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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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Neuromarketing: How-To Guide with Examples and Case Studies

Neuromarketing combines the study of psychology, neuroscience, and marketing to understand consumer behavior and decision-making processes.

Neuromarketing has been around since the 1990s, but it gained popularity in the early 2000s. Today, it’s a rapidly growing industry with businesses of all sizes using Neuromarketing techniques to gain an edge in the competitive market.

In today’s digital era, where consumers are constantly bombarded with advertisements, it’s becoming more and more important to understand what motivates their behavior. This is where Neuromarketing comes in. By using scientific techniques to measure and analyze brain activity, Neuromarketers can gain insights into what makes consumers tick and use that information to create effective marketing campaigns.

So, are you ready to dive deeper into the world of Neuromarketing? Let’s go!

Table of Contents

What is Neuromarketing?

Now that we’ve covered the basics, let’s explore Neuromarketing in more detail.

As we mentioned earlier, Neuromarketing is the study of consumer behavior and decision-making processes using a combination of psychology, neuroscience, and marketing. This approach allows businesses to gain insights into what motivates consumer behavior and create marketing strategies that resonate with their audience.

At its core, Neuromarketing is based on the idea that much of our decision-making occurs on a subconscious level. By measuring and analyzing brain activity, Neuromarketers can gain insights into how consumers perceive and respond to marketing messages, which can help businesses create more effective campaigns.

Why is Neuromarketing important for businesses?

Neuromarketing is especially important for businesses in today’s competitive market. With so many brands vying for consumer attention, it’s becoming increasingly difficult to stand out. By understanding what motivates consumer behavior, businesses can create marketing campaigns that are more engaging and persuasive, ultimately driving sales and increasing brand loyalty.

Overall, Neuromarketing is an innovative approach to marketing that has the potential to revolutionize the way businesses reach and engage with their audience. It’s a powerful tool that can provide valuable insights into consumer behavior, helping businesses create more effective marketing campaigns and achieve greater success.

How Does Neuromarketing Work?

So, how does Neuromarketing actually work? Let’s take a closer look.

Neuromarketing involves using a variety of scientific techniques to measure and analyze brain activity in response to marketing stimuli.

Here are some of the most common techniques used in Neuromarketing:

1. Eye-tracking

Eye-tracking technology is used to monitor where consumers look when they’re presented with a marketing message. By tracking eye movements, Neuromarketers can gain insights into what aspects of the message are most engaging or distracting.

2. EEG (electroencephalography)

EEG measures brain activity by recording electrical signals on the scalp. By analyzing these signals, Neuromarketers can gain insights into a consumer’s emotional and cognitive responses to marketing messages. You can read more about the EEG-based neuromarketing approach here .

3. fMRI (functional magnetic resonance imaging)

fMRI uses magnetic fields to measure changes in blood flow in the brain, which are indicative of neural activity. By measuring brain activity in response to marketing stimuli, Neuromarketers can gain insights into how consumers perceive and respond to marketing messages.

Neuromarketing is an innovative approach to understanding consumer behavior. While it has its limitations, it also offers a range of benefits over traditional marketing methods, making it an increasingly popular approach for businesses looking to gain a competitive edge. You can read more about fMRI here .

Advantages of Neuromarketing

Now that we’ve covered the basics of Neuromarketing and how it works, let’s take a closer look at some of the advantages it offers businesses.

Helps in better understanding consumer behavior

By measuring brain activity, Neuromarketing can provide insights into how consumers perceive and respond to marketing messages. This can help businesses better understand consumer behavior and create more effective marketing campaigns.

Enables businesses to create targeted marketing campaigns

With the insights gained from Neuromarketing, businesses can create more targeted and personalized marketing campaigns that resonate with their audience. This can lead to increased engagement, higher conversion rates, and ultimately, greater sales.

Provides insights on product design and packaging

Neuromarketing can also be used to gain insights into how consumers perceive and respond to product design and packaging. By measuring brain activity in response to different design elements, businesses can identify which elements are most appealing to consumers and use this information to create more effective product designs and packaging.

Overall, Neuromarketing offers a range of benefits for businesses looking to better understand their audience and create more effective marketing campaigns. By using the insights gained from Neuromarketing, businesses can improve their marketing efforts, increase engagement, and ultimately drive sales and revenue.

Disadvantages of Neuromarketing

While Neuromarketing offers a range of benefits for businesses, there are also some potential disadvantages to consider.

Expensive techniques and equipment

The techniques used in Neuromarketing, such as EEG and fMRI, can be expensive and require specialized equipment and expertise. This can make it difficult for smaller businesses to invest in Neuromarketing.

Ethical concerns surrounding privacy and data protection

Neuromarketing involves collecting and analyzing data on consumers’ brain activity, which can raise ethical concerns around privacy and data protection. It’s important for businesses to be transparent about how they collect and use this data and to ensure that they are in compliance with relevant laws and regulations.

It’s worth noting , however, that many of these concerns are not unique to Neuromarketing and are also applicable to other forms of data collection and analysis. With proper planning and ethical considerations, the potential drawbacks of Neuromarketing can be minimized, and the benefits can be fully realized.

Neuromarketing usage in different industries

Neuromarketing has gained popularity in a wide range of industries in recent years, and businesses in different sectors are finding ways to leverage its benefits.

Let’s take a closer look at some examples of Neuromarketing in different industries and how businesses are benefiting from it.

Neuromarketing is increasingly being used in the healthcare industry to gain insights into patients’ decision-making processes and improve the patient experience. For example, researchers have used EEG to study patients’ responses to different types of medical advertising, such as direct-to-consumer ads for prescription drugs.

Retailers are using Neuromarketing to better understand consumer behavior and create more effective in-store experiences. For example, some retailers have used eye-tracking technology to study how customers move through their stores and which products they spend the most time looking at.

Entertainment

Neuromarketing is also being used in the entertainment industry to test the effectiveness of marketing campaigns and gauge audience response to different types of content . For example, movie studios have used EEG to measure viewers’ emotional responses to movie trailers.

Other industries

Neuromarketing is also being used in a range of other industries, including finance, food and beverage, and automotive. For example, some financial institutions have used Neuromarketing to study investors’ responses to different types of investment advice, while some food and beverage companies have used EEG to measure consumers’ responses to different types of packaging.

Overall, Neuromarketing is proving to be a valuable tool for businesses in a wide range of industries, helping them better understand their customers and create more effective marketing campaigns. By leveraging the insights gained from Neuromarketing, businesses can gain a competitive edge and drive growth and revenue.

Real-world Examples of Neuromarketing

Neuromarketing has been used by a number of large companies to improve their marketing strategies and boost sales. Here are two real-world examples of Neuromarketing in action:

In 2012, Coca-Cola used Neuromarketing techniques to create a successful ad campaign. The company used EEG and eye-tracking technology to measure viewers’ emotional and cognitive responses to different ad concepts. Based on the insights gained from this research, Coca-Cola created an ad that featured smiling, happy people drinking Coca-Cola in a range of social settings. The ad was a success, helping to boost sales and improve the brand’s image.

In 2014, Nestle used Neuromarketing to redesign the packaging for its KitKat chocolate bars. The company used EEG to measure consumers’ emotional responses to different packaging designs, testing factors such as color, shape, and logo placement. Based on the insights gained from this research, Nestle redesigned the packaging to feature a larger, more prominent logo and a more vibrant color scheme. The new packaging design was a success, helping to boost sales and improve the brand’s visibility on store shelves.

Neuromarketing is like a secret weapon that businesses can use to create killer marketing campaigns and rake in the dough. By tapping into consumers’ emotional and cognitive responses to different stimuli, companies can create highly-targeted and irresistible marketing messages that connect with their audience on a deeper level. It’s like mind-reading, but for marketing purposes!

Popular Case Studies for Neuromarketing

There are several popular case studies that illustrate the power of Neuromarketing in action. Here are two examples:

In 2011, snack food giant Frito-Lay conducted a study using EEG to measure consumers’ emotional responses to different product packaging designs. The research found that people were more likely to buy chips with simple, bold designs rather than those with cluttered or complicated packaging. Based on this insight, Frito-Lay redesigned the packaging for their Sun Chips brand to feature a simpler, more eye-catching design. The new packaging design was a hit, helping to increase sales and improve the brand’s visibility on store shelves.

In 2012, soft drink giant PepsiCo conducted a study using fMRI to measure consumers’ brain activity in response to different soft drink flavors. The research found that people had stronger positive emotional responses to flavors like cherry and vanilla compared to other flavors. Based on this insight, PepsiCo introduced new flavors like Cherry Vanilla and Diet Cherry Vanilla to their product line, which proved to be a hit with consumers.

These case studies demonstrate how Neuromarketing can provide valuable insights into consumer behavior and preferences, helping businesses to create more effective marketing strategies and drive growth and revenue. By using advanced neuroscience techniques to measure consumers’ emotional and cognitive responses to different stimuli, companies can gain a deeper understanding of what motivates people to buy, and use that insight to craft marketing messages that really resonate.

These case studies prove that by using cutting-edge neuroscience techniques, companies can unlock the secrets of consumer behavior and preferences, and use that knowledge to create marketing strategies that really pack a punch.

In summary, Neuromarketing is a field that combines psychology, neuroscience, and marketing to gain a deeper understanding of consumer behavior and create more effective marketing strategies. By measuring consumers’ emotional and cognitive responses to different stimuli, businesses can craft messages that resonate with their audience and drive growth and revenue.

Despite some limitations and ethical concerns, Neuromarketing has become an important tool for businesses in today’s digital age, helping them stay ahead of the competition and connect with consumers in more meaningful ways.

Ali is a digital marketing blogger and author who uses the power of words to inspire and impact others. He has written for leading publications like Business2Community, Inc. Magazine, and Marketing Profs. When not writing, he enjoys spending time with his family.

How Neuromarketing Can Revolutionize the Marketing Industry [+Examples]

Published: October 07, 2022

Traditional metrics (like clicks, shares, and scroll times) can tell you a lot about campaign performance, but they can’t measure how customers feel about your brand. That’s where neuromarketing comes in. As a supplement to more standard marketing performance metrics, neuromarketing can help you analyze the emotional response to your campaigns.

Neuromarketing tells us what colors, pictures, music, or messages resonate the most with audiences. Your team can use this data to identify customers’ ad preferences.

Take a deep dive into how neuromarketing works for popular brands.

What is neuromarketing?

Neuromarketing blends neuroscience and marketing to help brands gauge the emotional resonance of their current and future campaigns. To do this, teams use technology that tracks customers’ neurochemical and physiological responses while consuming marketing content. Marketers can then test for ads that signal the most emotional engagement.

Let’s turn to P&G for a real-world example of neuromarketing at work.

In partnership with marketing firm Dentsu Data Labs, P&G designed an experiment to find mobile ads that emotionally resonated with their audience. During testing, the company worked with Sticky, a webcam eye-tracking tool by Tobii Pro , to measure engagement of on-the-go users.

What they found was intriguing — the time spent watching video ads on social media was not equal to the time they spent focusing on the ads. Social platforms’ impressions and watch rates did not correlate with real customers’ engagement.

Moreover, Sticky detected which video ad details triggered a desire to interact with the brand. Eye tracking data became actionable insights, highlighting content P&G should change to retain audience attention.

The big takeaway: Knowing what the brain actually resonates with is more important than knowing what people say they like or how much time they spend watching ads.

To grab your customer’s attention, make them feel something and compel them to act. Marketers need to focus more on neuroscience and less on web metrics and in-person interviews.

Neuromarketing Research

Neuromarketing research commonly uses either brain-scanning technology or physiological measurements to assess consumers’ subconscious preferences. This can help inform advertising, product development, or marketing materials.

Neuromarketing is typically done through brain scanning — either with fMRI or EEG technology — or physiological tracking, including eye movement measurements, facial coding, or body temperature and heart rate measurements.

fMRI and EEG technology have different strengths.

“Normally we use EEG to measure dynamic stimuli, like video, TV shows, commercials, online user experience. In such cases, it is interesting to see the brain responding moment-to-moment,” Dr. Roeland Dietvorst , Lead Behavioral Scientist at NN Investment Partners, told the Neuromarketing Science and Business Association. “We use fMRI mainly for static stimuli, like packaging design, campaign slogans, pay-offs, outdoor messaging.”

Measuring physiological tracking is typically much easier to do. Many tools are available in the marketplace, including FaceReader by Noldus , which measures facial expressions, or the eye-tracking software mentioned above.

However, even though leveraging neuroscience to inform your marketing strategy is an exciting opportunity, the tactic still seems more suited for a time when Black Mirror storylines are a reality.

In fact, people often ask, “Is neuromarketing even ethical?”

Below, let’s dive into that question.

Neuromarketing Ethics

While neuromarketing aims to determine how consumers respond to brands or campaigns – a rather innocuous study – not everyone is convinced that it’s ethical.

The book “Towards Ethical Neuromarketing 2.0 Based on Artificial Intelligence” addresses ethical issues such as, “Will algorithms predict future behavior?” and “Is neuromarketing immoral?”

In and of itself, neuromarketing isn’t unethical. However, companies must hold themselves to a high standard of ethics when studying their consumers.

For instance, brands shouldn’t intentionally promote anything harmful, deceptive, or illegal. Additionally, you shouldn’t study minors to figure out how to get them hooked on a product.

Neuromarketing should be used to create effective ads and eliminate ads that just don’t work, and that’s all.

The main ethical questioning has more to do with your product or service and less with how you market it. If you’re ever in doubt, ask yourself if the product or service is good for the customer.

In actuality, neuromarketing has already permeated the content space.

Advertising agency BBDO collaborates with Immersion to use smartwatch biometrics — including heart rate — to predict the success of their ads. One of Immersion’s studies correctly identified which BBDO’s ad would produce the largest sales bumps with an impressive 83% accuracy .

To help you envision a world where neuromarketing is widespread, here are eight practical ways you can refine your marketing efforts with the help of neuroscience

Neuromarketing Examples

• Brands can tell more compelling stories.
• Businesses can focus on ads that boost sales.
• Companies can host more engaging conferences.
• Brands can design more effective ads.
• Brands can sell more by using FOMO.
• Brands can ensure their packaging is effective.
• Businesses can determine the right price for a product or service.
• Brands can evaluate website performance.

1. Brands can tell more compelling stories.

In 2019, Renault released the newest version of their CLIO hatchbacks. To celebrate, the company released a commercial to highlight the car’s 30 years in development . The ad followed the love story of a lesbian couple that also took place over 30 years.

The world split into two camps. Haters were sure that the couple’s story had nothing to do with Renault as a brand. Other marketers praised the campaign for its boldness, originality, and the strong emotions evoked.

Neuromarketing settled the argument. The video ad reached very high likeability and brand recognition compared to other commercials, according to Alpha.One’s EEG and eye-tracking study .

“From 31 participants in our EEG and Eye-tracking study who viewed the commercial in a large reel of other commercials, 30 correctly identified the commercial as belonging to Renault,“ wrote Dietvorst on LinkedIn.

The audience’s emotional response peaked when the couple expressed happiness and affection. They developed compassion, becoming invested in love story’s ups and downs.

The audience’s emotional response to this ad suggests that telling great stories — chock-full of conflict, surprise, and emotion — triggers the release of oxytocin, the empathy chemical. You emotionally engage your audience and, ultimately, make them care about your brand.

Pro tip: When creating ad copy, develop stories about overcoming adversity and how that journey changes people to trigger an emotional response.

2. Businesses can focus on ads that boost sales.

Bolletje, a food company that makes healthy cereal, created two TV ads promoting the same product to the same audience. Yet, two campaigns brought drastically different results — one generating 250% higher sales.

Image Source

So what caused a 250% sales difference? A neuromarketing study using fMRI technology explains.

Eye-tracking and MRI technologies detected the specter of emotions the two ads aroused. As it turned out, the ad featuring aqua yoga elicited negative emotions. Viewers felt disgust, danger, and fear, which distracted them from the product.

Meanwhile, the ad featuring skinny jeans activated positive emotions like value, surprise, and expectations.

Pro tip: Before launching your next campaign, make sure it evokes positive emotions like sympathy, trust, value, or compassion. This prevents negative associations with your brand.

3. Companies can host more engaging conferences.

At a major global conference in Houston, Immersion Neuroscience put INBands on attendees and measured their immersion during certain presentations . They discovered that concise, energetic talks generated the most emotional engagement.

On the other hand, longer talks needed to revolve around a strong narrative, or they couldn’t hold an audience’s attention. Additionally, they realized the brain responds well to multimedia-heavy presentations due to the high variety of stimuli.

What we like: Tracking attendees’ emotional engagement during presentations can help companies refine their conferences by cutting out boring talks. Instead, provide attendees with relevant, compelling presentations.

4. Brands can design more effective ads.

The main goal of neuromarketing is to gain insight into what would make an ad more effective. That includes where ads are placed.

For instance, a recent neuroscience study revealed that positioning of display ads influences buying decisions regarding high- and low-calorie foods.

In a nutshell, researchers asked 57 participants to rate food images that appeared on the center, top, bottom, left, or right side of the screen.

Participants evaluated the desire to eat and buy, their liking, and willingness to pay for each image.

