tuning segmentation [ 36 ], and nudging consumer behaviors [ 36 ]. Table 6 details the Top ten neuromarketing papers in terms of citations, and Table 7 presents the topic area, application area, neuroscience technology used, and research gap addressed by each of those papers.

Top ten authors in neuromarketing paper citations.

EEG—Electroencephalogram; EMG—Electromyography; fMRI—Functional magnetic resonance imaging.

Top ten: Scival Topic Prominence.

Ranking (R); Absolute Frequency (AF); Relative Frequency (RF); Scival Percentile (SP).

Among the top ten articles, three [ 39 , 40 , 41 ] consist of introductions to neuromarketing. The majority of articles focus on predicting consumer behavior and preferences in various industries (i.e., music, movies, snack foods) [ 11 , 37 , 38 , 42 , 43 , 44 ]. Another frequently-addressed topic is the generalizability of individual fMRI results to market sales data [ 31 , 37 ]. In terms of neuroscience techniques, these researchers rely on fMRI, EEG, skin conductance and eye tracking to collect data on participant responses to stimuli. The only exception in this regard is Lopes et al. [ 1 ], which was included due to the use of the keyword neural networks.

4.3. Performance of Most Prominent Neuromarketing Topics: Author Word Network

To explore the most prominent themes in neuromarketing research from 2007 to 2020, a word network structure was performed considering 22 nodes with a minimum of two edges, as represented in Figure 3 . “Neuroscience”, with a betweenness score of 6.14 of, maintains a network of interconnections with most of the themes in the network. Accordingly, “Advertising” is the topic with the highest proximity (2.32 betweenness). This analysis highlights the interconnections between “advertising”, “marketing”, “neuroscience”, “consumer neuroscience”, “EEG”, and “electroencephalography”, which define this thematic network.

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Author keywords: co-word analysis.

Another strong thematic network (1.26 betweenness) is drawn through the theme of “attention”, with which several keywords are interconnected, including “emotion”, “decision-making”, “neuroeconomics”, “emotions”, “EEG”, and “electroencephalography”. Moreover, “emotions” is directly linked with “neuroeconomics” and “decision-making”, while “emotion” (with 0.2 betweenness) is directly linked with “attention” and “consumer”. These connections, however, are not strong. This low connectivity index is typical of emerging topics.

Other topics with low betweenness indexes (0), and therefore weak connectivity links, are “television”, “consumer behavior”, and “eye tracking”. Naturally, in the context of neuromarketing these are emerging topics. Regarding the nodes related to the technologies used in neuromarketing research, “EEG”, “FMRI”, and “eye tracking” relate to “consumer” and “advertising”, which reflects the use that is normally made of such techniques.

4.4. Performance of Prominent Scival Neuromarketing Topics

SciVal topic prominence performance is operationalized through the outputs of several variables, which are summarized in Table 7 . The most relevant result is that 55% of all scientific production on neuromarketing is clustered under the “Neuromarketing | Neurosciences | TV Commercial” Scival topic prominence, which has a 94.432 prominence percentile. This means that it is in the top 5% of all topics in the world, with high levels of CiteScore, citations, and views. The topic emerged in 2007, after which publications have consistently increased up to 2020. An analysis of this topic on the Scival platform (performed in 13 March 2021) reveals that 640 articles were published worldwide from 2010 to 2020, 27% of which were published in neuromarketing field. This is a high figure compared to overall world production. The leading country in this research topic is Spain, through Complutense University.

The other topics that follow the ranking of neuromarketing research mostly emerged after 2010. Most of these emerging topics have high prominence percentiles, which indicates that they generate high levels of research interest. For instance, the topic “Subjective Well-being | Happiness | Life Satisfaction” emerged in 2020, and has a 98.770 prominence percentile.

4.5. Neuromarketing

The 99th–100th percentile, that is, the 1% best percentile on the worldwide momentum and visibility of topics, includes 17 studies. Information on these topics, namely, the authors who publish on each topic, whether they got funding for their research, and first and second author affiliations, is summarized on Table 8 . Accordingly, information on the individual studies, namely, the topic area, application area, neuroscience technology used, and research objective/gap, are summarized on Table 9 .

Top ten Scival topics by best percentile: funding and author affiliations.

Ranking (R); Scival Percentile (SP); Number of papers (NP); Financing (F).

Articles within the top ten Scival topics by best percentile: topic area, application area, neuroscience technology, and research objectives.

EEG: Electroencephalogram; EMG: Electromyography; EOG: Electrooculography; fMRI: Functional magnetic resonance imaging; fNIRS: Functional near-infrared spectroscopy.

The articles within this percentile can be classified into three different categories: audience response studies, methodology improvement studies, and literature reviews. Audience response studies [ 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 ] explore the impact of marketing campaign pieces on the consumer decision-making process and examine consumer responses and preferences. These studies are applied to many consumer industries and specific markets, such as the coffee industry and hotel industry, and are applied to specific marketing functions, such as corporate communication and brand image.

Methodology improvement studies [ 1 , 53 , 54 , 55 , 56 , 57 , 58 ] propose methods for enhancing the accuracy of object recognition and classification by combining neuromarketing techniques with traditional marketing research methods. These studies are applied to specific goals of neuromarketing methods, such as emotional face retrieval and facial expression recognition. Others focus on certain consumer decision scenarios and situations, such as grocery shopping in supermarkets. Others focus on the media through which information is conveyed, such as audio, video, or still images. Finally, literature review articles [ 59 , 60 ] explore EEG applications in research, consumer privacy concerns, and the regulatory environment.

5. Conclusions

The present study aimed to examine the global performance of neuromarketing studies. To address this general goal, five specific objectives were adopted:

To achieve these objectives, scientific production on neuromarketing was mapped through a mixed-method bibliographic approach.

Regarding the first objective, 318 papers containing the word “neuromarketing” were published in the Scopus database between 2010 and 2020. In terms of leading journals, the ranking is led by Frontiers in Neuroscience and Frontiers in Psychology, closely followed by Profesional de la Informacion. In terms of publications by country, Spain leads the ranking, followed by the United States, China, and Italy. These results are reflected in those on the leading universities, as the Complutense Madrid University leads the ranking with 21 articles, followed by Rome’s la Sapienza University, Italy and Zhejiang University, China.

Although Italy is only fourth in the country ranking, Italian researchers stand out in terms of individual productivity as well as collaboration. The first, third and fourth most productive researchers are Italian and work in Italian research centers. Accordingly, the global collaboration network on neuromarketing is dominated by two very prominent groups of Italian researchers.

Regarding the second objective, the number of introductory articles within the top ten papers in terms of citation demands attention, as it indicates that neuromarketing remains a developing topic. This is somewhat expected, given how recently the subject has come to prominence. Another popular research goal is predicting consumer behavior and preferences in different scenarios, which is line with the general goals of neuromarketing studies, that is, to better understand consumer preferences by gaining access to potentially unconscious neural and physiological responses [ 36 ]. In neuromarketing studies, this goal is pursued via the application of neuroscience technologies. The present study’s findings point to fMRI, EEG, skin conductance, and eye tracking as the most employed neuroscience technologies in neuromarketing studies.

Regarding the third specific objective, most prominent topics within the subject are connected to “Neuroscience” and have some degree of approximation with “Advertising”. The network of prominent topics provides an overview of the main neuroscience techniques and technologies used in these studies, namely, EEG/electroencephalography. Once again, this reflects the very nature of neuromarketing studies, which apply neuroscience techniques to understand and predict consumer behavior in response to certain stimuli. Naturally, a popular source of such stimuli (as these results corroborate) is advertising, especially television advertising, as the topic is highlighted in the network. Therefore, these studies have the potential to unveil the effectiveness of advertising techniques and generate good practices on how best to generate the expected response from consumers in specific industries.

The findings stemming from the fourth objective largely corroborate those of the third, as they show that over half of the scientific production on neuromarketing is clustered under the “Neuromarketing | Neurosciences | TV Commercial” Scival topic prominence, which is within the top 5% of all topics in the world in terms of views and citations. Therefore, this cluster, and consequently neuromarketing research in general, arouses high levels of research interest and has significant potential for funding.

Finally, the findings from objective five provide a list of the very best rated topics in terms of visibility and momentum. Articles within the top 1% percentile include audience response studies, methodology improvement studies, and literature reviews. Audience response studies make up the bulk of neuromarketing research. They employ the technologies highlighted in the network analysis to analyse and predict consumer reactions, especially to advertising, as well as to real-world consumption situations such as hotel stays and grocery shopping.

Methodology improvement studies attempt to refine the methods applied in the previous category, providing best practices on how to analyse consumer reactions and behaviour more precisely and reliably. Finally, literature review studies, as in any other area, summarise and systematise the state of the art on the topic. The highlighted role they play in the neuromarketing field is (analogous to introductory studies) indicative of the fact that the topic is developing quickly, which calls for constant updating of systematic reviews.

Contributions, Implications, and Future Research

The present investigation consisted of a comprehensive bibliometric analysis of academic production on neuromarketing. To this end, in addition to traditional bibliometric indicators, SciVal Topic prominence was examined, which had not been carried out in previous neuromarketing reviews. Therefore, this study provides a more detailed understanding of the state of the art in this area as well as of trending and emerging topics and those with the highest potential for publication. For neuromarketing researchers, the present study reveals gaps in research as well as emerging subjects by identifying research topics that are growing, making it possible to identify future lines of research. This should be quite useful for researchers and research centers aiming to conduct investigatory projects in this area. Moreover, previous studies have shown that research impact and topic prominence are positively related to the proportion of financed research projects within a topic. Therefore, the findings of the present study are particularly useful for researchers seeking financing for such projects.

The present research provides a contribution for future bibliometric studies. It shows that the method proposed by Cardoso et. al. [ 10 ] to map a country’s research performance in a certain area of research—in that case, Switzerland, and Tourism and Hospitality—can be employed to map the worldwide scientific production in each area. Therefore, future studies are encouraged to employ this method, and particularly the novel measure used here (i.e., SciVal Topic prominence analysis), in order to assess research production in other areas and to update the state of the art on neuromarketing in the future.

Regarding future bibliographic studies on neuromarketing, the present research raises some questions that could be addressed by future works. In terms of worldwide collaboration, for instance, the present research highlights the clear domination of Italian research and research centres, although Italy is only fourth in the ranking of most productive countries. However, it was beyond the scope of this study to explore the reasons behind this phenomenon. In this context, future studies could further investigate these reasons, and determine whether research links aim to combine the expertise of researchers in each centre, the technologies owned by each institution, or whether they merely reflect the contact networks and academic histories of the involved researchers.

Author Contributions

Conceptualization, L.C., A.A., M.-M.C. and G.G.F.d.A.; methodology, L.C.; software, L.C.; formal analysis, L.C. and M.-M.C.; investigation, L.C., A.A. and M.-M.C.; resources, L.C. and G.G.F.d.A.; data curation, L.C.; writing, L.C., A.A., M.-M.C., G.G.F.d.A., F.D. and L.M.; writing—review and editing, L.C., A.A., M.-M.C., G.G.F.d.A., F.D. and L.M.; visualization, L.C., A.A., M.-M.C., G.G.F.d.A., F.D. and L.M.; supervision, L.C.; project administration, L.C.; funding acquisition, L.C. and G.G.F.d.A. All authors have read and agreed to the published version of the manuscript.

This research was financed by national funds through the FCT-Foundation for Science and Technology, I.P., within the scope of the UIDB/04470/2020 project, Portugal, and under the Scientific Employment Stimulus-Institutional Call-[CEECINST/00051/2018].

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open access
Published: 21 September 2020

Technological advancements and opportunities in Neuromarketing: a systematic review

Ferdousi Sabera Rawnaque 1 ,
Khandoker Mahmudur Rahman 2 ,
Syed Ferhat Anwar 3 ,
Ravi Vaidyanathan 4 ,
Tom Chau 5 ,
Farhana Sarker 6 &
Khondaker Abdullah Al Mamun 1 , 7

Brain Informatics volume 7 , Article number: 10 ( 2020 ) Cite this article

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Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years. For this purpose, authors have selected and reviewed a total of 57 relevant literatures from valid databases which directly contribute to the Neuromarketing field with basic or empirical research findings. This review finds consumer goods as the prevalent marketing stimuli used in both product and promotion forms in these selected literatures. A trend of analyzing frontal and prefrontal alpha band signals is observed among the consumer emotion recognition-based experiments, which corresponds to frontal alpha asymmetry theory. The use of electroencephalogram (EEG) is found favorable by many researchers over functional magnetic resonance imaging (fMRI) in video advertisement-based Neuromarketing experiments, apparently due to its low cost and high time resolution advantages. Physiological response measuring techniques such as eye tracking, skin conductance recording, heart rate monitoring, and facial mapping have also been found in these empirical studies exclusively or in parallel with brain recordings. Alongside traditional filtering methods, independent component analysis (ICA) was found most commonly in artifact removal from neural signal. In consumer response prediction and classification, Artificial Neural Network (ANN), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) have performed with the highest average accuracy among other machine learning algorithms used in these literatures. The authors hope, this review will assist the future researchers with vital information in the field of Neuromarketing for making novel contributions.