The study results uncovered that a banner for high-calorie food is more likely to draw attention and conversion if placed on the bottom right side. In contrast, ads for low-calorie food are most effective when placed on the top left side.

Pro tip: Leverage neuromarketing to find the ads that resonate most and where to place them.

5. Brands can sell more by using FOMO.

The fear of missing out, otherwise known as loss aversion, is a widely used tactic in marketing and sales.

In fact, 62% of consumers in a study from peer-reviewed publication Science were more likely to gamble their money than lose any money.

Here’s the scenario consumers were given.

If you were given $50, would you rather:

• Gamble it, with a 50/50 chance of keeping or losing the whole $50.

When the experimenter posed that question to the subjects, 43% of the subjects chose to gamble.

Then the options were changed to:

• Gamble, with a 50/50 chance of keeping or losing the whole $50.

With that slight change, there was a 44% jump in the number of people who gambled.

When more studies were done like this, 100% of subjects gambled more when the other choice was framed as a loss.

A 2021 study from University College London also revealed that urgent language leads to sales. Phrases such as “The #Sale is ON!” and “Only a few left in stock” on Facebook ads increased overall memory for advertisement information. In contrast, ads with no FOMO triggers performed worse.

The neuromarketing takeaway is that framing will greatly impact people’s behavior. And people are loss averse.

Pro tip: You can implement this method by changing the language of your ads. If you can pose the outcome of not buying your product or service as a loss, then you can sell more.

6. Brands can ensure their packaging is effective.

Brands might consider using neuromarketing to measure viewers’ emotional reactions to different packaging designs and determine which packaging option evokes the highest level of position emotion and engagement.

Let’s see how Alpro, a Belgium company that markets plant-based milk products, applied neuromarketing to build barista-preferred packaging . Working with neuroscience company Alpha.One, Alpro leveraged eye-tracking to measure engagement.

What we like: Small changes in color and more straightforward communication through images can evoke a better reaction from the target audience and result in a sales boost.

7. Businesses can determine the right price for a product or service.

Pricing is all about psychology.

For instance, University of Florida marketing professors Chris Janiszewski and Dan Uy wanted to evaluate whether consumers will truly evaluate a product as more fairly priced if it’s $19.95 rather than an even $20. They conducted a range of experiments and found people “ create mental measuring sticks that run in increments away from any opening bid, and the size of the increments depends on the opening bid.”

Or, put another way: If you see a product priced at $19.95, you might wish it was $19.75 or $19.50, but you’ll be thinking in terms of nickels and dimes. However, if you see a product priced to the nearest full dollar — such as an even $20 — you instead might wish it was priced at $19 or $18, moving the range further away from the actual price.

Pro tip: Rely on neuromarketing to evaluate consumers’ subconscious reactions and determine the right pricing. Just asking a focus group if your product is priced fairly, can lead to groupthink and obscure the truth. (Check out the Lays pricing study below for more.)

8. Brands can evaluate website performance.

That’s exactly what Taskworld did to boost its site conversion rate by 40% .

To figure out if the site was effective, Shiv Sharma, Marketing Consultant at Taskworld, used heatmaps to see where new visitors clicked when signing up. What fields are they struggling to fill out? What question in the sign-up form causes leads to drop off?

Thanks to heatmaps, Sharma discovered crucial glitches in the sign-up form that took only five minutes to fix. Those minor changes increased their website conversion by 40%.

Companies that Use Neuromarketing

Some world-known brands tested out neuromarketing years ago, ranging as far back as 2009. However, we’ve compiled a list of new neuromarketing case studies so you can gain insights and learn from each of these examples.

1. Frito-Lay

Frito-Lay worked with Neurensics, a neuro market research company, to understand the impact of a price increase of 0.25 Turkish Lira on Lays chips in Turkey. The prime question: Would a price change lead to a decrease in revenue?

To find out, Neurensics used both an EEG to study brain responses to the updated price and a standard questionnaire. The results showcased that what people say can strikingly differ from what people actually think, proving that buying decisions are often made unconsciously.

First, participants answered questions about the likelihood that they would buy a bag of chips after the price increase. Second, the same group answered the “expensive” or “cheap” questions about the same Lays products while an EEG device measured brain activity.

The difference in results of the two methods was staggering. According to the traditional questionnaire, Lays should have lost 33% in revenue. The EEG results showed only a 9% drop in sales.

Once applied, parent company PepsiCo experienced only a 7% loss in revenue from the price change.

Pro tip: Asking people for their opinion on prices, packaging, or ads can lead to incorrect predictions. Instead, you can rely on neuroscience and unconscious behavior to measure changes.

Philips wanted to select packaging for an ultra-light iron that appealed most to buyers and increased purchases. They designed two visuals with left and right hands holding the iron.

With Neurensics, Philips tested out both visuals to determine which one caused a positive emotional response.

The fMRI study showed that participants found the left-handed packaging disgusting and dangerous. The familiar, right-handed image activated attention, trust, and the same level of expectations. But why?

The Neurensics team explains this phenomenon as a mental simulation: “An unconscious process where the brain simulates using the product or experiencing a situation.”

An iron held with the left hand is a more difficult mental simulation to conjure when 90% of the population is right-handed. This leads to feelings of disgust.

With this new information, Philips pivoted to the packaging with the right hand holding the iron.

3. Steereo and Spotify

Can you use neuroscience to predict the next record-breaking song? Steereo, a platform that plays new music exclusively for rideshare drivers, posed this question to Immersion.

Immersion tracked subtle changes in listeners’ heartbeats to gauge emotional responses to music. The study accurately predicted hit songs with 92% accuracy.

They also estimated the numbers of super fans and followers for those songs on Spotify with 67% accuracy.

Compare this to traditional survey analysis of songs’ likeability, which resulted in no correlation to real hits.

Use Neuromarketing in Your Business

We live in an age of data overload where you can measure almost anything. But Google Analytics will never be able to accurately gauge the most important element of your marketing campaign — its ability to make your audience feel something.

Fortunately, the neuromarketing space is rapidly evolving, and this technology is becoming more affordable and practical for marketers today, leading to its mainstream use tomorrow.

Editor’s note: This post was originally published in January 2019 and has been updated for comprehensiveness.

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Home » Blog » Neuromarketing in Action: Real-World Case Studies

Neuromarketing in Action: Real-World Case Studies

Neuromarketing , the science of understanding consumer behavior at a neurological level, has transitioned from theory to impactful practice. In this dedicated article, we explore real-world case studies that vividly illustrate how businesses, large and small, have harnessed the power of neuromarketing to craft compelling digital campaigns. These stories offer invaluable insights into how neuromarketing principles have been applied across various industries, showcasing its transformative potential in the realm of digital marketing .

Coca-Cola’s “Share a Coke” Campaign:

• Coca-Cola’ s iconic “Share a Coke” campaign was a game-changer in the marketing world. It involved replacing the Coca-Cola logo on its bottles with popular first names and phrases. This simple yet brilliant idea tapped into the fundamental human need for recognition and personal connection.
• By personalizing their product at scale, Coca-Cola made consumers feel more involved and emotionally connected to the brand. People were excited to find their names on a Coke bottle or to share one with a friend or family member.
• The campaign went viral on social media, with people sharing pictures and stories about their personalized Coke bottles. This not only boosted brand engagement but also demonstrated the power of personalized marketing in the digital age, where consumers seek unique and meaningful experiences.

Amazon’s Precision in Product Recommendations:

• Amazon ‘s recommendation engine is a prime example of neuromarketing in action. It leverages advanced algorithms and user behavior data to suggest products with remarkable accuracy.
• By analyzing past purchases, browsing history, and user preferences, Amazon can make highly personalized product recommendations. This not only enhances the shopping experience but also significantly increases the likelihood of additional purchases.
• The success of Amazon’s recommendation system showcases how neuromarketing principles, such as understanding and catering to individual preferences, can drive sales and customer satisfaction in e-commerce.

Airbnb’s Emotional Storytelling:

• Airbnb ‘s marketing strategy is centered around emotionally resonant storytelling. They aim to connect travelers with the idea of “belonging anywhere” by showcasing unique and heartwarming stories of hosts and guests.
• By appealing to the emotions, Airbnb fosters a sense of trust and community. This emotional connection drives user engagement and loyalty. Travelers feel like they’re not just booking accommodations but also experiencing meaningful connections and adventures.
• Airbnb’s approach highlights the power of tapping into emotions to create a strong brand identity and attract a loyal customer base.

The ALS Ice Bucket Challenge:

• The ALS Ice Bucket Challenge was a viral social media campaign that successfully harnessed the principles of social proof and peer influence.
• Participants challenged their friends and family to pour a bucket of ice water over their heads to raise awareness for ALS. This campaign quickly spread across social media platforms because it was fun, engaging, and allowed people to participate in a collective cause.
• The campaign’s success demonstrated how leveraging social dynamics, creating a sense of community, and making participation easy can drive widespread engagement and awareness for a cause.

Burger King’s Whopper Detour:

• Burger King ‘s Whopper Detour campaign showcased the effectiveness of location-based marketing and gamification.
• Using geofencing technology, Burger King targeted McDonald’s customers by offering them a one-cent Whopper if they placed their order through the Burger King app while inside or near a McDonald’s restaurant.
• This campaign not only engaged consumers playfully and competitively but also demonstrated the power of real-time, location-based marketing in driving foot traffic and increasing sales.

Nike’s Emotional Branding:

• Nike ‘s marketing often focuses on powerful emotional storytelling. Their campaigns emphasize values such as empowerment, resilience, and the pursuit of greatness.
• By aligning with these universal values, Nike has created a strong emotional connection with its audience. Consumers see Nike as a brand that champions athletes and individuals striving for success against all odds.
• Nike’s approach showcases how emotional branding can create a lasting and loyal customer base, as consumers feel a deep emotional resonance with the brand’s message and values.

These case studies illustrate the versatility and effectiveness of neuromarketing techniques in various industries and contexts. They showcase how businesses can connect with consumers on a deeper, more emotional level, ultimately leading to increased engagement, higher conversion rates, and stronger customer loyalty. Neuromarketing in action is a testament to the transformative potential of this science in the digital landscape.

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A comparative analysis of neuromarketing methods for brand purchasing predictions among young adults

• Original Article
• Published: 12 January 2021
• Volume 28 , pages 171–185, ( 2021 )

Cite this article

• Urszula Garczarek-Bąk 1 ,
• Andrzej Szymkowiak   ORCID: orcid.org/0000-0001-5673-7093 1 ,
• Piotr Gaczek 2 &
• Aneta Disterheft 3  

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Until now, neuromarketing studies have usually been aimed at assessing the predictive value of psychophysiological measures gathered while watching a marketing message related to a particular product. This study is the first attempt to verify the possibility of predicting familiar and unfamiliar brand purchases based on psychophysiological reactions to a retailer television advertisement measured by EEG, EDA and eye-tracking. The number of private label products chosen later served to assess the binary dependent variable. A logistic regression model (with a prediction rate of 61.2%) was applied to determine which psychophysiological variables explained the largest part of the variance of a final purchase decision. The results show that among various measures, only the electrodermal peaks per second were significant in predicting further purchase decisions. The decision to buy was also influenced by brand familiarity. The article concludes that EDA is an unobtrusive measure of emotion-related anticipation of significant outcomes, particularly for dynamic stimuli, as related to decision-making.

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Introduction

Predicting consumer purchase behaviour is still one of the biggest challenges faced by marketers around the world, which has become especially more difficult as consumers are constantly being exposed to new technologies, products and wants. There is growing empirical evidence regarding the usefulness of applying neuro and psychophysiological measures into predictive models of purchase decisions (Yoon et al. 2012 ). According to Telpaz et al. ( 2015 ), neuroscience reveals information about consumer preference that is unobtainable through conventional methods, and neural activity can predict preferences of consumer products. Using neurophysiological measures (recording metabolic or electrical activity in the brain) without recording brain activities to probe consumer minds directly, without the attrition of conscious participation, enables discovering the consumers’ real emotions, feelings, expectations and even hidden restraints (Lin et al. 2018 ). Ariely and Berns ( 2010 ) underlined the prominent hope that neuroimaging will both streamline marketing processes and save money, because it will provide a more efficient trade-off between costs and benefits. The cost of performing neuroimaging studies would be outweighed by the benefit of improved product design and increased sales obtained through brain imaging, which could highlight not only what people like, but also what they will buy. On the other hand, the use of advanced neuromarketing techniques (e.g. fMRI) is relatively expensive, time-consuming and involves larger teams of researchers. Therefore, the question arises whether the added value of using them (i.e. more precise prediction of buyers’ decisions) is worth the higher expenditure.

In this paper, we aim to compare, inform and triangulate results from available neuromarketing-related measurements, such as electroencephalography (EEG), electrodermal activity (EDA) and eye-tracking (ET) data in order to see which could serve as the best predictor of a product choice. Moreover, we test whether these techniques are better predictors of purchase compared to traditional self-reports. Thus, we address the issue of marketing value of neuro-based techniques in product selection and provide managerial implications on how brand managements should approach consumer research to maximize effectiveness and predict behaviour with the highest accuracy. Our conclusions will be valuable for marketers looking for a guide on the most effective composition of measurement instruments in marketing research.

Our text distinguishes itself from others mainly because it links psychophysiological reactions to ads with brand choice (familiar vs. unfamiliar), while the majority of studies are focused on emotional and cognitive responses, and product but not brand choice (Harris et al. 2018 ). We contribute to the existing advanced knowledge by showing which measurements are effective in brand choice prediction influenced by video advertisements.

Literature review

Electroencephalography in predicting purchase decisions.

Measuring electrophysiological responses enables the gathering of immediate feedback to presented stimuli in the form of fluctuations of brain signal frequencies (Brown et al. 2012 ). Using a different methodological approach to investigate positive versus negative emotional experiences, neuromarketing studies are based on the idea that there is a left–right asymmetry of the frontal EEG signals. Greater relative left frontal EEG activity is routinely associated with the processing of positive effects (an indication of happiness or amusement), whereas greater relative right frontal EEG activity is consistently linked with the processing of negative effects (e.g. indicating disgust) (Davidson et al. 1990 ).

Empirical evidence provides strong support for the predictive value of EEG measurement in product preference formation. This is due to the possibility of capturing approach-withdrawal motivation, which reflects consumer desirability of a product (Ohme et al. 2010 ). Product preference is reflected in brain activity observed at the moment of exposition to an advertisement (Wei et al. 2018 ) and to the product itself (Telpaz et al. 2015 ; Khushaba et al. 2013 ). Moreover, the use of EEG creates an opportunity to predict the attractiveness of marketing communication, e.g. advertising, and thus allows to formulate conclusions about its effectiveness (Gauba et al. 2017 ; Maison and Oleksy 2017 ; Ohme et al. 2010 ). Researchers claim that EEG can provide inform about one’s interest in a commercial (Piwowarski 2018 ) as well as the emotional experience while watching it (Vecchiato et al. 2014 ; Ambler 2015 ).

EEG efficiency is also demonstrated in brand selection research (see Table  1 ). Similarly to product selection, neural activity reflects a consumer’s attitude towards a brand and translates into subsequent purchase intention. In the available literature, it is shown that brain activity can reveal the subjective emotional value that a brand has for a decision-maker (Pozharliev et al. 2019 ; Ohme et al. 2010 ). It may also serve as an indicator for a shift in brand preference caused by a TV advertisement (Silberstein and Nield 2015 ). Importantly, brand processing is associated with frontal region activity (Lucchiari and Pravettoni 2012 ); thus, based on EEG measurement marketers can conclude if the brand (e.g. exposed in a commercial) attracts consumer’s attention (Wang et al. 2016 ).

Electrodermal activity in predicting purchase decisions

EDA, referring to the variation of the skin’s electrical properties in response to sweat secretion, may be an appropriate method for engaging stimuli, similar to movie trailers (Benedek and Kaernbach 2010 ). According to Bradley and Lang ( 2000 ), EDA is a great physiological correlate for representing emotional arousal using a variety of different ways to elicit emotion (skin conductance responses occurring in a predefined response window, typically 1–3 s or 1–5 s after the stimulus, are attributed to the stimulus) (Dawson et al. 2007 ). Ravaja ( 2004 ) explained that changes in skin conductance (SC) indicate autonomic nervous system activation: the higher the SC levels, the higher physiological arousal. Boucsein ( 2012 ) reported that humans have lower EDA with positive emotions while negative emotions are associated with higher EDA. Furthermore, EDA can reveal unnoticeable emotions in browsing experiences.

As emotional arousal is a significant indicator of product and brand preference (Reimann et al. 2012 ; Gaczek 2015 ), EDA measurement makes it possible to predict whether a product or brand will meet buyers’ interest (see Table  1 ). Interestingly, EDA research reveals that brands may be as arousing as close friends and elicit more positive valence than interpersonal relationships (Langner et al. 2015 ). On the other hand, research is inconclusive on the specific influence that known or loved (vs. unknown or disliked) brands have on emotional arousal. For instance, Walla et al. ( 2011 ) demonstrated that arousal was significantly reduced in the case of viewing liked brand names compared to viewing those disliked (suggesting that visual perception of liked brand names elicited a more relaxing state compared to disliked brand names). A similar finding is reported by Reimann et al. ( 2012 ) who claimed that the difference in arousal is due to the recently formed brand relationship versus already established relationships.

To the contrary, Smith et al. ( 2019 ) demonstrated children are more aroused when presented with their favourite branded products compared to the same products but without branding. In turn, Gangadharbatla et al. ( 2013 ) claimed no interaction between emotional arousal elicited by brands or further recall of them. In a comparative study on the predictive value of neuromarketing tools, Garczarek-Bąk and Disterheft ( 2018 ) suggested that the use of EDA does not translate into a better prediction of brand choice.

Eye tracking in predicting purchase decisions

Another psychophysiological measure providing an unobtrusive means of pairing the EDA data or EEG data with the applied stimuli is eye-tracking (ET). Through ET, it is possible to visually document a consumer’s journey during the whole study. Within the context of visual choice and comparison tasks, monitoring the distribution and duration of eye fixations may provide an excellent measure of an observer’s interests and preferences (Glaholt et al. 2009 ). Precisely, these authors found evidence that the amount of time the eye spends on a chosen stimulus is positively related to the likelihood of that stimulus being selected and preferred.

Moreover, it is also documented that visual attention towards brand-related elements (e.g. labels) corresponds to general attitude towards the brand (Graham and Jeffery ( 2012 ). According to Venkatraman et al. ( 2015 ), ET, used as a direct measure of attention, is probably the most accessible method for catching ad response, which enables capturing not only which information was processed, but also the order and duration of these processes. Oliveira and Giraldi ( 2019 ) come to similar conclusions by demonstrating that visual attention captured with ET is different for weak and strong brands. More specifically, the percentage of valid fixations provides an index of overall attention or engagement with the ad, while the number of fixations and mean dwell times provide a measure of the depth to which information within an ad is processed.

Brand familiarity in marketing and neuroscientific research

Consumer attitudes towards a brand play a major contributing role regarding purchase decision (Bosshard et al. 2016 ). Familiar brands receive more favourable evaluations and consumers are more likely to consider them for future purchases (Sundaram 1999 ). Hoeffler and Keller ( 2003 ) noted that strong (familiar) brands have been suggested to have stronger and more positive brand associations compared to unfamiliar brands, and due to the greater number of associations in a wide variety of contexts, they are more likely to be in consumers’ consideration sets. As explained by Esch et al. ( 2012 ), strong positive associations should further contribute to positive feelings (in terms of recalling past consumption experiences), which may trigger strong sensations such as brand attachment, trust and excitement. Lim and Chung ( 2014 ) explained that for familiar brands, consumers are likely to be highly certain of their evaluation due to the fact that they may have had prior experience using the brands, seen the advertising or marketing communications, received information from the news media, or experienced word-of-mouth from friends and family. As noticed by Janiszewski et al. ( 2013 ), consumers are more likely to purchase a product if they have previously focused their attention on it. The act of attending to a product available and well known on a certain market may increase the likelihood that the product be chosen in the future.

Interestingly, consumers with prior brand familiarity are more likely to attenuate the influence of attitude towards the specific ad on attitude towards the brand (Campbell and Keller 2003 ). Therefore, the ad’s attitude effect on brand evaluations should be greater when the ad is for an unfamiliar brand, and consumers process an unfamiliar brand ad more extensively (Machleit et al. 1993 ). According to Campbell and Keller ( 2003 ), ads for unfamiliar brands may seem less boring; however, they can wear out more quickly than for ads more familiar. Additionally, attitudes engendered by an ad are less likely to influence attitudes towards familiar brands.

According to Reimann et al. ( 2012 ), insight into the psychological processes underlying the choice of novel versus familiar brands can enable a better understanding of consumer choice and evaluation processes. However, brain imaging has been used to a significantly lesser extent in determining the neurophysiological keystone of novel brands. Accessing psychological processes towards them is difficult because consumers have typically not yet formed any associations or attitudes while lacking certainty about their evaluation of unfamiliar brands. As mentioned by Ravaja et al. ( 2013 ), brand associations are formed when interacting with the brand (e.g., trips to shops and actual consumption), and during prior indirect brand exposures (e.g., via brand communications). In the EEG research conducted by de Azevedo ( 2010 ), only the familiar brands elicited activations in the frontal cortex during the first second following logo presentation (meaning that the frontal cortex was exclusively activated for familiar brands, revealing the importance of brands as meaningful symbols that convey a specific message). Moreover, the mean potential measured in the LPP interval revealed stronger activations in the centroparietal regions for the most preferred brands when compared to the unknown brands, suggesting that they were given more attention and were more self-relevant.

On the contrary, the unknown brands elicited more activation in the occipital cortex and frontocentral region. Surprisingly, the results of the asymmetries showed a pronounced right-hemisphere Alfa band power ( p value of < .05) for every group of response rating (meaning stronger left brain activation). Further attempts using the activity in the frontal cortex and its asymmetry in predicting the choice of purchasing a product proved that 95% of the respondents chose well-known brands and found a dominance trend of the left hemisphere over the right in the oral statements of the tested participants (Olarte 2017 ). In the case of electrodermal research, Walla et al. ( 2011 ) revealed that skin conductance data demonstrate a clear sensitivity related to subjective brand preference, while skin conductance was markedly reduced in the case of visual perception of liked brand names (versus disliked brand names), which may suggest that visual perception of liked brand names elicited a more relaxing state (Plassmann et al. 2015 ). These results may suggest that consumers use experienced emotions rather than declarative information to evaluate familiar and unfamiliar brands.

According to a quite rich body of empirical evidence implementing neurophysiology (as an additional tool, but not as a replacement method) leads to significantly better accuracy and greater predictive power compared to self-reports alone. However, based on our knowledge of prior works, there is still scarce research incorporating various neurophysiological methods with the aim of assessing their predictive values in terms of purchase decision-making. Thus, in this study, we aim to compare, inform and triangulate results from relatively inexpensive and available neuromarketing-related measurements, such as EEG, EDA, ET data and self-report measures in order to probe which can serve as the best predictor of a product choice. Simultaneous measurements of psychophysical reactions made by applying various devices were necessary to compensate for individual differences, which would be associated with the occurrence of type II errors.

Participants

The participants were twenty-four healthy, right-handed students and graduates of Poznań universities, among them 10 females, all born in Poland, within the age range of 21 to 34 years, at the average age of 26. Due to the breaks in the recorded signal, five individuals had incomplete data and were discarded from the analysis. The final dataset included 19 participants with valid and reliable data. Although there is no one sample size appropriate for all neuromarketing studies, and the sample size depends on multiple factors including research objectives and study design (Bojko and Adamczyk 2010 ), the reasonable minimum sample size in single consumer preference studies was 12 subjects in by Kostoulas et al. ( 2015 ) on EDA research, 15 subjects in the EEG research by Telpaz et al. ( 2015 ) and 16 subjects in ET research (Glaholt and Reingold 2011 ). In case of combined experiments, Khushaba et al. ( 2013 ) used a sample of 18 participants in EEG and ET research (Sheng and Joginapelly 2012 )—20 participants in EDA and ET research and Cartocci et al. ( 2017 )—22 participants in EEG and EDA research.

Individuals were invited to voluntarily participate in the experiment by subscribing to a list via the Internet. The main restrictions concerning participation and recommended preparation guidance (e.g., not to consume alcohol, caffeine or smoke cigarettes before the experiment, to avoid using hair styling products and sleep at least 8 h) were expressed over the telephone. A determinant criterion at recruitment was shopping on a weekly basis at supermarkets, which guaranteed that participants were aware of the existence of private label products. Respondents were paid a fixed fee of 100 PLN ($25) for their participation in the study.

Design and procedure

The research project was approved by the Ethical Committee at Poznań University of Economics and Business. The study was conducted in the Consumer Research Laboratory at PUEB and took approximately 30 min (including the EEG, ET and EDA equipment preparation and calibration).

According to the results obtained by Teixeira et al. ( 2014 ), familiar ads and brands (regarded as entertaining) are presumed to be more likely associated with higher reported purchase intent and choice. Therefore, research was structured in two blocks. The former included five chain-store ads from shops available in the participants’ country of residence, and the latter comprised chain stores operating in America (according to declarations—unfamiliar to participants). All stores were selected of the basis of the highest annual revenues in 2017. Chain-stores operating in America were chosen for this study as they do not operate in Poland; thus, they are unfamiliar to participants. We could not choose the top 5 stores operating in Europe as those chains are already present in Poland (e.g. Schwarz, Aldi, Carrefour, Tesco). Moreover, it was important to choose chain-stores that provided their marketing communication and product packages in English, as most young Polish adults are used to buying products with English labels.

In the first block (see Fig.  1 ), participants watched video commercials five familiar brands. After viewing each ad, on a grey screen, they were asked to rate their overall Ad Attitude (“How did you like the ad?”) on a 7-point Likert scale and Brand Attitude (non-appealing/appealing, dislikable/likable, unfavourable/favourable) on a 7-point semantic differential scale. After watching and rating five ads, they were asked to make 10 purchase choices with the click of a mouse. Every time they were shown a board with five private label products of the same FMCG category, from a different, previously advertised, chain store. The second block had the same command, but based on five unfamiliar retailers’ ads and making 10 purchase decisions among the American private label products. Dependent and independent variables are presented in Table  2 .

Experimental flow chart

In research, it is suggested that to a certain point the increment of positive elements in television advertisements can make them more attractive and persuasive, hence heightening purchase intentions and product choice, but an excessive load of entertainment can actually reduce the ad’s persuasiveness (Teixeira et al. 2014 ). Further, depending on whether the entertaining elements appear before or after the appearance of the brand, the product choice varies (in favour of ads that evoke positive emotions after a brand exposure). Having this in mind, real retailers’ TV advertisements with a moderate level of positive emotional charge and a brand that appears at the end were chosen for the study. They were also of comparable length (15–30 s) in order to reduce the possible impact of the ad length on product choice (there was a new variable created in order to account for different ad length).

Keeping in mind that involvement in a category is significantly related to product choice even once we endogenously control for viewing interest, the used stimuli include product choice for different product categories from FMCG (both food and hygienic products: corn flakes, jam, yogurt, juice, milk, dish soap, liquid soap, shower gel, tissues, toilet paper). All products within a certain category have comparable taste, smell, and similar packaging. Private label products were selected deliberately for the prevention of a new variable introduction.

For apparatus and data analysis description, see Appendix 1.

The Mann–Whitney U test revealed that there were no significant results between the declared Number of Chosen Products (NCP) regarding familiar and unfamiliar brands [ Z (165) = −.450, p  = .652]. Seven psychophysiological metrics from ET, EEG and EDA measurements, as well as two self-reported measures, ad and brand attitude, were tested for correlations with the total number of products chosen from a particular store. Alpha asymmetry for three bands and the number of electrodermal responses correlated with the amount of chosen goods (see Table  1 ).

Further analysis based on divided subsets, familiar and unfamiliar brands, revealed that the Number of Chosen Products was weakly correlated with EDA Peaks Per Second [ r s (69) = .252, p  = .018] and with Brand Attitude [ r s (69) = .279, p  = .010] only for familiar brands. Next, a nonparametric test was performed in order to check for differences in psychophysiological data for both brand types. Seven analysed variables from the Mann–Whitney test revealed significant differences across brands for Average Pupil Size [ t (165) = − 3.953, p  < .000], Number of Fixations [ t (165) = − 4.553, p  = .000], and EDA Peaks Per Time [ t (165) = − 3.995, p  < .000].

Interestingly, as the pupil size was larger for familiar brands, which may reflect the engagement of cognitive effort connected with bringing more brand association (while processing all of them, the cognitive load is greater). However, the higher number of fixation points measured for unfamiliar brands represents cognitive processes for unfamiliar advertisements (which may be interpreted as attracting respondents’ absolute attention to the ads or representing a misunderstanding to a certain degree). Higher EDA frequency peaks for unfamiliar brands, associated with emotional arousal to stimuli, may indicate stronger mental arousal while watching unknown commercials.

Finally, after rejecting the assumptions of linear regression, in order to search for potential nonlinear relationships between the number of chosen products and psychophysiological measures, backward elimination (conditional) logistic regression was performed. The binary Private Label Purchase (PLP) variable was computed by dichotomising the Number of Chosen Product score using the median split (more than 2, the median value was described as “a lot”). Variables included in the first step contained neurophysiological measures: two oculomotor characteristics, three frontal asymmetry indexes and two electrodermal activity related measures.

The final model consists only of EDA Peaks Per Seconds (in the zero step: p  = .047, score = 3.954) and Retailer Store Visitor ( p  = .272, score = 1.208). The Backward elimination approach in stepwise regression was as follows (parameters from step 0): EDA Amplitudes (.371, p  = .543), Number of Fixations (.139, p  = .710), Average Pupil Size (.564, p  = .453), Frontal Alpha Asymmetry (.191, p  = .662), Frontal Beta Asymmetry (.195, p  = .659), and Frontal Gamma Asymmetry (.238, p  = .626). Therefore, to predict the probability of private label purchases, the following model may be applied:

The log of the PLP odds was positively correlated with EDA Peaks Per Second ( p  = .017), meaning the higher the number of EDA Peaks Per Second, the more likely that a private label product will be bought. A score one point higher in the EDA peaks per second will increase the odds of a private label purchase by fivefold (that is, one more peak per second would result in the probability of a purchase being five times higher). Giving the same EPS score, familiar brands were more likely to be chosen than unfamiliar ones (they were coded as 1). In fact, the likelihood of choosing products of a familiar brand product was 2.44 times greater than for unfamiliar brands. The model evaluation is presented in Table  2 .

The overall model is significant ( p  = .043) with the Chi-square statistic of 7.548. The inferential goodness-of-fit test yielded a χ 2 (8) of 10.345 and was insignificant ( p  > .05). The overall correction of the prediction was 62.4%. With the cutoff set at .5, the prediction for respondents who did not intend to buy was more accurate than for those who did (86.9% and 15.6%, respectively). The accuracy of the diagnostic test was 61.2%, and it is shown as the area below the ROC curve presented in Fig.  2 .

Receiver operating characteristic curve for the brand purchase. Note dashed line—reference line. AUROC = .612. SE = .048. p  = .018

Due to the lack of predictive models for familiar and unfamiliar brands, by comparison, the model of predicting audience responses to movie content from EDA measurement by Silveira et al. ( 2013 ) provided 72% accuracy (offering a 31% improvement over predictions from demographics alone).

Following, in order to increase knowledge about brand familiarity importance, separate models for predicting private label purchases for familiar and unfamiliar brands were built using the same statistical tools and variables. In Table  3 , all variables included in the zero step of a logistic regression are presented for both familiar and unfamiliar brands (distribution was made based on declarations provided by respondents regarding the Retail Store Visitor variable). In the case of familiar brands, all variables related to EDA and ET were gradually eliminated from the regression model. In the event of unfamiliar ones, on the other hand, only one variable related to EDA (EDA amplitudes) was eliminated from the model (Tables  4 , 5 , 6 ).

Familiar brands model

For familiar brands, only the frontal asymmetry variables (for alpha, beta and gamma bands) remained in the fifth step of regression proposing the following model [however, at an insignificant level (.259; .258; .251, respectively)]:

Although the Hosmer–Lemeshow test results ( χ 2  = 4.045, 7 degrees of freedom, p  = .775) indicated that the goodness-of-fit is satisfactory, the Nagelkerke R 2 value was .087, suggesting that the model is not very useful in predicting private label purchases, similar to the area under the Receiver Operating Characteristic (AUROC) for these data, which gave a value of 58% (indicating that the discrimination of the model is poor), while the model, after removing components, offers the significance of changes for .014, .013, and .012, respectively, the contribution of the three explanatory variables in the prediction of familiar private label purchase is still poor. The classification table explains 66.7% of purchase decisions (with the cutoff set at .5, the prediction for participants who did not intend to buy was more accurate than for those who did, at 85.2% and 27.8%, respectively).

Unfamiliar brands model

Interestingly, for unfamiliar brands, as many as six variables (except EDA amplitudes) remained in the second step of the logistic regression, creating the following model:

This time, only the EEG measurements were statistically insignificant. Whereas eye-tracking variables were significant at the levels of .032 and .007, respectively, EDA Peaks Per Second (EPS) was significant at the .017 level and constant ( p  = .035). After removing components, the model indicating significance (with p  < .005). Due to the fact that there is more than one explanatory variable in the model, the interpretation of the odds ratio for one variable depends on the values of other variables being fixed. The interpretation of the odds ratio for chosen variables should be performed on the basis of the following example: the odds ratio of 4.999 indicates that, for given levels of all variables, for a score one point higher in average pupil size, the odds of private label purchase is five times as great. Generally, the overall model was significant at the level of .001, with the Chi-square statistic of 28.259, and R 2 Nagelkerke = .635, indicating that the model is useful in predicting private label purchases. Moreover, the Hosmer–Lemeshow test ( χ 2  = 3.905, 8 degrees of freedom, p  = .866) indicates that the numbers of chosen private labels are not significantly different from those predicted by the model and that the overall model fit is good. The overall correction of the prediction was 83.3%. With the cutoff set at .5, the prediction for participants who did not intend to buy was more accurate than for those who did (91.2% and 64.3%, respectively). The area below the ROC curve, quantifying the overall ability of the test to discriminate between an affirmative purchase decision and a negative one, revealed that the accuracy of the test was 65.8%. Affirmative purchase decisions as well as negative ones revealed a test accuracy of 65.8%.