1 Introduction

Neuromarketing, an application of the non-invasive brain–computer interface (BCI) technology, has emerged as an interdisciplinary bridge between neuroscience and marketing that has changed the perception of marketing research. Marketing is the channel between product and consumers which determines the ultimate sale. Without effective marketing, a good product fails to inform, engage and sustain its targeted audiences [ 1 ]. The expanding economy with new businesses is continuously evolving with changing consumer preferences. It is hard for the businesses to grow and sustain without having quantitative or qualitative assessment from their consumers. Newly launched products need even more effective marketing to successfully enter into a competitive market. However, traditional marketing renders only by posteriori analysis of consumer response. Conventional market research depends on surveys, focus group discussion, personal interviews, field trials and observations for collecting consumer feedback [ 2 ]. These approaches have the limitations of time requirement, high cost and unreliable information, which can often produce inaccurate results. In contrast to the traditional marketing research techniques, Neuromarketing allows capturing consumers’ unspoken cognitive and emotional response to various marketing stimuli and can forecast consumers’ purchase decisions.

Neuromarketing uses non-invasive brain signal recording techniques to directly measure the response of a customer’s brain to the marketing stimuli, superseding the traditional survey methods [ 3 ]. Functional magnetic resonance (fMRI), electroencephalography (EEG), magnetoencephalography (MEG), transcranial magnetic stimulator (TMS), positron emission tomography (PET), functional near-infrared spectroscopy (fNIRS) etc. are some examples of neural recording devices used in Neuromarketing research. By obtaining neuronal activity from the brain using these devices, one can explore the cognitive and emotional responses (i.e., like/dislike, approach/withdrawal) of a customer. Different stimuli trigger associated response in a human brain and the response can be tracked by monitoring the change in neuronal signals or brainwaves [ 4 ]. Further, the signal and image processing techniques and machine learning algorithms have enabled the researchers to measure, analyze and interpret the possible meanings of brainwaves. This opens a new door to detect, analyze and predict the buying behavior of customers in marketing research. Now with the help of brain–computer interface, the mental states of a customer, i.e., excitement, engagement, withdrawal, stress, etc., while experiencing a marketing stimuli can be captured [ 5 ]. Besides these brain signal recording techniques, Neuromarketing also utilizes physiological signals, i.e., eye tracking, heart rate and skin conductance measurements to gather the insight of audience’s physiological responses due to encountering stimuli. These neurophysiological signals with advanced spectral analysis and machine learning algorithms can now provide nearly accurate depiction of consumers’ preferences and likes/dislikes [ 6 , 7 , 8 ].

Early years of Neuromarketing generated a controversy between the academician and the marketers due to its high promises and lack of groundwork. From the claim of peeping into the consumer mind to finding the buy buttons of human brain, Neuromarketing has long been under the scrutiny of the academicians and researchers [ 9 , 10 ]. However, academic research in this field has started to pile up and the scope of Neuromarketing to reveal and predict consumer behavior is gradually becoming evident. Neuromarketing Science and Business Association (NMSBA) was established in 2012 to bridge the gap between academicians and Neuromarketers, and it is promoting Neuromarketing research across the world with its annual event of Neuromarketing World Forum [ 11 , 12 ]. It may be proposed that further dialogue may continue under such a platform for further industry–academia collaboration. Evidently, more than 150 consumer neuroscience companies are commercially operating across the globe and big brands (Google, Microsoft, Unilever, etc.) are using their insights to impact their consumers in a tailored and efficient way. Academic research, especially the high analytical accuracy from the engineering part of Neuromarketing has garnered this breakthrough and acceptance over the world. Hence, reviewing the building blocks of Neuromarketing is essential to evaluate its scopes and capacities, and to contribute new perspective in this field. Numerous literature reviews have been published focusing the theoretical aspect of consumer neuroscience, such as marketing, business ethics, management, psychology, consumer behavior, etc. [ 13 , 14 , 15 ]. However, systematic literature review from the engineering perspective with a focus on neural recording tools and interpretational methodologies used in this field is absent. In this regard, our article sets its premises to answer the following questions:

What are the types of marketing stimuli currently being used in Neuromarketing?

What are the brain regions activated by these marketing stimuli?

What is the best brain signal recording tool currently being used in Neuromarketing research?

How are these brain signals preprocessed for further analysis?

And what are the current methods or techniques used to interpret these brain signals?

These questions will allow us to gain a comprehensive knowledge on the up-to-date research scopes and techniques in consumer neuroscience. After this brief introduction, our methodology of conducting this systematic review will be presented, followed by the state-of-the-art findings corresponding to the aforementioned questions and synthesis of the important results. We concluded this review with relevant inference from synthesized result and a recommendation for future researchers.

2 Methodology

The systematic literature review is a process in which a body of literature is collected, screened, selected, reviewed and assessed with a pre-specified objective for the purpose of unbiased evidence collection and to reach an impartial conclusion [ 16 ]. Systematic review has the obligation to explicitly define its research question and to address inclusion–exclusion criteria for setting the scope of the investigation. After exhaustive search of existing literatures, articles should be selected based on their relevance, and the results of the selected studies must be synthesized and assessed critically to achieve clear conclusions [ 16 ].

In this systematic review, we would like to explore the marketing stimuli used in Neuromarketing research articles over the last 5 years with their triggered brain regions. We would also like to focus on the technological tools used to capture brain signals from these regions, and finally deliberate on signal processing and analytical methodologies used in these experiments.

Therefore, the inclusion criteria defined here are as follows:

Literatures must be published in the field of Neuromarketing from 2015 to 2019.

Studies must use brain–computer interface and/or other physiological signal recording device in their Neuromarketing experiments.

Studies must have experimental findings from neural and/or biometric data used in Neuromarketing research.

The exclusion criteria for this review are set as:

Any other literature review on Neuromarketing are excluded from this review.

Book chapters are excluded from this review. Since Neuromarketing is comparatively a new research field, alongside relevant academic journal articles, book chapters conducting empirical experiments using BCI can only be included.

Literatures written/published in any language other than English are excluded from this article.

To serve the purpose of this systematic literature review, a total of 931 articles were found across the internet by using the search item “Neuromarketing” and “Neuro-marketing” in valid databases. Among the screened publications, Table 1 presents the database source of selected 57 research articles including book chapters, which directly contribute to the Neuromarketing field with basic or empirical research findings.

As for the aggregation of relevant existing literatures, the researchers defined that the search for articles would be performed in six databases—Science Direct, Emerald Insight, Sage, IEEE Xplore, Wiley Online Library, and Taylor Francis Online. After the initial article accumulation, the articles were exhaustively screened by the authors by reviewing their title, abstract, keywords and scope to match the objective of this research. Once the studies met our aforementioned inclusion criteria, they were selected for further review and critical analysis. Table 2 classifies the selected articles in terms of the aforementioned dimensions.

By exploring the articles selected to develop this systematic review, it was possible to successfully categorize the trends and advancements in Neuromarketing field in following dimensions:

Marketing stimuli used in Neuromarketing research

Activation of the brain regions due to marketing stimuli

Neural response recording techniques

Brain signal processing in Neuromarketing

Machine learning applications in Neuromarketing.

Some of these Neuromarketing studies have used eye tracking, heart rate, galvanic skin response, facial action coding, etc., with or without brain signal recording techniques to gauge the consumer’s hidden response. As they are the response from autonomous nervous system (ANS), they have proven themselves as successful means of exploring consumer’s focus, arousal, attention and withdrawal actions. Hence, this study includes articles those empirically used these tools to answer Neuromarketing questions, since this study mainly focuses on the engineering perspective. Interpreting the neural data with only statistical analysis has been out of scope of this paper.

3 Systematic review on the advancements of Neuromarketing

Neuromarketing research utilizes marketing strategies in the form of stimuli, and aims to invoke, capture and analyze activities occurring in different brain regions while subjects experience these stimuli. To conduct a systematic review on this matter, it is important to recall the interconnection between brain functions with human behavior and actions triggered by the external stimuli. The knowledge of brain anatomy and the physiological functions of brain areas as well as the physiological response due to external stimuli along with it, makes it possible to model brain activity and predict hidden response. For this purpose, current neural imaging systems and neural recording systems have contributed much to capture the true essence of consumer preferences. This section will discuss the marketing stimuli, their targeted brain regions, neural and physiological signal capturing technologies used over the last 5 years in Neuromarketing research. Comparing these signals with their associated anatomical functionality some studies have already reached high accuracy. A number of the selected studies have used machine learning techniques to predict like/dislike and possible preference from the test subjects.

For the purpose of Neuromarketing experiments, the following literatures selected right-handed participants, with normal or corrected-to-normal vision, free of central nervous system influencing medications and with no history of neuropathology.

3.1 Marketing stimuli used in Neuromarketing

As Neuromarketing is a focus of marketers and consumer behavior researchers, different strategies from marketing have been applied in Neuromarketing and they are being investigated for quantitative assessment from neurological data. Nemorin et al. asserts that Neuromarketing differentiates from any other marketing models as it bypasses the thinking procedures of consumers and directly enters their brain [ 74 ]. Over the last 5 years, Neuromarketing stimuli has been mainly in two forms—products with/without price, and promotions. Product can be defined as physical object or service that meets the consumer demand. In Neuromarketing, product can be physical such as tasting a beverage to conceptual like a 3D (three dimensional) image of the product. Price in Neuromarketing experiments is mostly seen as a stimuli is most of the time intermingled with product or promotion. However, it plays an important role that determines the decision of test subjects to buy or not to buy the product [ 75 ].

Consumer response to a product has been recognized by either physically experiencing the product or by visualizing the image of it. To understand the user esthetics of 3D shapes, Chew et al. [ 17 ], used virtual 3D bracelet shapes in motion and recorded the brain response of test subjects with EEG with motion. As 3D visualization of objects for preference recognition is a new area of research, the authors used mathematical model (Gielis superformula ) to create 3D bracelet-like objects. Their study displayed 3D shapes appear like bracelets as the product to subjects. Using the 3D shapes gave the authors an advantage to produce as many of 60 bracelet shapes to conduct the research on. Another new product was the E-commerce products presented to the test subjects by Yadava et al. and Çakar et al. [ 18 , 34 ]. Yadava et al. proposed a predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes” and “dislikes” by analyzing EEG signals. In showing E-commerce product, they showed a total of 42 product images to the test participants. These product images were mainly of apparels and accessory items such as shirts, sweaters, shoes, school bags, wrist watches, etc. The test participants were asked to disclose their preference in terms of likes and dislikes after viewing the items [ 18 ]. Çakar et al. used both product and price to explore the experience during product search of first-time buyers in E-commerce. To motivate the participants, this research provided each participants around 73 USD as a gift card to use during the experiment. The test participants were asked to search and select three products of their interest from an e-commerce website and reach the maximum of their gift card limit to activate. Test subjects often experienced negative emotion while being unable to find necessary buttons such as “add to cart” or “sorting options” [ 34 ]. These Neuromarketing experiments on E-commerce products may help developers to build better user experience. Retail businesses lose large amount of money when they invest in the wrong product. Among retail products, shoes have thousands of blueprints for manufacturing. Producing thousands of shoes of different designs to satisfy consumers can be laborious and unprofitable since a large number of the designs turn out to be failures. Baldo et al. directly used 30 existing image of shoe designs to show the test subjects to and to choose from a mock shop showing on the screen [ 39 ]. EEG signals were recorded during the whole shoe selection time and then subjects were asked to rate the shoes in a rank of 1 to 5 of Likert scale. This experiment helped realize brain response-based prediction can supersede self-report-based methods, as the simulation on sales data showed 12.1% profit growth for survey-based prediction, and 36.4% profit growth for the brain response-based prediction.

Similar to the shoe experiment, Touchette and Lee [ 21 ] experimented on the choice of apparel products among young adults, based on Davidson’s frontal asymmetry theory. EEG signals were recorded while 34 college students viewed three attractive and three unattractive apparel products on a high-resolution computer screen in a random order. Pozharliev et al. [ 20 ] experimented on the emotion associated with visualizing luxury brand products vs. regular brand products. The experiment displayed 60 luxury items and 60 basic brand items to 40 female undergraduate students to recognize the brain response of seeing high emotional value (luxury) products in social vs. alone atmosphere. The study found that, luxury brand products invoked a higher emotional value in social atmosphere which could be utilized by the marketers. Bosshard et al. and Fehse et al. experimented on brand images and the comparison between the brain responses associated with preferred and not preferred brands [ 32 , 33 ]. In the study performed by Bosshard et al., consumer attitude towards established brand names were measured via electroencephalography. Subjects were shown 120 brand names in capital white letter in Tahoma font on black background and without any logo while their brain responses were recorded. On the other hand, Fehse et al. compared the brain response of test subjects while they visualized blocks of popular vs. organic food brand logos. These experiments on brand image may help marketers to recognize the implicit response of consumers on different types of branding.

As price is mentioned as an important factor that determines the user’s interest on purchasing a product, a number of Neuromarketing studies have used price alongside the products. In the aforementioned study by Çakar et al. [ 34 ] price was displayed while recording brain response during first-time e-commerce user experience. Marques et al. [ 22 ], Çakir et al. [ 24 ], Gong et al. [ 35 ], Pilelienė and Grigaliūnaitė [ 36 ], Hsu and Chen [ 26 ], Boccia et al. [ 37 ], Venkatraman et al. [ 38 ], and Baldo et al. [ 39 ] have included price as a marketing stimuli with the product or promotional.

An interesting concept was tried by Boccia et al. to recognize the relation between corporate social responsibilities and consumer behavior. The author attempted to identify if consumers were willing to pay more for the products from socially or environmentally responsible company. Consumers were found to prefer the conventional companies over the socially responsible companies due to lesser price. Marques et al. [ 22 ] investigated the influence of price to compare national brand vs. own-labeled branded products. In the experiment of Çakir et al, product then product and price were shown to the subjects before decision-making time and the brain responses were recorded through fNIRS [ 24 ]. Sometimes price can play a passive role in the form of discounts or gifts in a promotional. Gong et al. innovatively designed an experiment to compare consumer brain response associated with promotional using discount (25% off) vs. gift-giving (gift value equivalent to the discount) marketing strategies. Their study found that lower degree of ambiguity (e.g., discounts) better motivates consumer decision-making [ 35 ]. Hsu and Chen used price as a control variable in their wine tasting experiment. As price plays a pivotal role in purchase decision, two wines were selected of approximately equal price $15. Then the EEG signals of test subjects were recorded during the wine tasting session [ 26 ].

Promotion is the communication from the marketers’ end to influence the purchase decision of consumers [ 75 ]. In Neuromarketing research, promotion is usually found as the TV commercials and short movies for advertisement. One of the key focus of Neuromarketers is to evaluate the consumer engagement of advertisements. Predicting the engagement of advertisements before broadcasting them on air, ensures higher rate of successful promotions.

In 2015, Yang et al. used six smartphone commercials of different brands to compare among them in terms of extract cognitive neurophysiological indices such as happiness, surprise, and attention as well as behavioral indices (memory rate, preference, etc.) [ 41 ]. A common experimental design procedure is found among the promotion-based Neuromarketing experiments, that is subjects are first made comfortable in the experimental setting, consecutive advertisements were placed at a time distance no shorter than 10 s and consecutive advertisements used neutral stimuli such as white screen, green scenario, blank in between them to stabilize the test participants.

The Neuromarketing experiments of Soria Morillo et al. [ 40 , 43 ] tried to find out the electrical activity of audience brain while viewing advertisement relevant to audiences’ taste. They display used 14 TV commercials displayed to their 10 test subjects for their experiment and predicted like or dislike response from audience with the help of advanced algorithms. Cherubino et al. [ 42 ] investigated cognitive and emotional changes of cerebral activity during the observation of TV commercials among different aged population. Among seven TV commercials displayed during the experiment, one commercial with strong images was analyzed for the adults’ and older adults’ reaction. Other than them, Vasiljević et al. [ 44 ] used Nestle advertisement to measure consumer attention though pulse analysis; Daugherty et al. [ 46 ] replicated an experiment of Krugman (1971) using both TV advertisements and print media advertisements to recognize how consumers look and think; Royo et al. [ 47 ] focused on consumer response while viewing advertisements of sustainable product designs. For their experiment, an animated commercial was made containing verbal narrative of sustainable product and an existing commercial was used to convey the visual narrative of conventional product. Venkatraman et al. focused on measuring the success of TV advertisements using neuroimaging and biometric data [ 38 ]. Randolph and Pierquet [ 51 ] showed super bowl commercials to undergraduate students to compare the class rank of the commercials and the neural response from the test subjects. Nomura and Mitsukura [ 52 ] identified emotional states of audiences while watching favorable vs. unfavorable TV commercials. They selected 100 TV commercials among which 50 commercials were award winning which were labeled as favorable advertisements. Singh et al. [ 56 ] used promotion in the form of static vs. video advertisements to predict the success of omnichannel marketing strategies. Ungureanu et al. [ 53 ] measured user attention and arousal by eye tracking while surfing through web page containing static advertisements, while Goyal and Singh [ 54 ] utilized facial biometric sensors to model an automated review systems for video advertisements. Oon et al. [ 55 ] used merchandise product advertisement clips to recognize user preference. Singh et al. [ 56 ] used video advertisements to measure visual attentions of audiences.

Most of the TVC (television commercials) in these literatures had a standard time of 30 s. In Neuromarketing, these TVCs were displayed in between other videos such as documentary film, gaming video, drama, etc., to capture the true response of consumers.

Sometimes Neuromarketing is observed dealing with advertisement of different purposes, such as social advertisements or gender-related advertisements. The application of Neuromarketing in social advertisement is to predict the success of these ads to reach its messages to the targeted social groups [ 45 , 49 , 69 ]. Chen et al. [ 49 ] experimented on the neural response of adolescent audiences while they are exposed to e-cigarette commercials. Another social advertisement stimuli of smoking cessation frames was used by Yang [ 45 ], to understand what types of frames (positive/negative) achieve better attention from smokers and non-smokers. Gender plays a substantial role in advertisement industry from celebrity endorsement to gender-targeted marketing. Missaglia et al. [ 69 ] conducted a research on fast marketed consumer goods (FMCG) advertisements with celebrity vs. non-celebrity female spokesperson. Casado-Aranda et al. [ 50 ] worked on gender-targeted advertisements using congruent vs. incongruent product–voice combination. These studies show us the diversity of marketing stimuli for future Neuromarketing applications.

3.2 Activation of brain regions due to marketing stimuli

Human brain is a matter of profound astonishment. The anatomical development of our brain resulted in the complex web of cognitive and emotional process we experience every day. The evolution of vertebrate brain was initially proposed by Paul D. MacLean in his Triune Brain model [ 76 ]. In his hypothesis, evolution of vertebrate brain is formed through three phases. First the reptilian complex, which indicates the association of instincts with the anatomical structure basal ganglia. The paleomammalian complex consists of septum, amygdalae, hypothalamus, hippocampal complex, and cingulate cortex as the limbic system. These organelles were associated with motivation and emotional response of mammalian brain. Finally, neomammalian complex consists of cerebral neocortex or the outer layer of advanced mammalian brain, which is particularly a unique feature of human brain. In the cerebral neocortex, we find four lobes which control our sensory, motor, emotional and cognitive processes [ 76 ]. The triune brain model has been rejected by new neuroscientists due to the interconnectivity of human brain structures and their function. However, the anatomical structure of human brain explained by this theory plays a vital role in recognizing cognitive, emotional and behavioral process.

Understanding the anatomy of human brain has showed itself indispensable in Neuromarketing research, as its functionality is deeply associated with the interpretation of neural response. The outer layer of the human brain is a complex system organized in four lobes, namely (frontal, parietal, temporal and occipital lobes), each having distinct functionalities for cognitive, emotional, and motor responses. The frontal lobe is the region where most of our thoughts and conscious decisions are made [ 77 ]. Cognitive decision-making mainly takes part in the prefrontal region of this lobe, and movement-related decisions are made in the end part of frontal lobe. Information about taste, touch and movement is processed by the parietal lobe. The occipital lobe is the primary center for visual processing, and the temporal lobe is responsible for visual memories, auditory recognition and integrating new sensory information with memories [ 78 ]. Besides the primary lobes, cerebral cortex brain anatomy has gyri and sulci which create the folded appearance of the brain. The gyri functions on increasing surface area for information processing. Alongside the primary lobes, gyri of these lobes can be considered as the region of interest (ROI) in neural imaging techniques [ 79 ].

Deeper structures of the human brain consisting thalamus, amygdalae, etc., produces sensory and instinctual responses which are later transported to the cerebral cortex. Hypothalamus works as the master control of our autonomic system. Sleep, hunger, thirst, blood pressure, body temperature, sexual arousal are controlled and regulated by hypothalamus. Thalamus on the other hand regulates sensory information, attention and memory. Amygdalae originate our emotional response and hippocampus is the mainframe of our memory [ 77 ].

Retrieving information from brain requires diverse types of methodology. In Neuromarketing experiments, different parts of brain are selected for retrieving distinct information. An experiment which solely focuses on attention might only look at the signals from frontal lobe, whereas experiments focusing on buyer’s motivation might want to look at deeper structures [ 38 ].

According to Soria Morillo et al., brain signal acquisition may capture neural signals either from cerebral cortex or from the deeper layer of the brain [ 40 , 43 ]. Their experiment on TV advertisement liking recognition initially uses information only from prefrontal cortex using a single electrode EEG device. Their experiment showed, it is possible to classify like/dislike with information collected solely from frontal lobe.

Similarly, Cherubino et al. emphasized on the significance of frontal cortex (FC) and prefrontal cortex (PFC) in Neuromarketing studies. PFC processes the conscious and unconscious cognitive and emotional information. Hence, devices using only a single sensor select PFC as their signal acquisition region [ 42 ]. Also, ventromedial prefrontal cortex corresponds to motivational behaviors, imaging of which by fMRI or MEG can reveal purchase motivations [ 22 ].

Neural communication in the brain is conducted through the action potentials, or the firing of neurons [ 80 ]. A neuronal signal is the electrochemical information that neurons send to each other. These information are acquired as signals of non-linear pattern called the brainwaves [ 80 ]. These brainwaves are further associated with the neural signature of brain states. The neural signature is divided into frequency bands known as rhythms, such as the delta (0.1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz), beta (12–30 Hz), and gamma (30–90 Hz). These frequency bands are related to different brain states, regions, functions or pathologies. Delta ( δ ) waves are characteristic of deep sleep and have not been explored for BCI applications [ 81 ]. Theta ( θ ) waves are enhanced during sleep in adults and often related to various brain disorders. During wakefulness under relaxed conditions alpha ( α ) waves with moderate amplitude appear spontaneously. Beta ( β ) waves have less amplitude and are strongly related to motor control and engagement or decision-making procedure. Gamma ( γ ) waves are associated with movement-related activity of the brain and intensely observed in invasive neural recording [ 81 ].

In Neuromarketing, beta wave amplitudes are associated with reward processing which can further predict success of a product or TVC (Boksem and Smitds) [ 57 ].

Frontal alpha asymmetry is a key concept of hemisphere-based like–dislike classification approach. When the brain is considered as two hemispheres, left and right frontal cortices show hemispheric asymmetry in their activation during processing positive and negative emotion. Another term for emotional engagement, Approach–Withdrawal Index refers to the emotional response from Frontal Alpha Asymmetry theory [ 34 ]. Frontal Asymmetry Index is a marker of approach and avoidance. “Emotional Engagement” in Neuromarketing is expressed as the power of specific frequency bands from left and right frontal regions. The F3/F4 and F7/F8 electrodes are the best candidates for these EEG power reception as they are positioned at the most sensitive places (International 10–20 System). The alpha frequency band (8–12 Hz) is commonly used in the frontal alpha asymmetry theory. However, as the alpha activity corresponds with relaxation and meditation, it is negatively correlated with cognitive engagement.

Frontal Asymmetry Index is measured from the equation:

Higher the Frontal Asymmetry Index value, the more approach response is obtained from the test subjects and vice versa. This high or positive asymmetry score can determine pleasant feeling of a test subject and vice versa, which was explored in the study conducted by Touchette and Lee [ 21 ].

Neuroimaging and neural signal recording devices use these locations and brain states to map the mind of a consumer. A standard 10–20 system has been established, which is an internationally recognized method to apply the EEG sensors or electrodes on a human scalp. EEG electrodes under 10–20 system have letters to express their location on skull such as prefrontal (Fp), frontal (F), temporal (T), parietal (P), occipital (O), and central (C). Even number of electrodes are placed on the right side of the head.

On the other hand, a test subject is placed inside an fMRI machine where the activities of the cortices can be recorded from the hemodynamic response or blood oxygen level-dependent (BOLD) imaging process. fMRI can look deeper within the spatial range from millimeters to centimeters. This enables Neuromarketing researchers using fMRI imaging to examine the response at putamen, thalamus, amygdalae and even in the hippocampus.

Functional near-infrared spectroscopy (fNIRS) is another new brain imaging tool which uses the hemodynamic responses associated with neuronal activities [ 24 , 60 ]. However, fNIRS has a lower spatial resolution than fMRI and cannot look deeper than 4 cm.

Alongside brain regions associated with neural response, the human has a peripheral system which corresponds to cognitive and emotional processes. Eye movement, skin conductance, heart rate, facial expression all are result of neural processes. Eye tracking is primarily considered as the physiological response in consumer neuroscience, however studies have suggested eye tracking as a result of activation of the visual cortex or a secondary neural response [ 34 , 36 , 38 , 53 , 70 ].

Neuromarketing experiments focused on the affect–circumflex coordinate or valance–arousal coordinate use autonomic nervous system (ANS) response from sweat glands of hands or galvanic skin response (GSR), and cardiovascular measure or heart rate (HR). GSR is viewed as a sensitive and convenient measure for indexing changes in sympathetic arousal associated with emotion, cognition and attention. On the other hand, HR correlates with the emotional valence of a stimulus, e.g., the positive or negative component of the emotion [ 34 ].