Follow-up study

In order to more extensively test the obtained results, we conducted a simple replication of the experiment described above. Its aim was to test whether self-reports regarding emotional experience predict product choice to the same extent as psychophysiological measures. This issue is addressed due to the fact that self-reports may not be accurate enough to capture the subtle nature of emotional and cognitive influence on product and brand choice (Ariely and Berns 2010 ). The experimental procedure was similar to experiment 1, but as COVID-19 emerged, the study was conducted online. Participants ( N  = 28) were exposed to the same stimuli as in experiment 1; however, we used the Self-Assessment Manikin Scale (Bradley and Lang 2000 ) to assess emotional experience with regard to 3 dimensions: pleasure (whether the elicited emotion is positive or negative), arousal (how much the elicited emotion is “exciting”) and dominance (if one feels “in control” of the emotion). We conducted correlation analysis between self-reported emotions and product choices used in the first experiment. The results indicate that the correlation between self-reported emotional experience and chosen products is not statistically significant ( p  < .05). Nonetheless, we observed a positive correlation between ad attitude (and brand attitude) and pleasure as well as dominance, suggesting that declared pleasure associated with the ad and perceived control translate into more favourable judgment of an ad and brand. Those, however, do not correlate with the number of chosen products.

Based on our knowledge from prior work, the following research constitutes the first attempt (Nilashi et al. 2020 ) at incorporating various psychophysiological methods with the aim of assessing their predictive values in terms of purchase decision-making, interestingly, based on a brand advertisement rather than a particular product. The results of the present study comprise a further premise for the application of neuro and psychophysiological measures in predicting brand success (measured as the number of product purchases) while confirming its advantage over self-reports alone. Furthermore, this suggests that in the case of video advertisements of a general nature—focused on positive brand attitude formation, EDA may serve as an optimal solution in forecasting brand performance. What is interesting, unlike the study by Reimann et al. ( 2012 ), the different effects of EDA measurement on purchase decision are confirmed depending on brand familiarity, since the EDA Peaks Per Second was linked to the higher probability of purchasing familiar brands.

As the products of both brand types (familiar and unfamiliar) used in the study had comparable packaging appearance and the commercials were the same informational type and of similar content, the different patterns of decision-making for familiar and unfamiliar brands were thoroughly studied. The EDA peaks correlate with declared private label product purchase but only for the familiar stores, which may be interpreted not as an advertisement “like” but rather how much consumers like the advertised brand itself (which must be previously known to respondents), whereas EEG frontal asymmetries correlated with the number of declared chosen products did not reveal a statistically significant relevance in terms of predicting a purchase. It may be suggested that it is a proper, universal measurement, but one that is not really suitable in terms of predicting consumer intentions based on dynamic stimuli. Through the lack of significance in ET metrics regarding the general model, it may be understood that brands were equally camouflaged amongst the commercials. The recommendations of this research are not meant to disregard all future intersections with EEG, ET and retailers’ evaluation. Instead, more tactics must be used to understand the connection between the brain, the body’s nervous system, and finally, consumers’ visual attention and their self-report measures. According to Bosshard et al. ( 2016 ), it is crucial to use a multidimensional approach and apply as many measures as possible (and reasonable in terms of financial matters) to quantify the various aspects of brand attitude, as brands themselves are considered to be multidimensional concepts.

The follow-up study supports the notion that self-reports alone are not sufficient enough to predict purchase decisions (Ariely and Berns 2010 ). On the other hand, we observed that self-reported pleasure and control enhance the favourable evaluation of ads and brands. Hence, we state that emotional ratings can translate into a brand and advertisement attitude, but they cannot be an accurate predictor of actual brand choice. Specifically, we emphasize that the implementation of neuromarketing techniques is particularly useful in predicting actual behaviour, while self-reports are sufficient in predicting explicate attitudes towards brands (Lee et al. 2007 ; LaBarbera and Tucciarone 1995 ).

Conclusion and managerial implications

While understanding and predicting how customers really think, feel, and respond to offers of a certain company has always been complex and problematic (Hsu 2017 ). Nowadays, consumer neuroscience enables researchers to obtain a more objective understanding of consumers’ desires (Hubert 2010 ). Assessing the reaction of viewers to video content is important for a wide variety of applications, e.g. predicting the success of ad or brand campaigns (Silveira et al. 2013 ).

In this particular study, the number of chosen products of a given brand might be understood as an estimate of the future market share relative to competitive brands. Therefore, the investigation of EDA as a response to video ads may help assess market position and potential. Although the percentage of final purchases explained by the number of EDA responses is not very high, one may be satisfied with more general market performance estimates, namely whether the brand will perform better than the majority of other investigated brands.

The managerial implications resulting from the research relate to the effectiveness of neuromarketing research and the content of brand marketing communication. Firstly, we offer supportive evidence that that market researchers can potentially use EDA responses as a valid tool to measure video advertisement effectiveness in predicting consumers’ private label product choices. This implication also relates to a broader construct of emotional (but possibly to sexual as well) arousal that can be a better predictor of consumer behaviour than emotional valence (Coker 2020 ; Szymkowiak et al. 2020 ). Thus, marketers should not limit consumer research to assessing the positive versus negative dimensions of brand communication, but should also measure consumer excitement or arousal. On the other hand, more sophisticated and expensive neuro-techniques (e.g. EEG) should be chosen carefully, as their predictive value is scarce.

Secondly, as we observed emotional arousal is a significant predictor of product choice, marketers should design marketing communication to increase emotional stimulation associated with the brand. For example, ads evoking higher emotional arousal are better memorized (Bakalash and Reimer 2013 ) and evaluated more positively (Gorn et al. 2001 ). On the other hand, practitioners should carefully consider the context on which they display commercials. Newell et al. ( 2001 ) observed that programmes evoking strong emotional reactions (e.g. the Super Bowl) may inhibit the recall of ads or brand.

Further research would be recommended in order to determine further relationships between psychophysiological patterns and people’s reactions to persuasive stimuli. The investigation of the relation between facial expressions and measures of advertisement effectiveness would be interesting in terms of predicting consumers’ purchasing intentions concerning different types of products, although preferably not from the low-engaging category.

Limitations

The current study has some limitations that deserve to be addressed in further research. In this study, we look at the effects of commercials in the short term, of exposure to ten ads, when consumers form their preferences closely after watching ads (similarly as in the case of purchase intent measurement conducted by Teixeira et al. ( 2014 ). Although the chosen advertisements were similar in presented content and type of message (informational—not highly entertaining), there is still a risk that viewers may pay less attention to a message that is associated with a previously known brand, or in the absence of knowing the advertised retailer, they may associate the positive entertainment felt to the ad as opposed to transferring it to the advertised brand. Furthermore, TV commercials are dynamic stimuli including multiple events that can trigger different emotional reactions in a random time window (Lajante et al. 2012 ). Moreover, Reimann et al. ( 2012 ) pointed out that electrodermal activity data depend on length and closeness of the consumer’s relationship with a brand and result in greater arousal at the beginning of a strong relationship and abate over time. However, in this case, the assumption that EDA may be more useful when studying new brands on the market was rejected because greater arousal was recorded while watching familiar commercials. The next issue is the fact that participants made actual choices during the EEG, ET, and EDA recordings. Therefore, according to Telpaz et al. ( 2015 ), it is still an open question whether EEG data during passive viewing of stimulus may be used in order to predict choices over some substantial time horizon. Finally, potential criticism may concern the applied equipment. With technological developments, modern apparatus may provide higher sampling frequency that allows for gathering more accurate results and presenting less inconvenience for the participants. However, it still does not reduce the participants’ awareness of being observed, regardless of applying only single or a few neuromarketing research devices.

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Appendix: Apparatus and data analysis

The registration phase was conducted in a neutral room in which the temperature (24 °C) and the brightness (artificial lighting) were kept constant. The stimuli were shown on a 25-inch monitor using OpenSesame, and SMI eye-tracking glasses with the 60 Hz sampling frequency were used to record eye movements. All signals were recorded and amplified using an 8-channel bipolar system (provided by g.tec) with a 256 Hz sampling frequency. EEG electrodes were placed according to the 10–20 International Electrode Placement System on sites F3 and F4, with the ground electrode on Fz. The EDA signal was recorded from the forefinger and ring finger of the nondominant hand (left hand for all participants).

A MATLAB environment was used for signal preprocessing, artifact rejection, epoching, and further analysis. The EEG signal from the frontal lobe (F3 and F4) served to calculate the frontal asymmetry index for alpha (8–12 Hz), beta (13–25 Hz), and gamma (30–80 Hz) bands. Frontal brain asymmetry index in three bands (FAA, FBA, FGA) and two electrodermal responses (EPS—peaks per second and EAM—amplitude) were computed and analysed for every video ad and participant. Signal processing included notch filter (50 Hz) application, bandpass filtering (.01–1 Hz for EDA, 13–25 Hz for EEG), and smoothing. The threshold for EDR was computed individually based on the signal mean and standard deviation. Frontal asymmetries were computed using the following equation: example for alpha band: FAA = log (FPL − FPR/FPL + FPR), where: FPL—frequency power from left hemisphere; FPR—frequency power from right hemisphere. To reduce between-subjects differences in response magnitude, standardization of the recorded EDA data was necessary before performing most statistical tests. Due to the different time-dynamic stimuli, in order to compare the obtained results, there was a need to normalize the data and the new variable was computed by correlating the number of chosen products with the number of EDA peaks per second.

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Garczarek-Bąk, U., Szymkowiak, A., Gaczek, P. et al. A comparative analysis of neuromarketing methods for brand purchasing predictions among young adults. J Brand Manag 28 , 171–185 (2021). https://doi.org/10.1057/s41262-020-00221-7

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NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour

• Kostas Georgiadis   ORCID: orcid.org/0000-0001-9116-4729 1   na1 ,
• Fotis P. Kalaganis 1   na1 ,
• Kyriakos Riskos   ORCID: orcid.org/0000-0003-3726-8003 2 , 3 ,
• Eleftheria Matta 4 ,
• Vangelis P. Oikonomou 1 ,
• Ioanna Yfantidou 2 ,
• Dimitris Chantziaras 5 ,
• Kyriakos Pantouvakis 4 ,
• Spiros Nikolopoulos 1 ,
• Nikos A. Laskaris 6 &
• Ioannis Kompatsiaris 1  

Scientific Data volume  10 , Article number:  508 ( 2023 ) Cite this article

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Neuromarketing is a continuously evolving field that utilises neuroimaging technologies to explore consumers’ behavioural responses to specific marketing-related stimulation, and furthermore introduces novel marketing tools that could complement the traditional ones like questionnaires. In this context, the present paper introduces a multimodal Neuromarketing dataset that encompasses the data from 42 individuals who participated in an advertising brochure-browsing scenario. In more detail, participants were exposed to a series of supermarket brochures (containing various products) and instructed to select the products they intended to buy. The data collected for each individual executing this protocol included: (i) encephalographic (EEG) recordings, (ii) eye tracking (ET) recordings, (iii) questionnaire responses (demographic, profiling and product related questions), and (iv) computer mouse data. NeuMa dataset has both dynamic and multimodal nature and, due to the narrow availability of open relevant datasets, provides new and unique opportunities for researchers in the field to attempt a more holistic approach to neuromarketing.

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Background & summary.

Neuromarketing 1 refers to the emerging field that lies at the intersection of consumer behaviour studies and neuroscience. Using a more strict definition, neuromarketing refers to the “application of neuroscience in the marketing field” and plays an instrumental role in understanding how consumers’ decisions are driven by the subconscious mind. Despite the initial scepticism 2 , neuromarketing has met a rapid growth in the last years, as neuroimaging technology fosters the investigation of cognitive activity that may not be consciously perceived by the consumers 3 .

Recent innovations in long-standing science and neuroimaging technology have brought neuroscientists and marketers to a common ground that allows the integration of marketing with neuroscience. Within this context, a recent key study 4 underlined the field’s two most significant issues. Firstly, the vast majority of studies are confined to studying consumers’ brains irrespectively from consumers’ behaviour, limiting their conclusions to correlational but not causal evidence. Secondly, typical neuromarketing studies are based on the assumption that a brain region is causally associated with a cognitive process. In other words, these studies often conclude based on reverse inference, e.g., that participants exhibit a particular psychological state based on the observed neural activation in a particular brain region.

Among the existing neuroimaging methods, electroencephalography (EEG) emerges as the least invasive and most affordable solution. Despite the fact that its spatial resolution is inferior compared to other neuroimaging technologies (e.g., fMRI, fNIRS, etc.), EEG is capable of recording brain activity at minuscule increments of time. Hence, EEG constitutes a favourable candidate for investigating the consumers’ brain activity. Moreover, recent advancements in the fields of neuroengineering, signal processing and machine learning have enabled neuroscientists and EEG practitioners to accurately decode users’ brain activity as registered by an EEG device. More specifically, researchers have managed to uncover neural signatures inextricably connected to cognitive aspects that are of particular interest in the context of marketing studies 5 . Motivated by the previous, EEG was the neuroimaging modality of choice incorporated in our experimental protocol.

However, despite the valuable insights that EEG may bring in investigating the consumers’ underlying neural processes, it cannot provide answers to more complex questions, like “which part of the advertising flyer captured the consumers’ attention?” 6 . Although attention is a cognitive process that could be potentially investigated by means of EEG, the identification of the exact object that draws a consumer’s attention is a task that cannot be supported by EEG-based metrics. In order to mine such information at a fine level of detail, EEG-scanners should be complemented by various physiological and behavioural monitoring tools 7 . By adopting such multimodal recording schemes, neuromarketing has actually advanced to a new era 6 . Aligned with the current tendency, our experimental protocol also employed eye-tracking (ET) and computer mouse data (position and clicks). Time-evolving observations from all these modalities were complemented with responses to questionnaires (containing demographic, profiling and product related questions) that represented the classical data-collection approach to marketing research.

Despite the plethora of EEG-related neuromarketing studies, the vast majority is based on inhouse datasets, with only two of them being publicly available 8 , 9 . The first one concerns product selection and contains only EEG data (14 electrodes), while the second one concerns advertisement appraisal solely from EEG sensors located mainly over the frontal brain region. On the contrary, we provide access to a multimodal neuromarketing dataset that revolves around the simulation of a realistic buying procedure from the catalogue of a real grocery store. Beyond the EEG signals, with 19 electrodes covering the whole 10–20 system, our dataset also includes eye-tracking and computer mouse data. Moreover, we provide responses to questionnaires that contain information with respect to each of the participants’ personality traits and demographics as well as individual product-related questions. The dataset that accompanies this paper contains both behavioural and physiological data that will enable researchers to investigate several marketing concepts (perception, decision making, product appraisal, etc.) from a neuroscientific perspective in a holistic manner. It should be noted that the purpose of this dataset is to promote exploratory research in the field of neuromarketing, hence, strict statistical significance tests should be employed in order to ensure generalizable and reliable findings.

Experimental Protocol

The experimental paradigm simulated the browsing experience of a digital advertising brochure, where the participants had to select the products they intended to buy. The paradigm was designed accordingly so as to monitor the subject’s brain and ocular activity throughout the browsing experience, with the original scope being the identification of distinct brain and ocular activity patterns with respect to selected (i.e. intended to buy) and non-selected products.

During the experimental procedure, participants were seated in a comfortable armchair placed 50 cm from a 28 inch LCD monitor. Throughout the entire process, subjects could move their head freely. However, they were instructed to confine their movements (including head) as much as possible in order to minimize the artifacts in the EEG signals, but in a manner that does not cause any discomfort that may affect their overall experience. Prior to the product presentation, resting state EEG was recorded for two minutes. Once the recording of resting state was completed, participants could freely browse among the provided brochures by pressing the keyboard’s left and right arrow to move forwards and backwards respectively. Aiming to replicate the layout of a standard advertisement leaflet that will naturally lead to a more realistic experience for the participants, brochure pages were organized in order to contain products from the same product category (e.g. the first page included dairy products, the second frozen products, etc.). In total 144 supermarket products were illustrated encompassed into 6 brochure pages (each containing 24 products). Participants were instructed to identify and select the products (by left clicking on) they intended to buy in accordance with their regular buying habits, without having any global restrictions with respect to either the cost or the total number of products bought (i.e. the total number of selected products and consequently the total cost could significantly vary among participants). We should note that amongst the 144 viewed products, on average 18 were clicked by each participant leaving 126 unclicked. Figure  1 illustrates an exemplar case of the experimental timeline and the simultaneously recorded data and events, as depicted in the upper and lower part of the figure respectively. The products selected by the subject are highlighted in a light-blue colour in the upper panel, and the corresponding mouse clicks timestamps are embedded in the data recording as depicted in the lower panel. Finally, the total number of products selected in each brochure page is also incorporated in the figure.

( a ) The NeuMa-dataset experimental protocol/timeline. Six brochure pages including supermarket products were provided to the participants, that had to select products without any restriction. ( b ) The simultaneously recorded EEG activity and the corresponding mouse clicks embedded in the timeline of EEG traces.