Considering the available regions to capture signals from, it is highly likely that Neuromarketing will exponentially improve its recognition and predictions in user response and preferences.

3.3 Neural response recording techniques

The groundwork in Neuromarketing field is evidently indebted to the advancement of neuroimaging and neural recording tools. Neurophysiological tools, such as electroencephalography (EEG), functional magnetic resonance imaging (fMRI), eye tracking, skin conductance, heart rate, etc., made it feasible to conduct the academic and commercial Neuromarketing research. Many research-grade neurophysiological and biometric signal capturing devices are now available in the market. However, some devices have cost and mobility advantages over the others and therefore replacing the expensive and immobile devices for Neuromarketing purpose.

Among all neuroimaging devices, functional magnetic resonance imaging (fMRI) has been the most widely used neuroimaging technique in Neuromarketing research during the initial time of consumer neuroscience. The reason behind the wide acceptance of fMRI is that it offers the identification of cerebral regions associated with cognitive and emotional process. Combining magnetic field and radio waves, fMRI produces a sequence of images of the cerebral activity by measuring the blood flow of the cerebral cortex [ 38 ]. The signal imaged in fMRI is called BOLD (blood oxygen level dependent) signal. This technology also allows 3D views of the coordinates that denote certain location, making possible to investigate deeper brain structures [ 57 ]. The primary disadvantages of this method are that it is very expensive and till now has a poor temporal resolution. The computer screen used in fMRI refreshes the image every 2 to 5 s. This low temporal resolution to the order of seconds due to the time requirement of the cerebral blood flow’s increment after being exposed to the stimuli, makes fMRI unsuitable for tracking brain activities to the order of milliseconds, which is required in video advertisement analysis. Other disadvantage is the head of the subject must remain static during the whole image recording process [ 62 ]. This restriction causes complex preprocessing and movement-related artifact removal from the fMRI signals. A number of studies, i.e., Venkatraman et al. [ 38 ], Marques et al. [ 22 ], Hubert et al. [ 25 ], Hsu and Cheng [ 26 ], Chen et al. [ 49 ], Casado-Aranda et al. [ 50 ], Wang et al. [ 30 ], Wolfe et al. [ 31 ], Fehse et al. [ 33 ], etc., have used fMRI as the neuroimaging technique in their Neuromarketing studies. fMRI in all studies required the test subjects to remain static and displayed the subjects the images and commercials of products for 3–5 s. Later the subjects had to make purchase decision within 5 s after their exposure to the stimuli [ 50 ]. Researchers over the last 5 years are found using 3-T fMRI scanner 3.0-T Siemens Magnetom Trio system MRI Scanner equipped with a 32-channel bridge head coil (Hubert and Hsu and Cheng) [ 25 , 62 ] and 3 Tesla Siemens Verio scanner (Wang et al. [ 30 ]). Cost of an fMRI machine can be from $500,000 to $3 million varying on its spatiotemporal resolution.

Alongside fMRI, electroencephalography (EEG) is another popular tool used in Neuromarketing research. Number of research in Neuromarketing using EEG devices is increasing due to EEG’s cost efficiency high temporal resolution and mobility advantages. The EEG measures electrical activity in the cerebral cortex, the outer layer of the brain. EEG devices are placed following the 10–20 system. According to the 10–20 system, the 10 and 20 refer to the actual percentage of distances between adjacent electrodes which are either 10% or 20% of the total front–back or right–left distance of the skull [ 82 ]. As EEG is portable and allows capturing signal from cerebral cortex with high temporal resolution, it is mainly used in TV commercial engagement or success analysis. EEG signal of interest in Neuromarketing are mainly event-related potential (ERP), and late positive potential (LPP). ERP and LPP are used by Pozharliev et al. [ 20 ] to measure the emotional value of luxury products. Çakar et al. [ 34 ] used ERP to explore the experience of first-time user of E-commerce product. Pilelienė and Grigaliūnaitė [ 36 ]) used ERP along with eye tracking signal to measure the impact of celebrity spokesman in TVC. Shen et al. [ 23 ] used ERP and LPP to explore the influence of rating reviews on online products.

Research-grade EEG devices are vastly used in Neuromarketing. Emotiv Epoc and Emotive Epoc+ were found as the mostly commonly used EEG devices in the review. These devices were used in the studies of Yang et al. [ 45 ], Chew et al. [ 17 ], Soria Morillo et al. [ 40 ], Yadava et al. [ 18 ], Royo et al. [ 47 ], Jain et al. [ 63 ], and Singh et al. [ 56 ]. Emotive Epoc+ is a moveable, cost-effective EEG headset having 14 electrodes those cover the frontal, temporal, parietal and occipital lobes with channels AF3, F7, F3, FC5, T7, P7, O1, O2, P8, T8, FC6, F4, F8, AF4. The acquired brain signals from Emotiv Epoc+ are highly dependable and have already been used in these scientific researches. Another popular EEG device in Neuromarketing, NeuroSky Mindwave, has only one sensor placed on the prefrontal cortex of the head or the forehead. Unlike EEG devices with wet electrodes, Neurosky Mindwave employs a biosensor which does not require any conductive medium to be applied on the test subject’s scalp [ 40 ]. With the help of NeroSkyLab, the provided scientific research tool, data viewing and analysis can be conducted easily by non-engineer population. In 2015, Soria Morillo et al. and Ogino and Mitsukura in 2018 conducted Neuromarketing experiment with NeuroSky device and with the help of machine learning algorithm, their choice prediction accuracy was over 70% [ 40 , 68 ]. A 10-channel EEG device BrainAmp, from BrainProducts GmBh was used in the Neuromarketing experiment conducted by Cherubino et al. [ 42 ]. Another device EEGO Sports from ANT Neuro (32 channels) was used to analyze non-linear features of EEG signals by Oon et al. [ 55 ]. B-alert X10 headset from ABM consisting 9 electrode channels is found in use by the experiment of Chew et al. [ 17 ]. 8-channel E-Prime from Neuroscan is another EEG device is used in the sales strategy experiment by Gong et al. and Touchette et al. conducted their apparel liking experiment with NeXus-10 biofeedback system. EEG devices have different sampling rates starting from 128 to 512 Hz. This sampling rate determines the highest frequency recordable by the EEG device. In general EEG has a lower frequency spectrum, having Gamma band up to 90 Hz. This gives researchers advantage to choose the right EEG device from numerous manufacturers. Price of EEG devices depends mainly on the number of electrode channels and performance. Cost of EEG device starts from $99 and can go beyond $25,000, which gives researchers buying flexibility.

Magnetoencephalography (MEG) uses magnetic potentials to record brain activity at the scalp level, using magnetic field sensitive detectors in the helmet placed on the subject’s head. Magnetic field is not influenced by the type of tissue (blood, brain matter, bones), unlike electrical field-based EEG, and can indicate the depth of the location in the brain with high spatial and temporal resolution [ 3 ]. Similar to MEG, transcranial magnetic stimulation (TMS) uses varying magnetic field [ 83 ] generated by electromagnetic induction using an iron core. TMS can stimulate targeted part of the brain, which enables it to conduct social or behavioral experiments. TMS and MEG are also used frequently in Neuromarketing experiments. However, the selected databases for this review did not contain any Neuromarketing research articles using these technologies over the last 5 years.

The electromyography (EMG) measures electrical activity produced by skeletal muscles when the muscles contracts and expands in order to move the body [ 70 ]. Also EMG is generated from the autonomic nervous activity related to emotional or mental activity. In Neuromarketing research, facial EMG is the best measure of the valence of the emotional reaction as it records facial muscle movement from two different muscles, i.e., zygomaticus muscle and corrugator muscle. Zygomatic muscle is found to react more while exposed to positive stimuli [ 70 ].

Besides these brain signal recordings, eye tracking is the most popular method for analyzing consumer response. Eye tracking offers to measure visualization time and gaze path across a screen in Neuromarketing experiments. Besides tracking eye movement, pupil dilation measurement allows one to associate audience’s focus and arousal to the marketing stimuli. In the reviewed literatures, Tobii Pro X2-30 system from Tobii Technology was found as the most popular eye tracking device. In 2019, Etzold et al. used this eye tracking device to explore attention research on online booking [ 48 ]. Tobii Pro can also cooperate with fMRI-based Neuromarketing experiment (Venkatraman [ 38 ]). Other than Tobii, Eye Tribe is found in use by Çakar et al. [ 34 ]. Ungureanu et al. used eye tracking to measure the attention level of consumers while displaying static advertisements of cars and clothing products [ 53 ]. Figure 1 depicts the most popular methods of neural response recording i.e. EEG, fMRI and eye tracking used in the Neuromarketing experiments.

Neural recording in Neuromarketing experiments: a multichannel EEG [ 43 ], b fMRI imaging [ 50 ], and c eye tracking for online booking appointment [ 48 ]

Some of the Neuromarketing studies used heart rate, as one of the metrics to measure arousal and focus of the consumer while they encounter TV commercial stimuli. Heart rate is the speed of the heartbeat and it is typically measured by electrocardiogram (EKG). An EKG measures the electrical activity of the heart using external skin electrodes. Heart rate is controlled by two antagonistic nervous systems, i.e., the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS). Automatic response to external stimuli is determined by the sympathetic system of the body. Activation of this system increases heart rate, causing fight or flight mode, which is an independent measure of arousal [ 38 ]. In contrast, the calm and relaxed state characterized by slower heart rate is controlled by the parasympathetic system. Slower heart rate in response to an advertisement implies the increased focus on the ad, hence provides an independent measure of attention [ 38 ]. Another physiological parameter, skin conductance (SC), or galvanic skin response (GSR), develops when the skin acts as an electrical conductor due to the increased activity of the sweat glands from exposure to stimulus [ 38 ]. Skin conductance amplitude and response latency provide direct measures of arousal when watching TV commercials, unlike self-reported measures that are often based on later memory recall. Although GSR cannot independently relate to emotional valence, some of the Neuromarketing studies, i.e., Cherubino et al. [ 42 ], Çakar et al. [ 34 ], Ungureanu et al. [ 53 ], Magdin et al. [ 71 ], Goyal and Singh [ 54 ], and Singh et al. [ 56 ] have used skin conductance along with heart rate to measure the consumer attention and focus on the TVC.

3.4 Brain signal processing in Neuromarketing

Since neural signals and images are highly vulnerable to noise and artifacts, before performing any analysis or interpretation it is imperative to preprocess the neural signals to increase the signal-to-noise ratio (SNR). Noises that commonly accompany the EEG signals are cardiac signals (ECG), power line interference, eye movement artifact (EOG) and muscle movement artifacts (EMG). Preprocessing in Neuromarketing consists of filtering the signals to the frequency bands of interest, re-referencing the filtered signal to a common average, detecting and interpolating bad channels, noise and artifact removal, and framing or segmentation for further machine learning process.

EEG signals usually spread across its energy from 0.5 Hz to around 90 Hz. For classification purpose, it is required to have energies only from the relevant frequency bands, hence EEG preprocessing commonly uses band pass filtering techniques. Band pass filter requires two cutoff frequencies, one upper and one lower to pass the energy between them and blocks energies from all other frequencies. Band pass filter used in these Neuromarketing experiments are basically the digital version of the filter mostly applied by MATLAB and EEGLAB (a toolbox designated for EEG signal processing in MATLAB). Re-referencing to a common average reference is also found common after band pass filtering in the studies of Yang et al. [ 41 ], Fan and Touyama [ 66 ] to reduce possible shifts from external artifacts. Power line interference is usually found removed by using a notch filter at 60 Hz or 50 Hz.

The reviewed literatures had some common approaches in noise removal techniques. Since the noise accompanied with EEG signals are random in nature, signal averaging is a common approach to reduce these noises. Fan and Touyama [ 66 ] averaged the ERP signals for noise removal. Chew et al. [ 17 ] used ABM software development kit (SDK) in MATLAB to remove 5 types of artifacts, namely EMG, eye blinking artifact, excursions, saturations and spike. Excursion, saturation and spike artifacts in the EEG signals are replaced by zero values. Then they applied nearest neighbor interpolation to replace those zero values. Another type of filter Savitzky–Golay is found in use by Yadava et al. [ 18 ] for signal smoothing. For noise and artifact removal, the 4 th -order Butterworth filter was used in the studies of Ogino and Mitsukura [ 68 ] and Oon et al. [ 55 ].

Independent component analysis (ICA) is an approach to separate the statistical subcomponents of EEG signals. ICA is found as the most sought after technique for removing artifacts and noise from EEG signals in these articles. Studies of Cherubino et al. [ 42 ], Bhardwaj et al. [ 53 ], Venkatraman et al. [ 38 ], Pozharliev et al. [ 20 ], Boksem and Smitds[ 57 ], Wriessnegger et al. [ 29 ], Fan and Touyama [ 66 ], Pilelienė and Grigaliūnaitė [ 36 ] all used independent component analysis mostly for eye blink and eye movement artifact, and muscular movement noise removal.

Neuromarketing with fMRI studies have a different method for image preprocessing. Since the fMRI provides a 3D image of the brain region with time information, it is basically a 4D signal. A 4D dataset is motion corrected for any head movement, slice time corrected, spatially normalized and finally smoothed to recover a denoised fMRI image. Wang et al. [ 30 ] used statistical parametric mapping (SPM) software to preprocess their fMRI data. Their raw fMRI signal was subjected to standard preprocessing involving correction for head motion, slice timing correction, temporal and spatial denoising and normalization into standardized Montreal Neurological Institute (MNI) space. The mean fMRI signal from each region of interest was extracted from voxels in a sphere of 6-mm radius centered at the activation point in the regional activation map.

fMRI scan was also used by Hubert et al. [ 25 ] in their experiment on hedonic vs. prudent shopper based on consumer impulsiveness. Decision-making process with cognitive deliberation and the consideration of long-term consequences are associated with processing in brain areas such as the ventromedial prefrontal cortex (vmPFC) and the dorsolateral prefrontal cortex (dlPFC). Hence, these vmPFC and dlPFC were the region of interests to capture the BOLD activation imaging [ 62 ]. Brain activation through BOLD signals was used by Hsu and Cheng [ 26 ] to investigate negative emotion after product harm crisis. fMRI region of interest in this study included amygdala, left calcarine, striatum, ventral tegmental area (VTA) and right insula. The amygdala is associated with memory and subjective evaluation, left calcarine relates to human visual processing, the striatum is associated with goal-oriented evaluation, and reward evaluation, VTA relates to decision-making process and motive functions, and the insula regions are involved in consumer decision-making related to negative reinforcement. Acquiring activation within these regions affirms the relation between stimuli and cognitive response.

Signal detection and segmentation is the process by which the signal of interest is detected from the original signal and then separated for further procedures. The energy of the signal may be used as a threshold for detection of the signal. Often the Neuromarketing experiments contain multiple types of stimuli shown to the test subjects. In such cases segmentation separates the event-based time signals for further processing, example Bhardwaj et al. [ 58 ]. Segmentation or framing the EEG signals to a shorter time window is mostly required to process the signal in time–frequency domain [ 58 ]. Cherubino et al. [ 42 ] segmented their acquired and filtered EEG traces to extract the cerebral activity during the exposure to the marketing stimuli. Oon et al. [ 55 ] used 1-s segmentation time to extract non-linear detrended fluctuation analysis features.

The goal of feature extraction is to find the set of feature that minimizes intra-class variability and maximizes inter-class variability. So we need to extract useful information from the preprocessed signal, which can be spatial, spectral or temporal [ 45 ]. As the EEG signal is non-stationary, the feature extraction procedure is quite often complicated. Discrete wavelet transformation (DWT) is a viable way to extract features from EEG signals.

Yadava et al. [ 18 ] performed DWT-based four-level wavelet analysis to extract features from their EEG signals and decomposed the EEG signal into delta, theta, alpha, beta and gamma frequency bands. Another feature extraction approach, principal component analysis (PCA) was used by Venkatraman et al. [ 38 ] for extracting fMRI features in their Neuromarketing experiment. In 2016, Fan and Touyama applied spatial and temporal principal component analysis (STPCA) for feature extraction from ERP P300 signal. Rakshit and Lahiri [ 67 ] used a different approach to extract features from EEG signals. They used Welch method for one-sided power spectral density estimate and then applied a 256-point DFT algorithm on hamming window of length 50 to extract features. Chew et al. [ 17 ] adopted Hadjidimitriou and Hadjileontiadis methods in feature extraction where the feature estimation is based on the event-related synchronization and desynchronization theory.

Feature selection is also popularly known as dimensionality reduction or subset selection. This is a well-known concept in machine learning which is about selecting an optimal set of features that decreases dimensionality, but has the most contribution to the classification accuracy. In the past few years, feature selection has caught the attention of most researchers because of the nature of high dimensionality of bio-signals and the low number of sample data. Selection of the optimal feature subset is always relative to an evaluation function. In most cases it is the evaluation function that measures the classification accuracy. Feature selection techniques can be divided into three categories, namely: filter, wrapper and embedded approach. Wang et al. [ 30 ] used Recursive Cluster Elimination (RCE) algorithm in spatiotemporal fMRI feature selection. Soria Morillo et al. [ 40 ] used PCA for feature reduction from their dataset. One-way analyses of variance (ANOVA) then cross-validation were also found in use to identify the optimal feature set for cognitive or affective state classification by Yang et al. [ 41 ].

3.5 Machine learning application in Neuromarketing

Using advanced neural recording method and signal processing tools, one can analyze EEG signals and interpret their correspondence with marketing stimuli. Frontal alpha asymmetry theory helped the researcher classify emotional approach/withdrawal response of the test subjects using sub-band power of left and right hemispheric frontal electrode [ 21 ]. However, classifying approach/withdrawal or like/dislike without the FAA is possible, even possible from single electrode EEG signals. This requires advanced Machine Learning algorithm application in Neuromarketing. Both supervised and unsupervised learning methods were used in the following Neuromarketing experiments. Supervised learning in Neuromarketing uses a priori ground truth, usually the interviewed response (like/dislike) from the test subjects as the labels. The labels help the classifier know the signal pattern of like and dislike EEGs in the training datasets. During the testing phase, like/dislike is predicted from a dataset without the labels. Researcher can hide the training dataset labels from the classifier, and later use it for accuracy calculation. On the other hand, unsupervised learning approach used in Neuromarketing does not require prior knowledge of the like/dislike labels. It analyzes the signals with an aim to infer the existing structures for different classes. Supervised learning usually solves either classification problem or a regression problem. Support Vector Machines (SVM), Naive Bayes, Artificial Neural Networks (ANN), and Random Forests (RF) are the most common supervised learning classifiers in Neuromarketing. In parallel, unsupervised learning in Neuromarketing has prominently the clustering type classifiers, such as K-NN (k-nearest neighbors), principal component analysis, singular value decomposition, and independent component analysis (ICA).

Neuromarketing researches over the last 5 years mainly dealt with like/dislike classification problem and predicting consumer choice problem. Besides the learning method, both linear and non-linear classifiers have been used in these Neuromarketing experiments. The most used classification algorithms used in Neuromarketing over the last 5 years are Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Artificial Neural Network (ANN), Naïve Bayes, k-Nearest Neighbor (KNN) and Hidden Markov Model (HMM).

SVM is a supervised learning method, which requires training data for inferring a relation and recognizing patterns. SVM works as a discriminative classifier while a hyperplane separates the different classes. Based on the training data SVM creates a hyperplane which further classifies the new data. The advantage of using SVM in Neuromarketing is its computational simplicity and accuracy level. LDA classifiers are used in several literatures in comparison with SVM classifiers. LDA gathers data points with similar frequencies as distinct groups and 1D Eigen transformation creates the separate classes. Bhardwaj et al. [ 53 ] extracted energy and power spectral density as the feature from the acquired EEG signal and applied SVM and LDA classifiers to classify human emotions from EEG signals. Their model achieved 74.13% average accuracy for SVM-based emotion (happy, sad, anger, disgust, neutral, fear and surprised) classification. In contrast, the model achieved 66.50% average accuracy for LDA-based emotion classification. In the P300 signal-based experiment of Fan and Toyuyama, they used LD classifier to retrieve emotional faces from different subjects.

In 2016, Ogino and Mitsukura experimented on a single-channel EEG device for emotion estimation for mobile application. Their study used SVM, LR, KNN and SVR together to create a model of valence estimation from EEG signals. They used two regression methods linear regression (LR) and support vector regression (SVR) to define valence as sequential value from 1 to 9. SVM and KNN classified nine emotional classes, and SVR minimized the number of sample errors. Rakshit and Lahiri used SVM and interval-type 2 fuzzy classifiers to classify red blue and green colors from EEG signals. Their model achieved the classification with 78.81% average accuracy for SVM-based color classification [ 67 ]. However, IT2FS achieved the highest 80.04% mean accuracy compared to other classifiers in the experiment.

The hidden Markov model (HMM) is non-linear classifier under another supervised learning method. It is derived from statistical modeling and is widely used in temporal and biomedical signals. In Neuromarketing experiments, HMM is used to classify multiclass sequential data where transition from one mental state to another mental state can occur. Researchers can find possible observation of the states using the state transition probabilities. Yadava et al. proposed an HMM-based consumer choice prediction (like/dislike) model using EEG signals from frontal, parietal, temporal and occipital lobe. They compared their classification model with standard classifiers such as SVM, RF and ANN. Their HMM-based model achieved classification accuracy of 70.33% for male test subjects and 63.56% for female test subjects [ 18 ]. In comparison, accuracy of 62.85% was achieved with SVM classifier with C = 6, whereas ANN with two hidden layers achieved 60% average accuracy.

K-Nearest Neighbor algorithm serves both as a classification and regression algorithm. KNN algorithm predicts the test sample’s category based on to the K training samples which are the nearest neighbors to the test sample. In contrast to the hyperplane of SVM, KNN creates a decision boundary among different distinct classes. In the experiment of Chew et al. [ 17 ], SVM and KNN are used to explore the esthetic preference for 3D shapes. The mean accuracy for SVM classifier obtained was 68%, whereas the mean accuracy for KNN classifier was 64%.

Artificial Neural Network (ANN) is a form of neural network classifiers. ANN is a collection of artificial neurons which produces non-linear decision boundaries among large number of classes. ANN and its different subtypes are now becoming more common for the Neuromarketing data interpretation. However, ANN requires large number of sample data and large number of features. Soria Morillo et al. used ANN algorithm in 2015 and 2016 in comparison with Random Forest algorithm C4.5 and Ameva, respectively. In 2015, their advertisement liking recognition model achieved 80% average accuracy with ANN and 69.4% for C4.5 classifier [ 43 ]. In 2016, ANN, C4.5 and Ameva achieved average accuracy of 80%, 69%, and 75%, respectively.

Oon et al. focused on recognizing preference among different categories of products (food, automobile, etc.) using KNN and ANN to analyze non-linear features of the EEG signals [ 55 ]. ANN and KNN inputs were used as the features for Detrended Fluctuation Analysis (DFA) which achieved the highest classification accuracy 80% for alpha waves, and 76.18% for beta waves. Doborjeh et al. [ 64 ] used another type of Neural Network, Spiking Neural Network (SNN) to recognize attention bias pattern from spatio-temporal EEG signal. In their study, a brain-like SNN methodology (NeuCube) was used to create models from EEG signals to evaluate how attention bias can affect the consumer preferences. Their SNN-based classification model achieved 89.95% average accuracy, while traditional machine learning SVM classifier achieved 48.5% accuracy.

4 Result synthesis

This section synthesizes the results from already discussed research articles and book chapters with empirical findings on Neuromarketing, published from 2015 to 2019. To ensure the reliability of the experimental findings, the reviewed literatures had largely set their statistical significance at p < 0.05 [ 20 , 38 , 42 , 43 , 46 , 59 , 60 , 70 ].

With the advancements in technologies, marketing stimuli have become more TV commercial or image of the product oriented rather than the original product [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. 3D image of the products have also added to these virtual product purchase decision-making [ 17 ]. E-commerce products have gained interest among the Neuromarketing researchers, since these products are now more available to the consumers through online shopping [ 34 ]. First-time user experience in online shopping and user experience in online appointment have also diversified the stimuli group of Neuromarketing research. Other than these marketing focused stimuli, some of the Neuromarketing studies focused on social advertisements, particularly the campaign against smoking and alcohol consumption among young adults. These social advertisements used neuroimaging and neural signal decoding techniques to assess and predict the success of their message reaching the targeted social groups.

Analyzing consumer’s emotional response is found as a focus of current Neuromarketing research articles. These experiments widely used Frontal Alpha Asymmetry theory for left and right frontal channel. Besides the alpha band, beta and theta bands are also found in use in these literatures to recognize cognitive and emotional response of the consumers. Table 3 summarizes the findings related to brainwaves and their functionalities in the reviewed Neuromarketing literatures.

Over the last 5 years in consumer neuroscience research, the use of research-grade commercially available EEG devices has become more popular than fMRI scanners. EEG has been particularly used in TV advertisement evaluation, where a high temporal resolution is required to explore the dynamic effects of TV commercials. Even though fMRI has been used less in the Neuromarketing experiments, the use of fMRI is particularly found when a consumer is displayed product images and asked to make purchase decision [ 30 ]. The reason behind using product images as marketing stimuli in fMRI-based Neuromarketing research is that, fMRI can point out the activated brain region when a subject encounters a marketing stimuli. The activated brain region can estimate the positive or negative experience of the consumer in their brain. However, TVC changes stimuli in millisecond time frame, response of which cannot be obtained by an fMRI scanner with 2–5 s image refresh rate. Other than EEG and fMRI, fNIRS has started to enter the Neuromarketing research field. Having the advantage of mobility, fNIRS has been used in purchase behavior correlation and consumer reaction examination by Çakir et al. and Krampe et al. In these cases, fNIRS has shown accuracy over 70% and scored in reliability scale 0.7 out of 1, respectively [ 24 , 60 ]. This shows fNIRS can be a promising mean of neural recording for future Neuromarketing experiments.