Once the browsing/product selection experience was completed (ended by the participants on their own time), each participant was asked to fill in a questionnaire. The questionnaire included demographic questions (e.g. age, marital status, education, etc.), profiling questions (e.g. the big five personality traits, impulsive buying behaviour, etc.) and specific questions related to each of the selected products (i.e. reasons of selection, familiarity towards the selected product and whether the selected product constitutes a frequent purchase or not).

Participant demographics

A total of 45 individuals participated in this study, all native Greek speakers, originating from Greece. However, the dataset in its final form includes 42 subjects (23 males and 19 females, aged 31.5 ± 8.84), denoted as S01, S02, …, S42, as the data from three subjects were excluded due to high levels of artifactual contamination. Figure  2 provides the distribution of the participants’ demographics, with the left panel illustrating the age and the right the education level. As it can be seen, the distribution of participants’ age is skewed towards younger population. This fact is the aftereffect of limitation imposed by the experimental protocol, where the recruitment process was confined to participants exhibiting increased familiarity with computer interaction. Beyond that, this skewness aligns well with recent statistics which indicate that the demographics for online grocery consumers are concentrated over younger ages. Additionally, we note that amongst them, 34 are single, 7 are married and 1 is divorced, while 7 of the participants had at least one child. Figure  3 incorporates the distribution of the analysis of the responses to the profiling questions, including the Big 5 personality traits 10 , various consumer traits (e.g. utilitarian/hedonic motivation) 11 , 12 , 13 , 14 , 15 , 16 and decision impacts. Prior to the recording, subjects were thoroughly informed about the experimental procedure and gave written informed consent that was approved by the Ethical Committee of the Centre for Research & Technology Hellas (CERTH), with Ref. No. ETH.COM-68.

Participants’ distribution with respect to particular demographics (age and education level).

Participants’ distribution regarding profiling attributes. The low and high levels correspond to 25% and 75% of the measuring scale of the presented attributes accordingly.

Problem formulation

The paradigm was designed to measure and study the subjects’ behavioural, ocular and brain responses during the decision making process. It naturally induced two different states (or conditions), hereafter referred to as “Buy” and “NoBuy” condition, the contrast of which is expected to reveal the essence of buying behaviour. The former condition includes the physiological responses when the participants were examining products they intended to buy. Whereas the latter condition incorporates the responses corresponding to when the participants were examining products they did not intend to buy. However, considering that the experimental scenario is of a dynamic nature and that detailed questionnaire responses are registered for each individual, the dataset can be employed to investigate various alternative scenarios, including but not limited to the following: (i) compare responses of competitive products (e.g. mustard of brand A vs mustard of brand B), (ii) compare the different consumer profiles generated via the questionnaire analysis (e.g. impulsive buyer, bargain hunter etc.) in terms of buying habits and brain/ocular activity patterns, (iii) examine what is the primal point of interest for the participants and whether they focus on the product image, textual description or price given their individual profiles, (iv) investigate the decision making processes of products characterised as “Bought” and the corresponding influential factors such as price, brand, and discount.

Data Records

The dataset is provided into two forms, with the first including the raw experimental data (i.e. EEG data, ET data and mouse-related data) and the corresponding behavioural responses (i.e. questionnaires), hereafter referred as NeuMa Raw Dataset 17 , and the second its pre-processed version, referred as NeuMa Pre-processed Dataset 18 . Additionally, the raw version of the dataset is also provided in BIDS format 19 that can be retrieved via the OpenNeuro repository 20 .

Data recording and storage

Neuma raw dataset.

The recorded raw experimental data for each subject is stored in an Extensible Data Format (XDF) format, a general-purpose container format tailored for multi-channel biosignal time-series (e.g. EEG, ET, etc.) with extensive associated meta information. Each raw file is named after the subject ID (e.g. S01.xdf) and includes the following five data streams that were timely synchronised using LabRecorder (the recording program supported by Lab Streaming Layer, https://github.com/labstreaminglayer/App-LabRecorder ): (i) the EEG data, (ii) the eye tracker data, (iii) the computer mouse clicks, (iv) the cursor positions on the screen controlled via the mouse, and (v) the markers. Each data stream is organised as a struct and besides the recorded data and timestamps also provides specific information about each stream (e.g. sampling frequency, device name, stream type, etc.). Importing XDF data in a programming framework (such as Matlab or Python) requires the use of the corresponding XDF modules ( https://github.com/xdf-modules ), while the type of each stream can be easily retrieved by the provided stream info, as illustrated in the RawDataProcessing.mlx file. A brief description for each data stream and the associated data is provided bellow:

EEG Data : refers to the brain activity recorded in a continuous mode. The data structure consists of four fields. The first two, namely info and segments , provide information regarding the recorded data, with the most crucial information being stored under info/nominal_srate and info/desc providing information regarding the sampling frequency and the names of the recording electrodes (according to the 10–20 international system) respectively. The other two, namely time_series and time_stamps , encompass the recorded EEG activity ([number of electrodes × samples]) and the corresponding time stamps (unix timestamps).

Eye Tracker Data : corresponds to the recorded gaze data, with the data structure being identical to the structure of the EEG data, except from the field time_series , where the provided information reflects the metrics for the left and right eye respectively. More specifically, the first three rows encompass data related to the left eye (i.e. 2D coordinates and pupil size) and the next three related to the right eye.

Mouse Clicks : includes the sequence of clicks pressed, with the field time_series providing information regarding the type of click (i.e. left or right).

Mouse Positions : includes the positions (i.e. 2D screen coordinates) for each mouse click included in the stream Mouse Clicks . Consequently the field times_series encompasses the 2D coordinates where each click took place.

Markers : includes information regarding the alterations among brochure pages, with the field time_series providing the ID of the image collection (ranging between 1 and 6) and the field time_stamps the starting time of each presented brochure page. Finally, the values fixation_cross and EOE (end of experiment) encountered in the field time_series , refer to the initiation and completion of the experimental process respectively. Prior to the beginning of the browsing experience, there was a two-minutes long resting state recording period, during which the participants were looking at a fixation cross).

The registered behavioural responses for each subject are provided in a .xls file that follows the same naming convention as previously described for the .xdf files. Each .xls file includes the following information:

Demographics : A series of demographic indicators namely, age, gender, education, marital status, children (boolean variable) and dominant hand.

Personality traits : A series of clustered profiling related responses that upon analysis can provide information with respect to the following: (i) Big five personality traits 10 , (ii) Utilitarian/Hedonic shopping motivation 11 , (iii) Visual/Verbal information processing 12 , (iv) Impulse buying behaviour 13 , (v) Variety seeking behaviour 14 and (vi) Bargain Hunting 16 .

Consumer Behaviour : Information related to the participant’s buying behaviour, including: (i) Buying impact factors (i.e. in what extend the product selection is affected by (a) the product’s price, (b) the product’s brand, (c) the applied discount, (d) various product related promotional actions and (e) family/friend recommendation), (ii) Total number of weekly supermarket visits/Visit duration and (iii) The use or not of shopping list.

Product Info: Information related to the products illustrated to the participants. In detail, for each product a broad description regarding the product is provided (e.g. Milk, Cereals, Biscuits, etc.) that is accessed via the file Leaflet_Product_Descriptions.mat . Additionally, for each selected product the responses regarding the following questions are provided:

Why was the product selected? - The possible responses regarding this question were: A – Price : The product was selected due to its price, B – Brand : The product was selected due to its brand, C – Discount : The product was selected because it was on a discount, D – Type : The selection was based on the product’s type (e.g. The participant was in need of milk and therefore selected one of the available milks), E – Need : The consumer was running out of the selected product and therefore was in need of it, F – Like : This was a product that the participant likes, G – Regular : This was a product that is regularly bought by the participant, H – Combination : This product was selected with the purpose of being combined with another selected product (e.g. cereals were selected, due to the previous selection of milk), I – Alternative : The product was selected as it was the closest alternative choice available (e.g. milk of brand A was selected, as milk of brand B was not available), J – Other : The product was selected for a reason that is not included in the previous options

Is this a familiar product? - The response to this question indicates if the product is frequently bought by the participant ( A - Yes ) or not ( B - No ).

Does this product constitute a frequent selection? - The response to this question indicates how familiar the participant is with the specific product. The responses range in a five-point scale (i.e. ( A - totally unfamiliar ) - ( E - completely familiar )).

It is important to note here, that an additional .xls file that provides the encoding regarding the previously described information can be found under the name of Questionnaire_Coding.xls .

Additionally, the EEG electrode location coordinates and other electrode related information can be retrieved via the chanlocs_dsi24.mat file. Finally, the images of the 6 brochure pages used in the experimental process alongside with the bounding box coordinates of each product are provided as separate files. More specifically, the .tiff images are provided using the naming convention [ ImagePage_1.tiff, ImagePage_2.tiff, …, ImagePage_6. tiff] under the folder Brochure_Pages , while coordinates are provided via.mat files in the form [BoundingBoxPage_1.mat, BoundingBoxPage_2.mat, …, BoundingBoxPage_6.mat] under the folder BoundingBox_Coordinates .

NeuMa pre-processed dataset

The pre-processed dataset besides the pre-processed timeseries for both the EEG and the ET datastreams, also provides the data segmentation per product, various product related information and the data extracted from the questionnaire analysis. Again the data is provided for each subject independently in a.mat file, under the same file naming convention described for the raw dataset. Additionally, the transition from the raw dataset to the pre-processed can also be achieved using the provided Matlab Live Script that can be accessed via Github (NeuMa Pre-Processed Dataset, https://github.com/NeuroMkt/NeuMa_Dataset_Processing ) under the name RawDataProcessing.mlx . Finally, a second Matlab Live Script that provides general guidelines for handling and accessing the pre-processed data can also be found on our Github repository, under the name PreprocessedDataHandling.mlx. In the following a brief description regarding the data and information provided for each subject is provided:

The preprocessed EEG and ET data streams . In the first stream, raw EEG signals were subjected to a bandpass filter (butterworth, 3rd order, zero-phased) within 0.5–45 Hz, followed by the removal of artifactual activity via the combined use of Artifact Subspace Reconstruction (ASR) 21 and FORCe 22 . In the second (eye tracking), linear interpolation was applied to the gaze data in order to fill in missing data points (taking place during spontaneous eye blinking). Additionally, the sampling frequency (Fs) and the channel information (chans) for each data stream is provided. Finally, for each subject, the pre-processed data streams for EEG and ET are stored in variables EEG_clean.data and ET_clean.data respectively.

Information for each product . The information is provided in the form of PageNumber/ProductNumber and includes the following info for each product:

Product Info: Incorporates the label regarding the “Buy” – “NoBuy”categorization (variable bought : {Buy - 1; NoBuy - 0}). Each product is also associated with three extra variables, namely Reasons, Familiarity and FrequentBuy answering the questions “Why was the product selected?”, “Is this a familiar product?” and “Does this product constitute a frequent selection?” respectively. In the case the product was not selected (i.e. bought  = 0) all the aforementioned variables are set to zero. Finally, under the variable Description , the description for each product is provided (e.g. Milk, Cereals, Biscuits, etc.)

EEG segments: Provides the sample indices (in the form [sample start, sample_end]) of the continuous EEG recording (i.e. variable EEG_clean.data ) that correspond to the time during which the participant spent looking at each product as defined by the smallest bounding box that contains the whole product image.

ET segments: Provides the sample indices (in the form [sample start, sample_end]) of the continuous ET recording (i.e. variable ET_clean.data ) that correspond to the time during which the participant spent looking at each product as defined by the smallest bounding box that contains the whole product image. Additionally, the interested reader may also refer to our GitHub repository, where the tools for alternative segmentation strategies (as functions implemented in Matlab that can operate on the provided data streams) are provided. The aforementioned functions that facilitate the extraction of eye-related metrics (such as fixations and dwell time) can be combined with the stream conversion function (i.e. converterET2EEG.m ) to examine the dataset from different viewpoints.

Personalised information collected via the questionnaires . The information gathered for each subject are separated into two main categories:

Demographics - The demographic indicators for each subject (as described in the raw dataset).

Profile : The analysis of the questionnaire responses under the categories Personality Traits and Consumer Behaviour , resulted in the aggregation of the following general measures portraying each participant: (i) Big five personality traits, (ii) Utilitarian/Hedonic shopping motivation, (iii) Visual/Verbal information processing, (iv) Impulse buying behaviour, (v) Variety seeking behaviour, (vi) Bargain Hunting, (vii) Buying impact factors (including a score for price, brand, discount, advertisement and suggestion impact), (viii) Total number of weekly supermarket visits/Visit duration and (ix) Employment of a shopping list (boolean variable).

Finally, the brochure images and the bounding box coordinates are provided in the same way with the raw dataset.

The format of the NeuMa Pre-processed Dataset, is graphically illustrated in Fig.  4 , where the data and information provided for each subject, as described in this subsection, are depicted.

Illustration of the NeuMa Preprocessed Dataset format.

Technical Validation

A total of 42 subjects that had normal or corrected-to-normal vision and none of them had taken any psycho-active or psycho-tropic substance, participated in the experimental process. Additionally, none of them reported a history of psychiatric disorders, neurological disease or drug use disorders.

The raw data was recorded using Wearable Sensing’s DSI 24 ( https://wearablesensing.com/products/dsi-24/ ) and Tobii Pro Fusion ( https://www.tobiipro.com/product-listing/fusion/ ) for the EEG and ET data respectively. The brain activity was recorded, with a sampling frequency of 300 Hz, via 21 dry sensors, namely Fp1, Fp2, Fz, F3, F4, F7, F8, Cz, C3, C4, T7/T3, T8/T4, Pz, P3, P4, P7/T5, P8/T6, O1, O2, A1 and A2, that were placed at locations corresponding to the 10–20 International System. The Sensors A1 and A2 were the reference electrodes \(\left(\frac{A1+A2}{2}\right)\) and were placed on the mastoids. Prior to the experimental procedure, impedance for all electrodes was set below 10KΩ and EEG signals were inspected to avoid any irregularities. Additionally, an offline quality control was performed for the recorded data of each subject. More specifically, we employed the EEG Quality Indices (EQI) as proposed by Fickling et al . 23 , in order to assess the quality of the raw EEG signals. Hence the recorded EEG signals are segmented into 1-second-long non-overlapping windows and each window is being rated (i.e., excellent, good, poor, bad) according to its quality as defined by the EQI. As it can be seen in Fig.  5 , approximately 80% of the total EEG recordings can be characterized as of excellent quality, whereas approximately 95% is at least of good quality. The remaining 5% corresponds to artifacts that were removed in the preprocessing stage, the steps of which are provided in Section NeuMa Pre-processed Dataset . Moreover, the recorded EEG data and their spectra were visually inspected upon the application of pre-processing steps (i.e. band pass filtering and artefact removal) and were found to be in line with the expected/typical encephalographic activity. An exemplar case for a randomly chosen subject (i.e. S14) is provided in Fig.  6 , while the spectra for the entirety of the dataset can be accessed via the Figshare repository. The gaze data was recorded via Tobii’s screen based solution, with the eye movements being captured at a sampling frequency of 120 Hz. Prior to initiation of the experimental procedure, a 9-point calibration was performed for each subject, using the Tobii Pro SDK ( https://www.tobiipro.com/product-listing/tobii-pro-sdk/ ), to formulate the individual’s eye model and gaze point. Finally, it should be noted that the selected products reported in the questionnaires were cross-checked with the ones registered via the corresponding mouse clicks.

Percentage of EEG recording characterized by excellent ( a ) and good quality ( b ), based on the EEG Quality Indices.

EEG recordings and the corresponding power spectrums regarding subject S14 for the raw (upper panel) and the pre-processed (lower panel) versions of the dataset.

Usage Notes

The experimental datasets can be accessed and downloaded from the publicly accessible repository of Figshare, without mandatory registration, under the name NeuMa Raw Dataset and NeuMa Pre-processed Dataset. The data analysis can be performed either on Matlab or Python as implementations for loading .xdf files are available for both programming languages. However, we recommend the data analysis to be performed in Matlab, as several modules and functions have been already deployed by the authors of this dataset and can be accessed via GitHub ( https://github.com/NeuroMkt/NeuMa_Dataset_Processing ) Concluding, we strongly recommend the employment of both Live Scripts ( .mlx files) as they demonstrate the practical usage of the dataset in a comprehensive manner.

In the following, we provide the key steps/instructions with respect to data loading, parsing and processing, as well as all the required steps that enable the transition from the raw dataset to its pre-processed version. The aforementioned steps have also been incorporated in the Matlab Live script named RawDataProcessing.mlx , that can be found in our Github repository:

Data Loading : Loading an .xdf file either in Matlab or in Python requires the corresponding xdf module.

Data Parsing : Once the loading process is completed, five separate datastreams registered as structs are available in the workspace. Hence, a data parsing process is required to identify the exact type of the stream, with the required information being stored in the field info for each struct.

Preprocessing on EEG Data : The function EEG_preprocessing.m , incorporates the preprocessing steps, including but not limited to bandpass filtering and artefact rejection, applied on the continuous EEG recording.

Preprocessing on ET Data : The linear interpolation performed to the gaze data is utilized by the function interpNANs.m .