While comparing the EEG devices, Emotiv Epoc and Emotive Epoc+ had the largest number of academic research conducted through them. Other than the 14-channel device, BrainAmp is a 10-channel EEG device and eego Sports is a 32-channel device used by Neuromarketing researchers. NeuroSky MindWave despite having only one sensor, provided denoised EEG data and performed well with accuracy over 70%.

All of the fMRI-based Neuromarketing studies over the last 5 years have used 3-Tesla fMRI scanner Magnetom Trio, SIEMENS, and Siemens Verio scanner for their experiments [ 25 , 30 , 62 ]. The advantage of 3.0-T functional MRI is the high spatial resolution. However, BOLD signal-based fMRI has the possible confusion with blood flow due to head or muscle movement.

Signal preprocessing in the selected articles was mainly performed by using MATLAB and EEGLAB. Besides band pass filtering, increased used of independent component analysis (ICA) in spatiotemporal domain is also observed over the course of last 5 years [ 20 , 36 , 38 , 42 , 53 ]. Other than noise and artifact removal, preprocessing dealt with framing or segmentation of the temporal EEG signal. The fMRI data were preprocessed using the statistical parametric mapping (SPM) software.

In this systematic review, a number of Neuromarketing research experiments used artificial intelligent algorithms for prediction and classification purposes. Table 4 compares the average classification accuracy achieved by these algorithms in the selected Neuromarketing studies.

While comparing the classification performance of machine learning algorithms in Neuromarketing research, we found the Artificial Neural Network had the highest classification accuracy around 80% among all other algorithms [ 40 , 43 ]. However, ANN requires more training data than other classifiers such as 70% data in training and 30% in testing, which calls into question its viability in Neuromarketing. After ANN, SVM was the algorithm most widely used in Neuromarketing with the second highest classification accuracy above 70%. HMM performed better than KNN in overall application of machine learning algorithms in Neuromarketing.

5 Recommendation

From this systematic review, authors would like to suggest future Neuromarketing researchers to first define the scope of their inquisition, which defines the rest of the process. Neuromarketing on product purchase assessment and purchase decision-making have been using functional MRI to locate the activated region in consumer brain to predict the success or failure of the product. However, to recognize consumer engagement with product commercial, it is worthwhile to use EEG devices with high temporal resolution. Neuromarketing experiments with EEG devices of 14 channels and 32 channels have established their research-grade performance. However, the raw data availability should be kept in mind by the researchers while selecting an EEG device. Also, researcher should consider availability of bilateral EEG electrodes if they would like to utilize frontal alpha asymmetry theory. Accompanying EEG, eye tracking has also shown high performance in attention and arousal locating. Eye tracker, heart rate monitor, galvanic skin response device can be used alongside brain signal to cross-validate the experimental findings. While choosing among classifiers, although ANN has shown better performance consistently. However, authors would recommend preferring linear classifier over neural networks, as most of the Neuromarketing sampling EEG dataset does not contain plethora of samples to train a complex classifier as ANN.

6 Conclusion

Neuromarketing is an emerging field with opportunities in commercial, social and political advertisement domain. The advancements of this field hence requires proper documentation to capture its state-of-art. This study was conducted with a focus to shed light on the technological scope and possible opportunities in this field. Authors found over the course of last 5 years, Neuromarketing experiments have been conducted mainly with the stimuli of consumer goods, in both product and promotion forms. However, Neuromarketing is showing its possibilities in the domain of social advertisement. Neuromarketing researchers tend to focus on the frontal and prefrontal cortex of consumer brain for cognitive and emotional inquiries. Among all brain signal recording devices, we found EEG is becoming more popular in Neuromarketing experiments, especially with TVC analysis due to its high temporal resolution and cost effectiveness. However, EEG devices have different sampling rates causing a limitation for highest analyzable frequency, which should be under the scrutiny of the researchers. Signal processing in these studies largely adopted ICA for noise and artifact removal. Finally, the highest number of studies have used SVM for classification purpose among all other algorithms, perhaps due to its simplicity. We hope, our findings will guide future researchers to explore the opportunities in this field in a more efficient manner.

Availability of data and materials

This review used available literature relevant to the problem statement from valid databases across the internet. Databases are: Science Direct, Emerald Insight, Sage, IEEE Xplore, Wiley Online Library, and Taylor Francis Online.

Abbreviations

Artificial Neural Network

Discrete wavelet transformation

Detrended fluctuation analysis

Electroencephalography

Functional magnetic resonance imaging

Functional near infra-red spectroscopy

Galvanic skin response

Hidden Markov model

Independent component analysis

K-Nearest Neighbor

Linear discriminant analysis

Magneto encephalography

Principal component analysis

Prefrontal cortex

Support Vector Machine

TV commercial

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Acknowledgements

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This review was conducted under the research grant from Institute of Advanced Research, United International University, Project Code No. IAR/01/19/SE/10. Grant Recipient: Prof. Khondaker Abdullah Al Mamun.

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FSR prepared the manuscript and conveyed systematic literature review. KAM designed and developed the research framework and co-conducted the systematic literature review. Other authors: KMR, SFA, RV, TC and FS provided the conceptual guidelines, reviewed and sorted the selected literatures and contributed in the preparation of the final reviews and draft. All authors read and approved the final manuscript.

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Rawnaque, F.S., Rahman, K.M., Anwar, S.F. et al. Technological advancements and opportunities in Neuromarketing: a systematic review. Brain Inf. 7 , 10 (2020). https://doi.org/10.1186/s40708-020-00109-x

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Neuromarketing: What You Need to Know

Eben Harrell

The field of neuromarketing, sometimes known as consumer neuroscience, studies the brain to predict and potentially even manipulate consumer behavior and decision making. Over the past five years several groundbreaking studies have demonstrated its potential to create value for marketers. But those interested in using its tools must still determine whether that’s worth the investment and how to do it well.

“Neuromarketing” loosely refers to the measurement of physiological and neural signals to gain insight into customers’ motivations, preferences, and decisions. Its most common methods are brain scanning, which measures neural activity, and physiological tracking, which measures eye movement and other proxies for that activity.This article explores some of the research into those methods and discusses their benefits and drawbacks.

Potential users of neuromarketing should be cautious about partnering with specialist consulting firms—experts warn that the field is plagued by vendors who oversell what neuromarketing can deliver. One neuroscience and business professor suggests using a checklist: Are actual neuroscientists involved in the study? Are any of the consultancy’s methods, data, or tools published in peer-reviewed journals? Is its subject pool representative—a question that is particularly important for global brands? Do the consultants have marketing expertise along with scientific knowledge? Do they have a track record of success? And can they prove when challenged that they will offer insights beyond what can be gleaned through traditional methods?

A report on the state of the art

Idea in Brief

The challenge.

Despite recent studies validating the use of neuroscience methods in marketing, marketers struggle with the question of whether neuromarketing is worth the investment, what tools and techniques are most useful, and how to do it well.

The Solution

Marketers need to understand the range of techniques involved, from brain scanning methods to testing of physiological proxies; how they are being used in both academia and industry; and what possibilities they hold for the future.

The Benefits

By understanding the landscape, marketers can make better decisions about when to pursue a neuromarketing technique to gain insight into customers’ motivations and when and how to engage an outside firm as a partner.

Nobel Laureate Francis Crick called it the astonishing hypothesis: the idea that all human feelings, thoughts, and actions—even consciousness itself—are just the products of neural activity in the brain. For marketers the promise of this idea is that neurobiology can reduce the uncertainty and conjecture that traditionally hamper efforts to understand consumer behavior. The field of neuromarketing—sometimes known as consumer neuroscience—studies the brain to predict and potentially even manipulate consumer behavior and decision making. Until recently considered an extravagant “frontier science,” neuromarketing has been bolstered over the past five years by several groundbreaking studies that demonstrate its potential to create value for marketers.

Eben Harrell is a senior editor at Harvard Business Review. EbenHarrell

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Research Insight June 18, 2019

The Top 5 Neuromarketing Research Studies

Brendan Murray

Over the past decade, the use of neuroscience tools in market research has progressed from a “nice to have” add-on, to an essential part of any consumer research study . As we have detailed before on this blog, neuroscience tools are applied daily to a wide variety of use cases: Video advertising, driving and in-vehicle experiences , sensory evaluations, shopper experiences, and many more.

What all of these use cases have in common, however, is that they are all trying to achieve the same goal: To gain a better understanding of why humans behave the way that we do.

Today’s questions – such as being able to apply valid neuroscientific techniques to study how people respond to a virtual reality environment – did not just spring fully-formed into the research world, though. Instead, today’s questions have been built on decades of academic and industry research , which has provided the guard rails for what can (and cannot!) be validly measured in the moment from consumers.

Today, we will take a step back from the exciting consumer research being conducted in 2019, and look at some of the seminal research that has helped move the neuromarketing industry to where it is today. How can neuroscience predict what small-ticket consumer goods people will purchase? Are Super Bowl advertisers spending their ad dollars as efficiently as possible? Can neuroscience help researchers effect social change, like promoting smoking cessation and creating powerful female superhero role models? Read on!

The Origins of Neuromarketing: The Somatic Marker Hypothesis

Case study: In lab studies vs. in-market outcomes
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Neuromarketing eye tracking perfume shopping webpage

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What is Neuromarketing?

Neuromarketing is an innovative field that merges neuroscience with marketing to understand and influence consumer behavior. By studying how the brain responds to marketing stimuli, companies aim to design products, advertisements, and marketing campaigns that are more effective and engaging.

At the core of neuromarketing is the belief that consumer decision-making is largely unconscious. Traditional marketing methods rely on self-reported data, such as surveys and focus groups, which can be unreliable due to biases and the limitations of self-awareness. Neuromarketing bypasses these issues by directly observing the brain’s responses.

Techniques used in neuromarketing include functional Magnetic Resonance Imaging ( fMRI ) and Electroencephalography ( EEG ). fMRI tracks blood flow in the brain to identify areas that become active in response to certain stimuli, while EEG monitors electrical patterns to understand emotional responses. Eye tracking and measuring changes in skin conductance are also used to gauge interest and arousal.

By analyzing this data, marketers can understand what captures attention, triggers emotions, and prompts purchasing decisions. For instance, neuromarketing can reveal how different colors, words, or product placement can impact a consumer’s subconscious perception and buying behavior.

However, neuromarketing also raises ethical concerns. Critics argue that it could lead to manipulation, exploiting subconscious triggers to drive consumer behavior without their awareness. As such, there is an ongoing debate about the ethical implications of using neuroscience in marketing.

In conclusion, neuromarketing represents a frontier in understanding consumer behavior, offering deeper insights than traditional marketing methods. While its potential is vast, it also necessitates careful consideration of ethical boundaries in its application.

Our review of seminal neuromarketing studies begins not with a neuromarketing study, but with a line of academic research. Previously in our blog, we have detailed the many ways in which various neuroscientific tools – electrodermal activity , electroencephalography, facial expressions, and so on – can be used to understand the human emotional experience.

No review of “important neuromarketing studies” would be complete, however, without a brief reflection on one of the most important academic hypotheses linking brain to behavior: the somatic marker hypothesis [1].

Antonio Damasio is a highly-influential neurologist and emotion neuroscientist, whose work with brain-injured patients helped identify the importance of brain-based processes in both the conscious and non-cons cious components of decision-making. Damasio and his colleagues sought to understand why patients with damage to certain parts of their brains made decisions differently from healthy individuals, and published extensively on the link between electrodermal activity and choice.

Based on his work, Damasio posed the somatic marker hypothesis (SMH) as a way of explaining how the brain and body work in concert with one another to lead individuals towards the decisions they make.

In short, the SMH suggests that decision making is actually a learning process. When we make a decision, or a choice, and we experience the outcome, we will have some low-level (or even more overt!) emotional response to that outcome. That emotional response is manifested as some series of bodily reactions – an increase in skin sweat, a decrease in heart rate variability, an expression of emotion on our face – and information about that response is stored as a “somatic marker” ( soma meaning “body”) in the brain.

When an individual later finds themselves in a similar situation, or making a similar choice, the relevant somatic markers are retrieved and provide information to help guide the decision process.

The SMH laid the groundwork for much of modern neuromarketing, by showing that we do not need to image consumers’ brains, using something like functional MRI, to understand when they something is getting an emotional response from them. Measures like electrodermal activity, although taken from the periphery of the body, can directly reflect processes in the brain that are related to decision making.

This provided earlier neuromarketers with a means by which they could non-invasively gain more insight into consumers’ behavior, and which could be done in a scalable fashion.

Neuromarketing research: In lab studies vs. in-market outcomes

One of the “holy grails” of neuromarketing is demonstrating that in-lab neuroscience measures relate, in some meaningful way, to a company’s key market outcomes. Neuromarketers are consistently tasked – as they should be – with providing validation that their measures are significantly related to consumer behavior.