Segmentation : The EEG an ET signals are segmented on a product basis. Therefore, for each product, we provide the EEG and ET segments that correspond to the time intervals during which the participants’ gaze was located within the bounding box that encloses each product. These segments can be used in order to isolate the brain and ocular responses corresponding to particular products. The segmentation process includes two steps that are executed in sequence and are incorporated in the function segmentationLeaflet.m . Firstly, the data-stamps from the stream Markers alongside with the bounding boxes’ coordinates are exploited to define a brochure page and a product respectively. Then, the gaze coordinates are used to identify the segments (if available) during which the participant was observing each product.

Product Labelling: Firstly, the mouse streams are exploited to identify the products that were selected by the participants. Then, the questionnaires were analysed in order to provide additional information (i.e. reasons of selection, familiarity with the product and buying frequency). It should be noted that the aforementioned additional information is provided only for the selected products. Finally, the category for each product is loaded from the corresponding file ( Leaflet_Product_Descriptions.mat ), so as to provide a rough description for each product. These are implemented in functions segmentationLeaflet.m and parseExcelProfiles.m .

Stream Conversion : Provides the transition, in terms of samples, from the ET to the EEG datastream by converting the corresponding time aligned samples. The function that materialises the stream conversion can be found on the GitHub repository under the name converterET2EEG.m .

Code availability

The Matlab code developed for data loading, data parsing, segmentation and preprocessing required for the use of both datasets (provided via Figshare) alongside with two Matlab Live Scripts that demonstrate the data handling process for each dataset are provided in our Github repository (NeuMa Raw Dataset, https://github.com/NeuroMkt/NeuMa_Dataset_Processing/tree/main/NeuMa_Raw_Usage_Code ; NeuMa Pre-processed Dataset, https://github.com/NeuroMkt/NeuMa_Dataset_Processing/tree/main/NeuMa_PreProcessed_Usage_Code ).

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Acknowledgements

This work was a part of project NeuroMkt that had been co-financed by the European Regional Development Fund of the European Union and Greek National Funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH CREATE INNOVATE (Project code T2EDK-03661).

Author information

These authors contributed equally: Kostas Georgiadis, Fotis P. Kalaganis.

Authors and Affiliations

Centre for Research & Technology Hellas, Information Technologies Institute (ITI), Thermi-Thessaloniki, Greece

Kostas Georgiadis, Fotis P. Kalaganis, Vangelis P. Oikonomou, Spiros Nikolopoulos & Ioannis Kompatsiaris

Aristotle University of Thessaloniki, Department of Journalism and Mass Communications, Thessaloniki, Greece

Kyriakos Riskos & Ioanna Yfantidou

Erasmus University Rotterdam, Department of Media and Communication, Rotterdam, the Netherlands

Kyriakos Riskos

D. Masoutis S.A., Thessaloniki, Greece

Eleftheria Matta & Kyriakos Pantouvakis

MMS Advertising – Full Service Agency, Thessaloniki, Greece

Dimitris Chantziaras

Artificial Intelligence & Information Analysis Lab, Department of Informatics, School of Sciences, Aristotle University of Thessaloniki, Thessaloniki, Greece

Nikos A. Laskaris

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Contributions

K.G. collected the data, processed the EEG recordings and wrote the manuscript. F.K. collected the data, processed the ET recordings and wrote the manuscript. K.R. designed the questionnaires. E.M. designed and analysed the questionnaires. V.O. participated in the data collection process and performed the data quality assessment. I.Y. performed the questionnaire analysis. D.C. designed the brochure pages. K.P. provided critical information regarding the product placement in the brochure pages. S.N. conceived the study and reviewed the manuscript. N.L. supervised the experimental protocol design and the data collection process. I.K. conceived the study and offered critical revisions.

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Correspondence to Kostas Georgiadis .

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Georgiadis, K., Kalaganis, F.P., Riskos, K. et al. NeuMa - the absolute Neuromarketing dataset en route to an holistic understanding of consumer behaviour. Sci Data 10 , 508 (2023). https://doi.org/10.1038/s41597-023-02392-9

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DOI : https://doi.org/10.1038/s41597-023-02392-9

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REVIEW article

Neuromarketing as an emotional connection tool between organizations and audiences in social networks. a theoretical review.

• 1 Department of Applied Communication Sciences, Complutense University of Madrid, Madrid, Spain
• 2 Faculty of Business and Communication, International University of La Rioja, Madrid, Spain

Currently, there is an important debate on how social networks have affected relations between organizations and their audiences: originally complementary –since organizations had full control over the messages that they sent to users, who were mere consumers of information–, they are now symmetric –since users produce and disseminate information about organizations on a global scale through social media–. Therefore, one of the main concerns of organizations when investing in social networks is to connect with their target audience, to have virality, greater visibility and scope. Likewise, neuromarketing is gaining significant importance when it comes to predicting user behavior through biometric measurements, so it can be an essential tool for developing content that engages organizations and their audiences. The main objective of this work is to conduct a theoretical review of the main scientific research on the effectiveness of neuromarketing as a tool to improve the emotional connection between organizations and users in social networks. Thus, the scientific literature on the object under study available on the Web Of Science has been extensively reviewed. The results of the analysis of the main researches in this field reveal the importance of neuromarketing: some of them agree that the communicative effectiveness between organizations and audiences in social networks depends more on sociology and psychology than on technology itself. Neuromarketing has also allowed to demonstrate the relevance of the so-called social influence in social networks: users tend to imitate the behaviors of others, under the premise that these actions reflect the appropriate procedure. That is, when a user sees that others in their environment comment or share a post, they tend to replicate that action in order to avoid the fear of being the only one who behaves differently.

Introduction

Neuroscience is introducing new ways to understand various fields of scientific knowledge, among them, its contributions to understand the operation and effects of advertising on potential consumers must be highlighted. Morin (2011) indicates that the concept comes from the combination of “neuro” and “marketing,” which implies the fusion of two major fields of study (neuroscience and marketing). Neuroscience was developed by Gerald Zaltman, and aims to help marketers understand how the human brain is physiologically affected by advertising and marketing strategies ( Lee et al., 2007 ). It is one of the newer branches of the advertising industry, as it is an emerging interdisciplinary field linking the knowledge of psychology and neuroscience to marketing ( Gurgu et al., 2020 ). This science is in an embryonic state, as marketing professionals are just beginning to unveil the brain circuits involved in finding, choosing and purchasing a product. While many of the studies conducted by neuromarketers are commercial and, as such, do not go through the standards nor the review process imposed by academics, enough evidence has already been published to highlight some neurocognitive principles at play when consumers perceive advertising messages ( Morin, 2011 ).

Ariely and Berns (2010) affirm that marketers are enthusiastic about this new science for two fundamental reasons. Firstly, because they believe that these types of techniques will make it possible to offset costs and benefits. This hope is based on the idea that consumers are not able to expressly articulate their purchasing preferences when explicitly asked, and that their brain possesses hidden information about their true predilections. Such information could be used to influence their purchasing behavior so that the cost of conducting neuroimaging studies would be offset by the benefit of better product design and higher sales. In theory, at least, brain imaging could shed light not only on what people like but also on what they will buy. The second reason is that they hope it will provide an accurate market research method that can be implemented even before a product exists. Nonetheless, Fisher et al. (2010) indicate that neuromarketing raises important professional, ethical and scientific concerns. This new field exemplifies the complicated question of professional ethics applied to academic-business relationships. Furthermore, as it is a new application of neuroscience methods, it presents important considerations for responsibly conducting research and its public understanding.

Materials and Methods

The objective of this article is to review the most recent investigations that analyze neuromarketing as a tool that connects consumers and organizations through social networks. This review highlights the conceptualization of the relationship between disseminated content on social networks and the effect that these platforms have on users’ emotional responses. In order to contextualize this review, selected articles had to meet the following requirements: they focus on the study of social networks, carry out a research about the possibilities of these platforms for generating emotions and/or analyze the effects they produce on the user. Based on these criteria, the article is structured around three main blocks: a first section, which introduces the concept of neuromarketing and its influence on consumer decision-making; a second section, in which the characteristics and possibilities of social networks as platforms for generating emotions are reviewed; and a third section, focused on the effects they produce on the user:

1. Neuromarketing and consumer decision-making.

2. Social networks and emotions: content, language, tools, and possibilities.

3. Social networks and emotions: user reactions, determining elements, and engagement implications.

This review is descriptive in nature, as it aims to provide an update on the concept of neuromarketing in relation to a constantly evolving medium such as social media.

The first criterion for the selection of articles was their presence in the Web of Science platform since it includes the references of the main scientific publications of any discipline of knowledge, both scientific and technological, humanistic and sociological, since 1945. The second criterion was novelty, as in, that their publication date was as recent as possible. In this sense, it is necessary to highlight that the discipline of neuromarketing is relatively young and, if included to the study of relationships between companies and consumers in social networks, the bibliographic corpus is considerably reduced.

In order to present and synthesize the characteristics of the included studies, the aforementioned eligibility criteria have been followed. A total of 75 articles were selected. The earliest publication date was 2000 and the most recent, 2020.

It is important to highlight that the articles reviewed which were published between 2000 and 2010 mainly refer to the concept of neuromarketing and its application in the analysis of advertisements in conventional mass media.

Table 1 provides detailed data on the publications covered in this study.

Table 1. Research on neuromarketing and emotions in social networks.

Neuromarketing as a Tool for Anticipating Consumer Decision-Making

Bault and Rusconi (2020) indicate that, in recent years, knowledge on the neurobiology of choice has increased significantly. Research in the field of decision-making has identified important brain mechanisms that construct a representation of an option’s subjective value based on previous experience, recovered, and compared with that of other options available to choose from. Lim (2018) ensures that neuroscientific methods encapsulate the use of tools and techniques to measure, map, and record brain and neuronal activity during behavior and, in doing so, generate neurological representations of that activity to understand specific responses in the brain and in the nervous system as a result of exposure to a stimulus. These methods, which allow neuroscientists to observe the neural processes that occur during behavior in real time, can be classified into three broad categories: neuroscientific tools and techniques that record neural activity within (electromagnetic and metabolic) and outside the brain, and neuroscientific methods to manipulate neural activity.

Ambler et al. (2000) carried out two small-scale experiments with neuromarketing techniques, in order to determine the effects of emotional and rational advertising on users exposed to it. The authors based their study on the results of previous research that indicated that emotional advertising generated higher levels of recognition and memory than purely cognitive. Their first experiment tested these conclusions with conventional methods before using pharmacological treatments (β blockers), to see if reducing the impact of affection also reduced the difference between remembering and recognizing both types of advertising. The second experiment used Magneto-Encephalography (MEG) to investigate whether there were distinguishable patterns of brain activation in time and space between affective and cognitive advertising. In the preliminary experiment, recall and recognition of affective advertisement were significantly stronger for both the control and placebo groups. Recall decreased in the group which used the drug. The results related to recognition were not definitive. Harris et al. (2019) researched the use of consumer neuroscience to improve and determine the effectiveness of ads related to public health and social causes in digital media. This study showed that action/emotion-based marketing communications that ask people to act, share, promise or challenge tend to be more effective than those based on rationality. Also, none of the highest attention peaks were produced when viewing the brand logos. Besides, Hafez (2019) explains that marketing specialists must develop a positive and favorable brand image in the minds of customers through the development of attractive ads with emotional content. Neuromarketing research has empirically evidenced that most purchasing decisions are made emotionally. Therefore, creating initiatives to build an emotional bond is the main task of experts to improve marketing performance.

Neuromarketing has allowed to analyze how the type of medium in which advertising is inserted impacts the emotional reaction of the viewer. Baraybar-Fernández et al. (2017) carried out a research focused on discovering the relationship between the emotions induced in audiovisual advertising messages and their impact on the subject’s memory. To achieve this, they carried out an experiment with eight audiovisual advertising messages (six representatives of six basic emotions: joy, surprise, anger, disgust, fear and sadness; and two rational ones). On the one hand, they used neuromarketing techniques such as the cardiac electrical activity (ECG) and the electrical activity of the dermis (AED) of the subjects. On the other, a conventional research technique was also used: a questionnaire applied to the subjects who participated in the research. The results showed that, both for the suggested memory of the message transmitted and for the activity of the advertiser, the announcement with the best results was that of sadness, an announcement that was also considered the most attractive by the subjects under study. Accordingly, Vecchiato et al. (2014) carried out an experiment to investigate cognitive and emotional changes in brain activity evaluated by neurophysiological indices while watching television commercials. In particular, they recorded electroencephalogram (EEG), galvanic skin response (GSR), and heart rate (HR) in a group of 28 healthy subjects while watching a series of television commercials that were grouped by category. They performed brain index comparisons to highlight gender differences between categories and scenes of interest from two specific ads. The results show how EEG methodologies, together with measurements of autonomous variables, can be used to obtain hidden information from advertisers that is not otherwise accessible. One of the main findings was to determine that these tools allow analyzing the perception of television advertisements and differentiating their production according to the gender of the target audience.

These techniques have also been effective in analyzing the effects of print advertising. For example, Dos Santos et al. (2019) analyzed how sponsorships functioned in sports posters. The authors’ objective was to examine the influence of congruence (perceived and effective) and the level of visual attention toward sponsors on recall as well as purchase intention in sports sponsorship by applying neurophysiological measures. The experiment used eye tracking techniques with 111 men and 129 women ( n = 24) with 24 sports posters from three different disciplines (sailing, tennis, and F1), with varying consistency, number of sponsors, and position. The results showed that the recall of the brand is influenced by the number of sponsors present on the poster and by the time of fixation. Likewise, it has been shown that the use of sexual claims in advertisements published in print media does not increase brand recall, compared to those that do not use this type of strategy ( Fidelis et al., 2017 ).

Guixeres et al. (2017) studied whether it was possible to predict the effectiveness of advertisements on digital channels by using neural networks and metrics based on neuroscience (brain response, heart rate variability, and eye tracking). The neurophysiological records of 35 participants were exposed to eight Super Bowl television commercials. Correlations between metrics based on neurophysiology, ad recall, ad likes , the audience rating provided by ACE metrix, and the number of YouTube views over a year were investigated. Results suggest a significant correlation between neuroscience metrics, the advertising effectiveness self-report, and the direct number of visits on the YouTube channel. This study is a pioneer in the use of neurophysiological methods to predict advertising success in a digital context. Likewise, some researchers have shown that the electroencephalography (EGG) technique can provide indications about a subject’s interest in watching a video or the possibility of closing and skipping it without seeing it ( Libert and Van Hulle, 2019 ).

In 2011, Kendall Goodrich analyzed the relationship between attention to online advertising, attitude toward the brand, suggested memory, and purchase intention. Thus, attention tracking techniques were used in a controlled online environment. The results of this experiment suggest that attention is positively related to the suggested memory and purchase intention, but negatively related to the attitude toward the brand.

With the advent of web 2.0, neuromarketing is providing interesting data to advertisers on the effectiveness of their advertising on social networks. Muñoz-Leiva et al. (2019) carried out research on travel advertising on social networks and showed that it is more effective when inserted in media with little editorial content such as Facebook or specialized blogs. They also showed that the use of celebrities as a claim in these types of ads captures the attention of potential consumers.

In their study on social cognitive processes and neural systems, Meshi et al. (2015) described the social motives that drive people to use social networks and proposed systems for their use. The use of social networks occurs for two main reasons: connecting with others and managing the impression they leave on others. People try to satisfy their basic social needs on these platforms and adopt behaviors based on social cognition, thinking about the mental states and motivations of other users; self-referential cognition, publishing information about themselves; and social reward processing, social connection suggestion, or reputation enhancement. Meshi et al. (2013) studied the relationship between the way the brain processes earnings specifically relevant to reputation and the degree of use of Facebook. In their study, the authors demonstrate that, when users respond to gains in self-reputation, relative to observing the gains of others, the intensity of users’ involvement with Facebook can be predicted. Turel et al. (2018) research the excessive and compulsive use of social networks in order to understand the brain systems and processes that are involved in addition to these platforms. Symptoms of addiction to social networking sites are manifested in usage behaviors that focus on immediate profits and weighing their misuse with future consequences.

Using neuromarketing for social media analysis enables companies to look past big data and go beyond the socially desired responses, as it brings to light real reactions. Therefore, the effort has a great final reward. However, to be sustainable, since this is a joint effort (companies need the help of consumers for data collection), the communication strategy should focus on showing consumers how they are benefited ( Constantinescu et al., 2019 ).

Emotions: Content, Language, Tools and Possibilities in Social Networks

User engagement and participation have become central non-transactional concepts in the new era of marketing. The work of Cvijikj and Michahelles (2013) analyzes how the characteristics of the content communicated by a company on Facebook affect user behavior. The authors focused on the type of medium, the type of content, the day and time of publication, the number of likes, comments, shared actions, and duration of interaction on the brand page on this platform. Their results suggests that entertainment content is the most influential, posts with information related to the brand increase the level of engagement through likes and comments, photos are the most attractive type of publication medium, and the amount of comments is higher in posts shared on weekdays. Khan et al. (2016) analyzed the impact of cultural differences on social networks and the commitment, loyalty and brand recommendations of users. According to these authors, videos are an influential element and improve the number of likes, comments, and shares. The number of comments tends to be higher in this type of content and when the brand’s posts stays for a longer time at the top of the page. However, this work shows that CSR-related posts do not improve the number of comments nor the number of times content is shared. de Vries et al. (2012) analyzed possible factors that drive the popularity of brands’ posts on social media. Their study on eleven international brands determined that the position of the post at the top of the brand’s fan page improves its popularity, and that positive comments on a brand’s posts is positively related to the number of likes. Kim et al. (2015) studied the marketing practices implemented on Facebook by the world’s leading brands in order to detect the qualitative factors of the messages most likely to generate a consumer response. Consistent with the studies noted above, the results of this research indicate that images attract more consumer responses than those based solely on text and, on several occasions, tend to receive more responses than video content. According to this study, the content published more frequently on the pages of this social network is oriented toward interaction, something that may be due to the intention of promoting customer-brand relationship in the long term.