The best of these validation studies are the ones in which the teams collecting and analyzing the data are blind to the in-market outcomes, ensuring that the research results are unbiased.

Check out: 5 Marketing Myths Disproved by Neuromarketing

An excellent demonstration of this type of validation came in 2012, from Innerscope Research (now Nielsen Consumer Neuroscience). Innerscope was approached by Mimoco, a consumer electronics retailer, to test their “Mimobots” line of designer USB flash drives. These flash drives were designed to look like stylized characters, both pre-existing (e.g., Hello Kitty) and some entirely novel designs.

Mimoco had online sales data for hundreds of their designer flash drives, and provided Innerscope with a subset of about 30 of their top- and bottom-performing designs to test in the lab. Innerscope was blind to which designs had sold well, and which had sold poorly.

The designs were tested using a static, screen-based methodology in the lab, while EDA, heart rate, and eye tracking data were collected from respondents who were passively viewing the designs.

Innerscope then ranked the flash drives, from #1 to #30, based on data from the combination of biosensors. When Mimoco lined up Innerscope’s ranking with their own sales data, the neuroscience data correctly identified four of the top five sellers, and also correctly identified a number of the bottom-selling designs.

Overall, the in-lab results were able to explain 50 % of the variability of in-market sales. Although there had been several studies prior to this one demonstrating relationships between neuromarketing techniques and in-market behavior, this was one of the most prominent single-blind studies to show that neuroscience tools can be used to reliably predict the sales of a high-volume consumer electronics product (while also highlighting the value of a biosensor-based research ).

3 Popular Neuromarketing Studies

1. case study: sales of fast-moving consumer goods (fmcg).

Although the Mimoco study is remembered more for its design and results than its market impact, the next case study on our list made a much broader splash in 2016. In an ever-changing broadcast media landscape, CBS was looking to gain a deeper understanding into what made for effective television advertising.

As David Poltrack, the former Chief Research Officer at CBS, said, “With today’s consumer continuously exposed to more and more media messages, it is increasingly difficult for an advertiser to develop creative that breaks through and resonates with the target audience.”

To this end, CBS partnered with Nielsen Consumer Neuroscience and Nielsen Catalina Solutions, to extend the concept of “neuroscience validation” to the broad category of fast-moving consumer goods. Nielsen Consumer Neuroscience tested 60 ads from a variety of FMCG categories (including food, household goods, beauty products, etc.). These ads were tested in Nielsen’s labs around the US, using a combination of EEG, EDA, and facial expression analysis .

Nielsen Catalina Solutions then provided data for each of those ads, using a combination of set-top box data and retail purchase data, to identify sales “lift” for households who were exposed to the advertisements versus not exposed. The researchers then looked at how well the in-lab neuroscience measures were able to predict which ads generated more sales lift in-market, and which ads didn’t move the needle.

The results demonstrated that not only were the neuroscientific measures able to significantly predict sales data for this large number of ads, but that – once again – a multimodal approach that incorporated all three measures ( EEG , EDA, and FEA) was the best predictor of sales lift.

At a time when network broadcast companies were looking for new and better ways to make an emotional connection with their TV viewers, this large-scale validation partnership made a huge impact in the advertising research world.

2. Case Study: Predicting Smoking Cessation Behavior

One of the most heavily-cited studies in the neuromarketing field wasn’t actually a neuromarketing study at all; rather, it was a study showing how neuroscience could be used to identify more effective public service advertising.

Emily Falk, a neuroscientist at the University of Pennsylvania, wanted to understand the degree to which activity in the brain – specifically in the ventromedial prefrontal cortex (vmPFC) , which has been implicated heavily in decision-making processes – would predict call volume in response to the US National Cancer Institute’s anti-smoking campaigns [2].

Falk and her colleagues used functional MRI to test three TV ad campaigns promoting the USNCI’s 1-800-QUIT-NOW hotline, looking specifically at the degree to which each of the ads elicited activity in the vmPFC. They then compared the neuroimaging results to both self-reported ratings of “effectiveness” of the ads, as well as the call volume generated by each advertisement.

While there was no relationship between self-reported efficacy of the advertisements and called volume, vmPFC activity tracked directly with how many calls the 1-800-QUIT-NOW hotline received.

Although fMRI research is rarely conducted in the neuromarketing industry because of cost, logistics, and perceived invasiveness, the study nevertheless has been hugely influential with agencies that seek to promote better human behavior . The Ad Council, for example, frequently employs neuroscience testing to hone their public service advertisement creative – everything from ads for pet adoption to inspiring fathers to be more involved in their children’s lives.

3. Case study: The Importance of Female Superheros

Continuing on the topic of using neuroscience to affect positive change, iMotions recently partnered with Screen Engine/ASI, BBC America, and the Women’s Media Center to understand how various depictions of female superheroes are experienced by teenage males and females.

In the latest installment of BBC’s wildly-popular Dr. Who series, the main character was being portrayed by a woman for the first time in the show’s decades-long run. In addition to wanting to know how the “female Doctor” would be received by audiences, BBC, SE/ASI, and the WMC wanted to know how portrayals of superheroes – both male and female – impacted measures like self-esteem and self-confidence in younger demographics who looked to superheroes as role models.

The researchers tested a variety of TV trailers for programs anchored by superheroes like The Flash, Supergirl, Wonder Woman, and Luke Cage. An audience of late-adolescent males and females watched the trailers while the researchers measured their EDA, facial expressions, and visual attention (using eye tracking).

One of the key findings from the research was that young women responded much more favorably to depictions of female superheroes acting, well – like superheroes. Supergirl saving a plane from crashing, for example, resonated strongly with both female and male audiences. Conversely, the sexualization of female leads consistently led female viewers to tune out.

The use of neuroscience tools in this study revealed important guard rails around the most effective creative treatment of female superheroes. The researchers hope that this investigation will help generate change in the approach to storytelling in TVs and movies, and that the increased prevalence of female superheroes will continue to provide role models to a younger generation of female viewers.

Limits of Neuromarketing research

Of course, not all of neuromarketing’s history is paved with sales validation, smoking cessation, and promotion of gender equality in media. A handful of the best-known neuromarketing studies are famous for their infamy. These studies have nonetheless added great value to the field, by educating both neuromarketing consumers and providers about what these types of tools cannot do.

One highly-visible study was published as part of a New York Times op-ed in the fall of 2011. Martin Lindstrom describes a functional MRI study that he had conducted in collaboration with the now-defunct MindSign Neuromarketing. Respondents in the study were placed in an MRI scanner and exposed to ringing iPhones, and the researchers noted activation of (among other regions) the insular cortex in the brain.

Briefly, the insula is a “jack of all trades” region of the brain that is implicated in a wide variety of cognitive processes, including emotion processing, sensory integration, experiential consciousness, and many others [3]. Lindstorm picked just one of those functions – the experience of “love and compassion” – and concluded that respondents felt the same kind of love for their iPhone as they do for family or other loved ones.

Drawing that type of after-the-fact conclusion is similar to saying, “My new coworker is wearing sneakers today, so they must be a marathon runner”. Sure, that could be! But people also wear sneakers because they’re comfortable, or because they have a long walk to the office in the morning, or because they like the way they look, or any number of other reasons.

This is called “reverse inference” – finding a result, and cherry-picking an explanation for it after the fact, rather than coming up with a real hypothesis first – and Lindstorm’s assertion did not go unnoticed. A group of more than 40 academic neuroscience researchers co-authored a response letter to the New York Times, highlighting the lack of scientific validity of the claim.

Over the years, there have been similar, highly-visible examples of neuromarketing “gone wrong”. These serve as important cautionary tales for research consumers and providers alike: Neuromarketing must, above all else, be credible to be valuable, and an educated market is one that won’t let bad research slide. In a rapidly-growing industry, keeping these studies in mind is critical for holding everyone accountable for doing the best research they possibly can.

Frequently Asked Questions

What are the ethical issues of neuromarketing research.

Neuromarketing research raises several ethical concerns. One of the primary issues is the potential for manipulation. By understanding and targeting the subconscious drivers of consumer behavior, there is a risk of manipulating customers without their conscious consent. This raises questions about consumer autonomy and the ethics of influencing decision-making at a subconscious level.

Another concern is privacy. Neuromarketing involves collecting sensitive data about an individual’s preferences and brain activity, which could potentially be misused if not properly safeguarded. The confidentiality and use of this data is a significant ethical consideration.

Finally, there’s the issue of fairness. The use of neuromarketing techniques might create an uneven playing field, where companies with access to these advanced tools have an undue advantage over competitors and consumers, potentially leading to market distortions.

What are the problems with neuromarketing?

Apart from ethical concerns, neuromarketing faces several practical and scientific challenges. The reliability of neuromarketing methods is a significant issue. Brain imaging and measurement techniques can vary in their accuracy and may not always provide consistent results. The interpretation of neurological data can be complex and subjective, leading to potential inaccuracies in understanding consumer behavior.

Putting it All Together

The neuromarketing industry has come a long way in the last 15 years. There has been more groundbreaking research done than we have space to share here, and there have been some stark examples of “how not to do neuromarketing”, as well. We hope that this overview of a few seminal studies has been interesting, and if you’d like to learn more, be sure to keep an eye on our blog for more exceptional case studies in the future!

References:

[1] Damasio, A. R. (1996). The somatic marker hypothesis and the possible functions of the prefrontal cortex. Philos Trans R Soc Lond [Biol] 351:1413–1420.

[2] Falk, E. B., Berkman, E. T., Mann, T., Harrison, B., & Lieberman, M. D. (2010). Predicting persuasion-induced behavior change from the brain. The Journal of Neuroscience , 30, 8421–8424.

[3] Gasquoine PG (2014). Contributions of the insula to cognition and emotion . Neuropsychol Rev 24 : 77–87.

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Neuromarketing for Design Thinking: The Use of Neuroscientific Tools in the Innovation Process

First Online: 19 April 2022

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Flor Morton 3

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The growing competitiveness in the current market environment urges companies to innovate not only to succeed but also to survive. Nowadays, a popular methodology of innovation is design thinking, which is in its early stages in both the academic literature and practice. In the past few decades, several marketing researches for both academic and commercial purposes are trying to understand consumers’ responses to marketing stimuli through the use of neuroscience tools. Although neuroscience tools have been used to facilitate product development and testing as well as improve innovation processes and results, this approach is also in its early stages. This chapter brings together the literature on consumer neuroscience and design thinking and suggests that neuroscientific tools used in neuromarketing can be applied in the phases of an innovation process. Therefore, the chapter reviews some of the most commonly used neuroscientific tools in marketing, provides some ideas on how to integrate the use of these tools into the phases of the design thinking methodology and closes with a discussion on ethical issues associated with the use of these tools for innovation.

Consumer neuroscience
Neuromarketing
Design thinking
Product development

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Morton, F. (2022). Neuromarketing for Design Thinking: The Use of Neuroscientific Tools in the Innovation Process. In: Machado, C., Davim, J.P. (eds) Organizational Innovation in the Digital Age. Springer, Cham. https://doi.org/10.1007/978-3-030-98183-9_2

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International Journal of Case Studies in Business, IT, and Education (IJCSBE)

DSOUZA P R I M A FREDERICK

The concept of neuro-marketing is explored as an emerging economic approach that originated from human brain research and traditional marketing facts. The core idea of the paper is to understand the concept of Neuromarketing and its influence on customers' decisionmaking process. Design: The benefits of various neuromarketing tactics on various market input devices are studied in the paper. From available literature and research studies, the influence of neuromarketing's various techniques and procedures on verifiable marketing success is been highlighted. Findings: Neuromarketing concept has gain lot of importance in recent years. It has contributed in the various fields of marketing such as framing marketing strategies, selection of brand, consumer behaviour, advertising, ethical concerns and decision-making. The study highlights the importance of neuromarketing principles and concepts for engaging neuroscience in the field of marketing and understanding consumer behaviour which could help in planning new marketing strategies based on neuroscience. Originality: The impact of sensory aspects on a customer's perception and conscious or subconscious purchasing choice is highlighted in this study. It also addresses the ethical problems that have been raised concerning neuromarketing. In this study, the benefits, limitations, ethical difficulties, and future potential of neuromarketing are discussed. Value: A neuromarketing study will help the companies compete for market leadership, increase customer base and convert them into loyal consumers. It will help to determine what the customer wants, what services he likes, and how to draw the consumer's attention. Marketers can understand customer behaviour, including how it reacts to a company's advertising, brand, and product quality. Neuro-marketing can help a marketer to increase their turnover.

Computational Intelligence and Neuroscience

Giulia Cartocci

The new technological advances achieved during the last decade allowed the scientific community to investigate and employ neurophysiological measures not only for research purposes but also for the study of human behaviour in real and daily life situations. The aim of this review is to understand how and whether neuroscientific technologies can be effectively employed to better understand the human behaviour in real decision-making contexts. To do so, firstly, we will describe the historical development of neuromarketing and its main applications in assessing the sensory perceptions of some marketing and advertising stimuli. Then, we will describe the main neuroscientific tools available for such kind of investigations (e.g., measuring the cerebral electrical or hemodynamic activity, the eye movements, and the psychometric responses). Also, this review will present different brain measurement techniques, along with their pros and cons, and the main cerebral indexes linked to the spe...