The characteristics of the content disseminated on social networks affect the forms of user interaction, but so do the sector and the characteristics of the organization. Schultz (2017) also studied user participation in brand posts, considering their characteristics, duration, number of fans, and industry. This author identified differences in user participation depending on activities and industries. According to the study, environmental variables such as market and target group characteristics affect consumer engagement with brand messages. Therefore, social media strategies must consider market and target group segmentation. Swani and Milne (2017) studied how the Fortune 500 companies’ brand content strategies favor the reach of popularity on Facebook, analyzing the differences between brands of goods and of service. The results of their work show that the use of corporate brands is more popular in service-related posts, while the use of product brands, images, and videos is more popular for product posts. According to these authors, posts related to services generate more comments than those related to goods. Comparing business-to-business (B2B) models and business-to-consumer (B2C) models, Swani et al. (2017) analyzed brand content published on Facebook by Fortune 500 companies in B2B markets compared to B2C models. These authors studied the key factors that influence the popularity of the content of this social network, based on the theory of psychological motivation. Their results indicate that the inclusion of corporate brand names, functional and emotional appeals in messages, the lack of direct calls to purchases or sales, and the inclusion of informative content increases the popularity of B2B messages compared to B2C messages. Shen et al. (2017) researched whether media-based emotions can be used to predict future commodity market returns. These authors provided more evidence on the effects that news and emotions based on social networks have on the commodity market. Hwong et al. (2017) studied the participation of users in science-related messages on Facebook and Twitter. Through supervised learning algorithms, they identified several unique characteristics of space science communications. These authors presented a predictive model to forecast the levels of user participation in posts. Their results indicate that the levels of interaction in the messages related to space science in social networks can be predicted with an accuracy of close to 90% using only content-based features. This study identifies anger and anxiety in messages, linked to pressing global problems such as climate change or disasters due to natural phenomena, the rarity of safe and positive publications related to this field, and the good reception by the public of messages with positive emotions and visual elements as exclusive characteristics of this field.

The identification of feelings and the analysis of the opinions of individuals disseminated on social networks facilitate the understanding of public opinion and the recommendation of content on these platforms for users. In Smith and Seitz’s (2019) paper on correcting neuroscience myths via Facebook, it is evident that readers evaluate articles more positively when they are consistent with pre-existing opinions. However, their study suggests that submitting articles related to correcting those myths immediately after exposure to misinformation may reduce belief in them. However, the research by Vermeulen et al. (2018) on the social exchange of emotions between adolescents on social networks indicates that updates on Facebook, Instagram, and Snapchat are mainly used to share positive emotions, while Twitter and Messenger are used to share negative emotions. The research by Goh et al. (2013) analyzed the interaction between users and administrators on clothing brand pages on Facebook, considering the impact of content created by consumers and sellers. Their results show that participation in brand communities of social networks leads to an increase in purchasing expenses and that the social impact of user-generated content is stronger than the content published by the administrator of these pages to stimulate consumer buying behavior. Kim and Yang (2017) analyzed how the actions of commenting, sharing, and reacting to Facebook posts can be used to improve the ranking of users’ feelings. According to these authors, behaviors such as liking, commenting, and sharing contribute to the classification of feelings and are necessary for calculating feelings polarity. Hong and Cameron’s (2017) results show that, in a crisis situation, users tend to consider the reputation of organizations more positively when they read online comments defending the company, compared to when they only read the news. According to these authors, comments can motivate people to redirect the crisis in a positive direction.

Given the vast content generated on social networks and the increasing amount of information, Chang et al. (2019) proposed a method for analyzing the emotional aspects of the Chinese vocabulary and evaluating the massive comments of movie reviews on social platforms. Their approach improves the effectiveness of recommendation systems, based on machine learning and emotional information. In order to share valuable information at the right time, Lee and Kahle (2016) analyzed the linguistic composition of the content of social networks in sports, specifically the communication of teams and sports equipment companies on Twitter. These authors presented a framework for understanding the choice of certain words in sports communication, their association with social interests, the complexity of thought, and other psychological processes. Also, Ranganathan and Tzacheva (2019) proposed a model for the automatic detection of emotions in Twitter messages. Considering the emotions of the user, their research allows extracting rules of action to provide suggestions with a wide variety of applications in teaching, customer satisfaction, or business improvement models, following the automatic data classification model Support Vector Machine LibLinear, by Fan et al. (2008) . Through machine learning, Rout et al. (2018) identified feelings from unstructured data, specifically on Twitter and SMS (messages via mobile phones). These authors evaluate the utility of supervised and unsupervised algorithms for the classification of these feelings. For their analysis, they generated a test lexicon in their corpus and took advantage of Google’s search engine to determine the score of each term using precise mutual information. Carrillo et al. (2015) proposed a tool for the study of semantic structures, dependent on time, based on the social network Twitter. This measure of time-dependent semantic similarity is validated for use in synonyms in cases that do not involve a highly specialized semantic space, such as a given professional field, and allows semantics to be defined using more colloquial language expressions.

Beyond verbal rating systems, Moussa’s (2019) study focused on a non-verbal mechanism: the emoji. This author introduced a new emoji-based metric for monitoring consumer emotions toward brands on social media, associated with the American Customer Satisfaction Index (ACSI). The author suggested that this abbreviated communication mechanism may be more diagnostic than complete statements. Gómez-Adorno et al. (2016) presented a lexical resource to preprocess social network data based on neural networks and also includes systems of non-verbal mechanisms: emoticons. This research on PAN 2015 and PAN 2016 author profiles includes slang word dictionaries, contractions, abbreviations, and emoticons commonly used on social media in English, Spanish, Dutch, and Italian.

The collection of user data and the self-learning of these tools must be carried out without the user perceiving that their privacy is being violated. Aguirre et al. (2015) demonstrated that when companies collect information about users to offer them personalized online advertising, the expected results are not always achieved since it can make consumers feel that their privacy is being violated. Their exploratory field study on Facebook showed sharp falls in the click-through rate when customers realized that their personal information had been collected without their consent. When companies collect user data in an open way, they exhibit higher click intentions in response to personalized ads, as opposed to when companies covertly collect information. The effect reflects the feelings of vulnerability that consumers experience when companies engage in covert information-gathering strategies.

Regarding crisis communication, Vignal Lambret and Barki (2018) analyzed how the emotions of online stakeholders can help companies face a crisis in social networks and, consequently, minimize the threat of reputation. These authors presented a crisis management matrix on social media and emphasized the need for flexible, stakeholder-focused approaches that can influence crisis development and resolution. Xu and Wu (2017) studied the effect of incorporating sympathy through social networks to counteract psychological reactance in crisis communication. The results of their research suggest that using Twitter and expressing sympathy significantly reduces reactance.

Posting images of users on social media can be linked to aspects related to self-esteem or specific themes correlated with narcissism and concerns about appearance. Nash et al. (2019) studied whether people with high levels of narcissism regulate their anguish through approval on social networks. Results indicate that validation on social networks reduced the anguish caused by social exclusion for those with a greater sense of leadership and mastery. Sion’s (2019) study on selfies posted by American adults on social media and Barry et al.’s (2019) research on the publication of selfies on Instagram and the self-perception of university students offer an analysis on the communication of emotions through visual content. Results indicate that users can post self-portraits as a way of acting in accordance with a prevailing cultural norm. Along these lines, Wang et al.’s (2017) research on the psychological effects of posting and viewing selfies and group posts on social networks emphasizes that recurring viewing of selfies may be linked to decreased satisfaction with life, in contrast to a group view associated with fuller satisfaction.

Nonetheless, there are studies that show that social networks, through influencers, can help create social awareness around different topics. Mañas-Viniegra et al. (2019) conducted a study to determine how attention is paid to fashion advertising and awareness-raising around physical appearance by curvy influencers compared to advertising by fashion brands on Instagram. They carried out a biometric eye tracking on a sample of 120 participants from Spain and Portugal, whose profile coincided with that of the main users of the social network under study: urban women under 25, interested in fashion and who perceived themselves as curvy. The results indicate these curvilinear influencers are raising awareness, focusing more on imperfections than on the same fashion items that they promote.

Emotions: User Reactions, Elements That Determine Them and Engagement Implications

When analyzing the object of study from the user’s perspective and reviewing the existing bibliography that addresses this issue, there are three thematic trends that stand out above the rest: the analysis and categorization of possible emotional reactions by users, the attempts to identify the possible elements that influence these reactions, and the implications that users’ emotional reactions may have in terms of engagement.

Regarding the first of these two questions, Brynielsson et al. (2014) developed a tool to classify user reactions on Twitter during a crisis and identified four main categories that correspond to the same number of user reactions. Specifically, they refer to positive reactions, fear, anger, or other. In the first case, they collected the reactions that show happiness or, at least, positive feelings. In the second, they collected reactions that reveal that people are scared, worried or afraid for some reason. In the third, they collected the reactions of users who showed anger or disappointment. Finally, the category of “others” is defined by exclusion, and groups all those reactions that do not correspond to any of the other three categories indicated.

Different authors delve into some of these specific feelings or emotions. Some of them relate the consumption of content on Facebook to the appearance of positive emotions ( Mauri et al., 2011 ; Lin and Utz, 2015 ). Thus, for example, a feeling of well-being, coupled with a highly positive valence and a high level of excitement are usually the most common reactions ( Mauri et al., 2011 ).

In turn, Lin and Utz (2015) delved into these aspects and analyzed the influence of the strength of the existing link on the reaction that arises in a person who reads a post on Facebook. These authors referred to two main mechanisms to explain this phenomenon, one of which is closely related to emotions: emotional contagion and upward social comparison. Both are closely connected with two of the most common emotional responses in these cases and which, as different authors have shown, can be given in online communication and not only face-to-face: happiness ( Cheshin et al., 2011 ; Coviello et al., 2014 ) and envy ( Tandoc et al., 2014 ).

The feelings manifested by the subjects who participated in the study by Lin and Utz (2015) are mostly positive. Specifically, they define themselves as connected, informed or entertained. On the other hand, when it comes to negative feelings –much less common– subjects are defined as envious, jealous, annoyed, and frustrated. The final conclusions of these authors suggest that when users browse Facebook, positive emotions prevail over negative ones. The second conclusion has important implications for brands: the strength of the existing link facilitates the generation of a feeling of happiness or, in the case of envy, it is benign. Meanwhile, when the link does not exist or is not as strong, malicious envy is more likely to appear, even if the tone of the message that has been read on Facebook is positive.

Stieglitz and Dang-Xuan (2013) , researching political communication on Twitter in Germany, insisted on this idea, concluding that messages that contain some kind of emotional power (regardless of whether it is positive, negative, or mixed) are much more likely to be viralized in some way (shared, retweeted, etc.) than those that don’t. The reason is that these are messages that have a much greater probability of generating some type of reaction in the user and thus make them feel the need to share them, incorporating their own personal point of view on the subject. Chmiel et al. (2011) offered a complementary element to this vision: the emergence of some kind of emotion and, beyond that, of collective emotional states –that is, of emotions shared by the different people who feel that way– is the key element to the creation and permanence of online user communities over time.

Meanwhile, Min and Yun (2019) focused on anger and determined, in the field of political communication and in South Korea, that this emotion plays a fundamental role in social networks when it comes to promoting or intensifying the force of social mobilizations. These authors concluded that the emergence of negative emotions, especially anger, is a more determining factor when the number of participants in a social protest increases, much more than the specific object of that protest or other factors, such as the personal agenda or the greater or lesser availability of the participants.

From a more pure business perspective, trying to analyze the relationship between the company and customers in social media, Sashi (2012) defined four basic profiles in terms of emotional ties and relational exchanges: the transactional customer (a profile characterized by the low connection both in the emotional bonds that are established and in the relational exchanges that develop), the delighted customer (high emotional bond and low relational exchange), the loyal customer (low emotional bond and high relational exchange), and fans (both emotional ties and relational exchanges are high). All of this, in turn, has interesting applications in terms of engagement between the company and customers, which this author specifies in what he calls the engagement cycle, consisting of seven stages: connection, interaction, satisfaction, retention, commitment, advocacy, and engagement. According to these conclusions, knowing these four profiles and their reactions, the company’s strategy should be based on identifying its audiences and, above all, detecting the presence of fans, from which that emotional link will be created.

Coviello et al. (2014) delved into the analysis of positive emotions, especially happiness. Their study implies that, on the one hand, emotional contagion also works online, through social networks. In fact, the magnitude of this contagion is intensified. And, on the other hand, it can reach different parts of the world, that is, it can reach subjects who initially had not interacted with the protagonists of the beginning of that process. In this sense, these authors insist on the need to be cautious when extrapolating their incidence in smaller and more specific cities or geographical nuclei.

Considering the elements that are studied as possible influential agents in the generation of these reactions, Sano et al. (2019) analyzed the influence of the temporal element on the emergence of these collective emotions. This study is significant because it collected user reactions on social media in Japan throughout over 10 years (2006–2016). The conclusion is that there are specific periods that are repeated year after year and in which specific emotional states are generated. They are periods associated, on the one hand, with special dates, such as Christmas Eve and Day, New Year’s Eve, the beginning of the holidays, Thanksgiving Day or Valentine’s Day and, on the other hand, to specific but relevant events, especially catastrophes or natural phenomena that alter the development of daily life (such as earthquakes, typhoons, heavy snow, among others). Although the authors warned that their results would need a greater basis than only the reactions of users in social media, this study still suggests a possible future line of study.

Another of the trends detected in the literature is the analysis of socio-cultural differences, addressed by several authors ( Hudson et al., 2016 ; Lee and Hong, 2016 ; Ng and Kozlowski, 2018 ). In the first case, the authors conducted a study on the relations between brands and their users developed in the United States, the United Kingdom, and France, concluding that contextual differences may constitute one of the elements that decisively influence emotional reactions on the users. Ng and Kozlowski (2018) developed a similar analysis in Australia and Singapore, and concluded that there is a positive correlation between the development of positive emotional reactions and the feeling of well-being. At the same time, these authors detected that, on the contrary, there is no such relationship between the intensity of activity in social activities and the feeling of well-being. Lee and Hong (2016) approached these questions from the perspective of attitudinal beliefs and social influences, and concluded that the first element is more important in the user’s reaction than the second: the perception of what the rest think (the environment, society in general, etc.) influences, but not as much as previous beliefs, the development of empathy with the brand.

On the other hand, several authors ( Teixeira et al., 2012 ; Nelson-Field et al., 2013 ; Lewinski et al., 2014 ) focused on analyzing the effects of the videos included in messages when generating these reactions. The most significant thing about these studies is that the emergence of reactions, especially positive ones, such as happiness, joy or even surprise, influence other elements such recalling the video, the way in which it is retained and, consequently, the opinion that the user has about that message. However, Schreiner et al. (2019) , reviewing the literature that addresses these and other questions, concluded that more research is needed on this subject in order to reach results that can be considered more reliable.

Finally, another of the localized trends is the relationship between emotions, user reactions and engagement. Thus, the main conclusion reached by studies dealing with these issues is that the emotions developed by users also significantly influence the level of engagement that can be generated among those who publish messages on social networks (brands, politicians, private users, etc.) and the public that receives these messages ( Hollebeek and Chen, 2014 ; Ferrara and Yang, 2015 ; Hudson et al., 2015 ; Marbach et al., 2016 ; Dessart, 2017 ; Fan et al., 2018 ). In fact, Ferrara and Yang (2015) studied the transmission of emotions through Twitter, identifying two types of users: highly and scarcely susceptible to emotional contagion. These authors also determined that the former are much less predisposed than the latter to develop negative emotions. However, regarding positive emotions, no significant differences between each other were detected. Therefore, in general, the probability of developing positive feelings is greater.

Hudson et al. (2015) studied the emotional connection from the perspective of music festivals, with implications also for engagement between the organizers and the public. They developed a scale composed of ten categories: affectionate, friendly, loved, peaceful, passionate, delightful, captivated, connected, bored, and attached. The results show that social networks contribute significantly to the generation of these emotions, all of them positive in one way or another, which in turn leads to desired results in terms of word of mouth and engagement.

Dessart (2017) draws important conclusions for brands. The author analyzed 48 Facebook pages, which corresponded to nine different product categories, and concluded that the degree of emotional involvement by users toward the communities that are regularly created around a brand is one of the elements that determines the level of engagement, not only with that community, but also with the brand itself, toward which trust, commitment, and loyalty can be generated. According to this author, for a user, the fact that a brand responds to a comment on social networks would have, in emotional terms, a similar value, and would generate an equally positive feeling, to have an interaction with other members of the community. In short, engagement with a community can be considered as a precursor to engagement with the brand.