The knowledge about the brain and therefore the interest in the topic of neuromarketing has increased in recent years. Therefore the purpose of this paper is to examine the added value of neuromarketing tools in the area of marketing research. There is no literature which includes all the aspects of marketing research and the added value of neuromarketing tools to these aspects. In regard to the topic of neuromarketing this study will be done on the basis of a critical literature review. The inability of people to describe their feelings as a method of self-assessment is one of the most important reasons why neuromarketing could be useful. Another reason mentioned is that the brains of people also contain hidden information about their true preferences. Such information can be used to influence their buying behaviour. After a brief explanation of the neuromarketing techniques like the outside reflexes, input-/output models and the inside reflexes, this paper examines the added value of these neuromarketing tools in the area of marketing research. The results indicate a positive contribution of neuromarketing tools to the aspects of identifying the customers' needs and wants and to all four aspects of the integrated marketing program: product, price, distribution and promotion. Therefore it may be concluded that neuromarketing adds a lot of value to the marketing research area.

Dr. Harit Kumar

In current volatile environment, advanced marketing research methods are needed to identify preferences or constraints for fulfilling the needs of customers. Neuromarketing research tools have capacity to study the brain of consumers and may provide answer of many unanswered questions related with consumers. This research demonstrates the complementary role of neuromarketing techniques played in understanding the aspects of consumer behavior. It focuses on the prospective usage of Eye Tracking as techniques of neuromarketing for conducting market research. This research is conceptual in nature and based on literature review. The outcomes of this paper suggest prospective uses for Eye Tracking in marketing strategies, such as segmentation, targeting and positioning. It can be alleged that in the current and upcoming scenarios neuromarketing techniques especially Eye Tracking will be a part of marketing research.

IOSR Journals

Asian countries like India represent large untapped markets in Neuromarketing. Hence, there is a need of this approach to conquer the genuineness of customer intention across brands as there are hardly any business practices which examines the genuineness in consumer behavior in developing county like India. This paper is an exploratory study through scholarly contributions of extant Literature indicating neuroimaging techniques as a branch of neuroscience can provide better valuable insights of consumers through unconscious process results in genuinity of consumers preferences and behavior. Finally, study outlines the techniques such as fMRI, EEG and MEG adopted in Neuromarketing and strategies in purchase in consumers' purchase decision-making. JEL :M31,M37

Adoption of Innovation

Xavier J , Sharad Agarwal

Eglė Taraskevičiūtė

This study is yet another response to the ongoing neuromarketing debate in both scientific and business fields. However, this paper is unique due to its qualitative focus on the companies that adopted neuromarketing as one of their marketing research tools. The study aims to narrow the gap between the scientific and practical approaches to neuromarketing. This paper answers the question of how do companies motivate their decisions to implement neuromarketing into their marketing research actions and how do they perceive the method in the final result. Accordingly, the opinions of scientists, neuromarketers and businesses are taken into account. The inquiry is carried out by reviewing neuromarketing literature and giving a theoretical framework of organisational decision making and organisational buying. The empirical data is collected with the help of semi-structured in-depth interviews and analyzed through the perspective of the discourse. The study is carried out in Sweden and, therefore, represents the situation of neuromarketing in this country. The results of this paper point to the irrational decision making when buying neuromarketing services and realistic expectations that the companies have in terms of price, performance and outcomes. The research reveals the high trust environment and the high importance of buyer-seller relationships that are present in the market of neuromarketing in Sweden.

Hakan BOZ , Aytuğ Arslan , ERDOGAN KOC

TO CITE: Boz, H., Arslan, A., and Koç., E., (2017), Neuromarketing aspect of tourism pricing psychology, Tourism Management Perspectives, 23(2017), 119-128. The price of a product is the key determinant of the revenues and profits of a tourism or hospitality business. Customers form their value judgments of a touristic product or service based on the price they have paid. Moreover, the price of a touristic product or service may have psychological influences on the customer. Thus, the way prices are perceived by potential tourists is of paramount importance. Against this backdrop, this study aims to provide insight into how tourists perceive prices and pricing issues. In particular, it provides neuromarketing examples to explain how tourists perceive prices in holiday advertisements in terms of design features, positioning and content.

selcuk koyluoglu

The purpose of this chapter, which is designed to measure where and how the consumer focuses in an advertising brochure, which visual is more striking, and how much eye strain (twitch) it takes, is to measure the density and visual attention of the eyes through the eye-tracking device during the individual examination. For this study, an experimental laboratory for neuromarketing research was used. After watching the videos and images of the participants in the eye-tracking module, the general evaluations were taken to determine what they remembered, and a comparison opportunity was born. According to the findings, logos, and photographs are more effective than texts. Viewers read large text and skip small text. Suggestions for future research are presented in the chapter.

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Full article: Neuromarketing research in the last five years: a
Neuromarketing (NM) is an application of neuroimaging and physiological tools to record the neural correlates of consumers' behaviour (e.g., decision-making, emotion, attention, and memory) toward marketing stimuli such as brands and advertisements. This study aims to present the current tools employed in the empirical research in the last ...
10 Recent Neuromarketing Research Studies with Real-World-Examples
Instead of focusing solely on what we self-report in qualitative surveys, neuromarketing examines how our brain responds to stimuli. 1. "Multiple 'buy buttons' in the brain: Forecasting chocolate sales at point-of-sale based on functional brain activation using fMRI". Takeaways.
A review of research on neuromarketing using content analysis: key
Most papers related to neuromarketing cover many topics, ranging from literature reviews on specific applications, experiments with neuroscientific techniques or new applications of neuromarketing, to new neuroscientific tools. Thus, some papers included in this cluster start with a theoretical review of neuromarketing before using a specific ...
Popular Topics
What's popular at Neuromarketing varies over time, of course, but below are some selections our readers have found interesting over time. Since our main article listing is chronological, this is an alternate way to find what you are looking for. If you don't see your topic listed, just use the search box above!
Accessing Neuromarketing Scientific Performance: Research Gaps and
The topic emerged in 2007, after which publications have consistently increased up to 2020. An analysis of this topic on the Scival platform (performed in 13 March 2021) reveals that 640 articles were published worldwide from 2010 to 2020, 27% of which were published in neuromarketing field. This is a high figure compared to overall world ...
Technological advancements and opportunities in Neuromarketing: a
Neuromarketing has become an academic and commercial area of interest, as the advancements in neural recording techniques and interpreting algorithms have made it an effective tool for recognizing the unspoken response of consumers to the marketing stimuli. This article presents the very first systematic review of the technological advancements in Neuromarketing field over the last 5 years ...
PDF A NEUROMARKETING PERSPECTIVE OF GREEN CONSUMERISM A case study of ...
MASTER'S THESIS 2019 A NEUROMARKETING PERSPECTIVE OF GREEN CONSUMERISM A case study of behavioural science Msc. in Economics and Business Administration, International Business (cand. merc) Student: Alexandra Dias Student Number: 93004 Supervisor: Jesper Clement Number of characters :156.079 Number of pages: 80 Date of submission: 15-05-2020
Neuroscience in business-to-business marketing research: A literature
By connecting business-to-business marketing topics with the identified clusters, their orientations to neuroscience and their integration strategies (see Fig. 5), a non-exhaustive research agenda for the integration of business-to-business marketing and neuroscience is proposed. • Social or organisational neuroscience in multi-agent systems.
PDF Unveiling Neuromarketing and Its Research Methodology
voting behaviours, and neuromarketing is understood in a more practical and business way as a set of neurophysiological tools used by market research companies or practi-tioners [8]. In the same vein, Nemorin (2018, p. 56) understands "neuromarketing as a commercialised market research method for studying brain activity that combines the
Neuromanagement and Neuromarketing
Keywords: Neuromarketing, neuromanagement, decision making, emotion, consumer neuroscience . Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements.Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer ...
(PDF) Neuromarketing Approach: An Overview and Future ...
Neuromarketing is the embryonic field of marketing science. Despite being controversial, it remains the most promising field to study genuine consumers' responses in front of the marketing stimuli ...
Neuromarketing Research Topics
Top Quality Thesis Topics in Neuromarketing. Topic 11: Impact of Audio Branding on Brand Recognition and Recall : A Systematic Review. Topic 12: Critical Evaluation of the Ethical Considerations in Political Neuromarketing. Topic 13: How Brands Use Facial Coding as a Neuromarketng Technique: A Systematic Literature Review.
Demystifying neuromarketing
1. Introduction. Neuromarketing is a popular topic and area of research in marketing science. In essence, the goal of neuromarketing is to adapt theories and methods from neuroscience and combine them with theories and methods from marketing and related disciplines, such as economics and psychology, to develop neuroscientifically sound explanations of the impact of marketing on target customer ...
Neuromarketing: Understanding the Application of Neuroscientific
A growing interest within marketing research is the movement away from self-reported consumer research toward the use of direct neuroscientific methods—characterized as neuromarketing (see Fig. 2.1).Neuromarketing is the application of neuroscience measurement techniques for understanding how consumers respond, both consciously and unconsciously, to marketing (Lee et al. 2007).
Neuromarketing: A Review Regarding Marketing Field
The results showed important insights such as neuromarketing topic has begun to rise after 2010; neuroscience methods such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), functional magnetic resonance imaging (fMRI), and other important methods have been used in marketing field; neuromarketing-related methods are ...
Neuromarketing: What You Need to Know
EbenHarrell. The field of neuromarketing, sometimes known as consumer neuroscience, studies the brain to predict and potentially even manipulate consumer behavior and decision making. Over the ...
The Top 5 Neuromarketing Research Studies
2. Case Study: Predicting Smoking Cessation Behavior. One of the most heavily-cited studies in the neuromarketing field wasn't actually a neuromarketing study at all; rather, it was a study showing how neuroscience could be used to identify more effective public service advertising.
PDF The Minds of Customers Through the Lens of Neuromarketing and ...
marketing strategies. The thesis' premise is a clear vision of developing the case company's marketing strategies, aligning it with the thesis topic. 2 Methodology The idea for the thesis topic arose from the author's own interest in marketing and consumer behavior, with a deep interest especially in neuromarketing and behavioral sciences. The
Behavioral Sciences
In terms of originality, this study is the first to apply SciVal topic prominence to a bibliometric analysis of neuromarketing, and provides a new bibliometric indicator for neuromarketing research. (1) Background: Using neuroscience to understand and influence consumer behavior often leads to ethical controversy.
Neuromarketing for Design Thinking: The Use of ...
Hence, consumer neuroscience is understood as the use of neuroscientific tools to provide a better understanding of consumer behaviour and contribute to the theory on this topic; neuromarketing is research conducted with neuroscientific tools for a specific company with the purpose of using the resulting information and insights of the research ...
Anida Krajina Master Thesis final Neuromarketing in Practice
The outcomes of this paper suggest prospective uses for Eye Tracking in marketing strategies, such as segmentation, targeting and positioning. It can be alleged that in the current and upcoming scenarios neuromarketing techniques especially Eye Tracking will be a part of marketing research. Download Free PDF. View PDF.
Dissertations / Theses on the topic 'Neuromarketing'
List of dissertations / theses on the topic 'Neuromarketing'. Scholarly publications with full text pdf download. Related research topic ideas.
Neuromarketing thesis. Which topic to write about? : r/marketing
The topic needs to be something you're extremely interested in or passionate about because throughout the journey if you're not fully invested you will lose interest and just be miserable. For instance, if I were having to write a 70 page paper, I would focus on non-profit marketing as my main group then break it down into subsections that I'm ...

Popular Topics

Accessing Neuromarketing Scientific Performance: Research Gaps and Emerging Topics

Meng-Mei Chen

Arthur Araújo

Giovana Goretti Feijó de Almeida

1. Introduction

2. Mapping Neuromarketing Science

2.2. Mapping Research Topics through Bibliometric Studies

3. Materials and Methods

3.1. Indicators and Methods Used

3.2. Data Collection and Organization Procedures

3.3. Data Analysis Techniques and Procedures

4.1. Neuromarketing: Overall Research Performance

4.2. Performance of the Most Cited Neuromarketing Articles

4.3. Performance of Most Prominent Neuromarketing Topics: Author Word Network

4.4. Performance of Prominent Scival Neuromarketing Topics

4.5. Neuromarketing

5. Conclusions

Contributions, Implications, and Future Research

Author Contributions

Conflicts of Interest

Technological advancements and opportunities in Neuromarketing: a systematic review

1 Introduction

2 Methodology

3 Systematic review on the advancements of Neuromarketing

3.1 Marketing stimuli used in Neuromarketing

3.2 Activation of brain regions due to marketing stimuli

3.3 Neural response recording techniques

3.4 Brain signal processing in Neuromarketing

3.5 Machine learning application in Neuromarketing

4 Result synthesis

5 Recommendation

6 Conclusion

Availability of data and materials

Abbreviations

Acknowledgements

Author information

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