Marbach et al. (2016) analyzed the influence of personality traits and emotions on the development of engagement between brands and users. Specifically, the traits that can play some kind of influence are seven: introversion/extroversion, (dis)agreeableness, conscientiousness, openness to experience, need for activity, need for learning and altruism. It is a classification with very important implications in terms of user segmentation by brands.

As can be seen, in general, the studies that address these aspects do so taking into account positive emotions. However, there are also authors who have analyzed the impact of negative emotions ( Hollebeek and Chen, 2014 ; Fan et al., 2018 ). The conclusions of Hollebeek and Chen (2014) can be related to those previously mentioned by Dessart (2017) , who suggest that when it comes to generating engagement related to negative emotions, the connection with the brand plays a more relevant role than that which can be established with a user community. Fan et al. (2018) find that negative emotions are more easily transmitted in the case of user networks whose connection is weak, while positive feelings are more likely to be channeled in those other networks in which the ties are closer and more consolidated. Furthermore, negative feelings, and specifically anger, are more likely to become dominant when some public event occurs that also has negative connotations (attack, murder, etc.).

The analysis of the research reviewed throughout this article allow to draw a series of conclusions that demonstrate the effectiveness of neuromarketing as a tool for studying the relationships between companies and consumers in social networks:

1. Neuromarketing has shown that advertising content that directly appeals to emotions, mainly sadness, obtain better levels of effectiveness than those that try to convey a purely rational message, regardless of the medium ( Ambler et al., 2000 ; Baraybar-Fernández et al., 2017 ; Hafez, 2019 ; Harris et al., 2019 ).

2. Neuromarketing techniques indicate that the use of social networks occurs because users need to satisfy their social needs, to find validation and to make a good impression on their network, which encourages them to act largely based on the emotions and behaviors they observe in others ( Meshi et al., 2013 , 2015 ; Turel et al., 2018 ).

3. Engagement between companies and consumers improves on social networks when they post entertainment content. Business or corporate social responsibility posts do not drive user interaction ( de Vries et al., 2012 ; Cvijikj and Michahelles, 2013 ; Khan et al., 2016 ).

4. In times of corporate crisis, users consider the reputation of organizations more positive when they read positive and defensive comments than when they only read the news ( Hong and Cameron, 2017 ; Xu and Wu, 2017 ; Vignal Lambret and Barki, 2018 ), since they value the opinion of other users very much, more than that of the media. These data indicate that, during critical moments, organizations have to design strategies that generate engagement and encourage the most committed users and their stakeholders to come to their defense to preserve their reputation.

5. Messages on social networks that carry an emotional charge, both positive and negative, generate reactions on the users, so they present higher levels of virality ( Hollebeek and Chen, 2014 ; Marbach et al., 2016 ; Fan et al., 2018 ).

6. Another important element to assess the relationship between organizations and consumers is that studies show that when they interact with the user on social networks, for example, responding to a comment, the sentiment generated is just as positive as when the individual interacts with other members of community ( Dessart, 2017 ).

7. Facebook is by far the most analyzed social network in the most recent studies on neuromarketing applied to social networks. This social platform is the object of study in researches by Mauri et al. (2011) , de Vries et al. (2012) , Cvijikj and Michahelles (2013) , Goh et al. (2013) , Meshi et al. (2013) , Nelson-Field et al. (2013) , Coviello et al. (2014) , Hollebeek and Chen (2014) , Tandoc et al. (2014) , Aguirre et al. (2015) , Hudson et al. (2015) , Kim et al. (2015) , Lin and Utz (2015) , Khan et al. (2016) , Lee and Hong (2016) , Marbach et al. (2016) , Dessart (2017) , Hwong et al. (2017) , Kim and Yang (2017) , Schultz (2017) , Swani and Milne (2017) , Swani et al. (2017) , Wang et al. (2017) , Turel et al. (2018) , Vermeulen et al. (2018) , Vignal Lambret and Barki (2018) , Muñoz-Leiva et al. (2019) , Sion (2019) , and Smith and Seitz (2019) .

8. Business communication and personal communication are the most studied subject areas in neuromarketing applied to social networks. Business communication is presented as the main subject of research by Chmiel et al. (2011) , de Vries et al. (2012) , Teixeira et al. (2012) , Cvijikj and Michahelles (2013) , Goh et al. (2013) , Hollebeek and Chen (2014) , Hudson et al. (2015) , Hudson et al. (2016) , Kim et al. (2015) , Khan et al. (2016) , Marbach et al. (2016) , Dessart (2017) , Kim and Yang (2017) , Schultz (2017) , Swani and Milne (2017) , Swani et al. (2017) , Xu and Wu (2017) , Rout et al. (2018) , Vignal Lambret and Barki (2018) , and Moussa (2019) . Personal communication is analyzed in studies by Mauri et al. (2011) , Meshi et al. (2013) , Nelson-Field et al. (2013) , Brynielsson et al. (2014) , Coviello et al. (2014) , Tandoc et al. (2014) , Carrillo et al. (2015) , Ferrara and Yang (2015) , Lin and Utz (2015) , Lee and Hong (2016) , Wang et al. (2017) , Fan et al. (2018) , Ng and Kozlowski (2018) , Turel et al. (2018) , Vermeulen et al. (2018) , Barry et al. (2019) , Min and Yun (2019) , Nash et al. (2019) , Ranganathan and Tzacheva (2019) , and Sion (2019) .

These conclusions show the usefulness of neuromarketing as a tool to improve communication between companies and users on social networks, given its ability to determine what type of messages work best and what type of multimedia content they prefer. With this data, companies can optimize their communication strategies, avoid crises, and protect their reputation on social networks.

Likewise, this study has allowed to detect a series of future trends whose research would contribute to deepening the study of social networks through neuromarketing. In this sense, the following needs have been detected:

1. Studies that delve into the influence that social networks are exerting on the purchase decision of certain products and services. In this sense, it would be necessary to analyze how reading positive or negative comments influences the purchase of a certain product or service.

2. Deepening studies that allow analyzing the impact of social networks on behavior change or awareness of certain social problems through influencers.

3. When analyzing the reactions of users, research could delve into the effect of negative appreciations, since until now studies that focus on positive ones have predominated.

Author Contributions

NV worked in the introduction, research design, results, and discussion. JD-C and DR worked in the research design, results, and discussion. All authors contributed to the article and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Keywords : social media, emotions, neuromarketing, organizations, audiences

Citation: Vences NA, Díaz-Campo J and Rosales DFG (2020) Neuromarketing as an Emotional Connection Tool Between Organizations and Audiences in Social Networks. A Theoretical Review. Front. Psychol. 11:1787. doi: 10.3389/fpsyg.2020.01787

Received: 16 May 2020; Accepted: 29 June 2020; Published: 21 July 2020.

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*Correspondence: Natalia Abuín Vences, [email protected]

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• 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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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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15 Powerful Examples of Neuromarketing in Action

Table of Contents

Neuromarketing is taking the world by storm and has been utilized by almost every major company and university in some way or form. Despite such a widespread influence on the marketing world, many people do not know exactly what neuromarketing is, or how it can be used effectively. The following articles outline 15 fascinating examples of neuromarketing in action.

1. The Importance of Eye Gaze

It is old news that ads that include people are much more effective than those that do not. In particular, images and videos that include babies tend to attract longer and more focused attention from potential customers. Advertisers have long attempted to boost sales for baby products using close ups of adorable baby faces – with the help of eye tracking technology they have identified that this alone is not enough.

Researchers discovered that when the infant looks face on, viewers will be far more focused on the baby’s face to the detriment of focusing on the ad content. However, if the infant is directing its gaze at the product or text then the viewer will in fact focus on the advertising content.

Takeaway : As a result of such findings advertisers have now taken on board that although baby faces are popular among consumers, they also make sure that the baby is looking at what they want the consumer to buy. Read more about the study here .

2. Using Effective Packaging

We all know the feeling of being drawn to particularly striking or attractive packaging. Advertisers have always known that it’s not always what’s inside that counts, but neuroimaging has managed to take this to a whole new level . Brands such as Campbell’s and Frito-Lay have used neuroimaging to reimagine their packaging. In studies, customers were shown packaging with their responses recorded as positive, negative or neutral. In addition, they were interviewed extensively in relation to color, text and imagery.

This research revealed that customers had a negative response to shiny packaging, but didn’t show a negative response to packaging when it was matte. Frito-Lay then went on to scrap the shiny packaging, and move on with the new, matte look.

Takeaway : Neuromarketing techniques are being employed extensively to redesign packaging and presentation. To read more about the study above (and some other interesting studies) check out this link .

3. Color is Key

When selecting colors, bear in mind that you may be influencing how potential customers feel. Colors can evoke a wide range of emotions, with studies consistently showing a link between certain colors and certain emotions.

Utilizing a color effectively can be a powerful marketing tool. One of the most infamous examples is Coca Cola’s ubiquitous use of the color red,  but there are many more companies who have also used color to great effect . Neuromarketing experts specializing in color and advertising have divided colors into subgroups as a guide to how they may be used effectively. Cool blues, for example, are the go-to color if you wish to attract professionals.

Takeaway : Make sure to familiarize yourself with how color may be used to influence purchasing behavior .

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4. Ad Efficiency

For many years brain imaging was purely the reserve of the academic or the scientific. Neuromarketing, however, has tapped into the incredible potential of fMRI imaging to grant us insights into human behavior and consumer habits.

One example of how neuromarketing has made use of fMRI is to compare advertising campaigns before releasing them to the general public. In one particular study , three different ads for the National Cancer Institute’s telephone hotline were viewed by participants. The ad campaign that elicited the highest amount of brain activity in a particular region, led to significantly higher calls to the hotline. This novel approach is a new avenue for identifying ad campaigns that will genuinely engage the public.

Takeaway: fMRI has incredible potential for enhancing marketing strategies, increasing engagement and action.

5. Decision Paralysis

Sometimes, consumer behavior research goes against what we may have previously believed. A study by Columbia University revealed that too many choices may actually be a deterrent for potential customers. Using different types of setups, they found that displays containing a wide array of options were less likely to get customers to stop.

Takeaway: Less is more and sometimes customers can be overwhelmed by too many choices. Interested in learning more about decision paralysis and what to do about it? Take a look at this great article .

6. Evaluating Satisfaction

Emotion Response Analysis (ERA) uses EEG imaging to identify the emotional response an individual has to a product, advertisement etc.

Our level of engagement or emotional arousal in relation to a product is invaluable to the advertiser. If, for example, the consumer experiences high levels of frustration in response to your product then there is evidently an issue with usability you may wish to address. EEG may be used to evaluate consumer satisfaction. In one study EEG was used to evaluate satisfaction with a dermatological treatment. They found that customer satisfaction correlated with activation in the neural circuits involved in evaluating facial beauty.

Takeaway : Like fMRI, EEG can shed light on the most effective ways of advertising (amongst other uses). If you’re interested in how EEG can be used in conjunction with iMotions software then check out this link .

7. Loss Aversion

One interesting finding utilized by neuromarketing is that people really don’t want to lose out. People are just as worried about what they might lose as what they might gain. For this reason “buy before it’s gone” strategies are highly effective.

When the alternative option is posed as a loss, consumers are much more likely to buy . For this reason, a concept called “framing” is highly important in neuromarketing. This technique is how advertisers present decisions to consumers in a way that makes them more likely to splash the cash.

Takeaway: Consumers hate to feel they are missing out on a bargain, so make sure to emphasize if they are set to lose out.

8. Anchoring

The first piece of information your customer receives is highly important. It can be the basis for any subsequent decision-making and set the tone for their purchasing behavior . Neuroscientists have discovered a flaw in the workings of the mind, and how it reaches decisions. As individuals, we are rarely able to evaluate the value of something based on its intrinsic worth, but instead compare it with the surrounding options.

A valuable application of neuromarketing therefore, is to take advantage of this “anchoring effect”. If for example, you are looking at two hotel rooms which are priced similarly but one offers a free coffee in the morning, you are much more likely to go with the free coffee. You will more than likely not explore the quality of the rooms offered or any detailed features.

Advertisers often take advantage of this when comparing bundle packages or deals against each other. In this way, we may often find ourselves signing up to contracts or a year-long commitment.

Takeaway: Anchoring can help you swing the deal the right way.  This interesting piece highlights how anchoring methods can work for businesses.

9. The Need for Speed

Neuromarketing is useful for detecting customer trends. Whilst companies often seek to portray a sense of safety and security, speed and efficiency may be what customers are after. PayPal discovered this by conducting a study which found that the promise of convenience activated the brain more than security . They used this information to convert more shoppers to their online payment service by emphasizing their speedy payment system.

Takeaway: Whilst it may seem like emphasizing the safety and security of a product will win customers over, you may instead want to get the message across that your product is fast and efficient.

10. Revealing Hidden Responses

When testing a new advertisement, Cheetos used focus groups and EEG to evaluate consumer response .

In this particular ad, a woman played a prank on her friend by filling her white load of laundry with orange Cheetos. Focus Groups reported a dislike for the ad, however when an EEG study was ran with the same participants it revealed that they really liked it. Participants in the focus group were afraid to voice the fact they found the ad humorous in case other members thought they were unkind. In this manner, neuromarketing can reveal hidden thoughts and preferences.

Takeaway: Neuromarketing techniques can reveal hidden responses. To read about another interesting technique capable of illuminating our thought processes check out the IAT .

11. Reward and Punishment

Even video game design has started to use psychological principles in the product design process, specifically using reward and punishment in order to make engaging games, and to keep people playing them . By increasing the reward presented by the game, the action may also increase the levels of dopamine (a neurotransmitter) within the brain. This neurotransmitter is associated with pleasure and positive associations, which can increase the attachment to keep playing.

Game designers are now even hiring psychologists to help with game design, building psychological principles directly into the game mechanics.

Takeaway:  Create a pleasurable experience for consumers to keep them attached, and coming back, to the product.

12. Prototype Testing

Whilst advertisements are obviously vital to influencing consumer behavior, the design of products themselves can also be instrumental.

In a famous neuromarketing case, Hyundai used EEG to test their prototypes . They measured brain activity in response to different design features, and explored which kind of stimulation was most likely to result in buying.

The findings of this study led Hyundai to change the exterior design of the cars themselves.

Takeaway: The growth of neuromarketing has the capability to transform the world we live in.

13. Setting the Right Price

How to price products in a way that tempts consumers is a long-running and contentious question. We are all aware that pricing something at $9.99 instead of $10 is an advertising tactic, but does it work?

An array of new findings are shedding light on this age-old question . This fascinating new piece of information being used by neuromarketers, is that rounded figures are more likely to work alongside emotional decision-making, whilst more complex figures work better when the logical brain is engaged. This is because complex numbers make the brain work harder, perhaps convincing it that the complexly priced product is the more logical decision.

Takeaway: Take the neuromarketing approach to set your price.

14. Website Layout

Neuromarketing techniques are also being employed to inform how websites are designed.

From color schemes, layouts, font size and beyond, neuromarketers are delving into our website preferences. There are now some firm rules of thumb when it comes to creating websites. For example, using certifications, testimonials and social widgets are sure to draw customers in more than those that don’t .

Another interesting finding is that newer, horizontal style website layouts are less effective than traditionally vertical . This is because reading webpages from the top down engages the brain, and makes viewers more likely to keep on scrolling.

Takeaway: Use science to inform your website design. For 15 additional ways to engage web traffic take a look at this link .

15. Memorable Headlines

Headlines are one of the first things the viewer sees so obviously they need to stand out and be noticed.

As a result, they have been heavily researched, with a new neuromarketing technique called “Hippocampal Headlines” being coined. What does this mean? Researchers at University College London found that when a familiar phrase is slightly altered, our hippocampus is activated, and our attention is piqued. Many bloggers have used the example of Patron and their marketing slogan “Practice makes Patron” as an example of this.

Takeaway: If you surprise the brain your advertising campaign will be much more effective.

We hope you have enjoyed these examples. If you want to learn more about neuromarketing and to see how iMotions can elevate your neuromarketing research, please feel free to get in touch or download our guide below to see how eye tracking can help you uncover valuable insights.

For further reading Check out: 5 Marketing Myths Disproved by Neuromarketing

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A Questionnaire Survey and Case Analysis on the Combination of 4P, 4C and 4R Theory and Neuromarketing

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Neuromarketing

Applications of neuromarketing: amul case-study.

Reason Why You Are A Rookie In Applications Of Neuromarketing

Given the marketing competition today, Darwin might be somewhere up there smiling to himself, thinking, ” Survival of the Fittest applies to much more than just evolution! “

Application of Neuromarketing is the life jacket to survive the high tides of the marketing competition and make it to the consumer shore. 

Case Studies are to us, what stories are to kids. They make a concept lucid and interesting to understand. So, through the case study of Amul, we’re going to understand the concept and value of neuromarketing and some of its most important aspects. 

Why Amul?  

Out of more than 600 Dairy brands in India, almost every Indian consumer that goes to a shop to fetch some dairy products, directly asks for Amul, without any contemplation or second thoughts, despite several other products having more or less the same taste, quality, and price!

Amul owns 85% share in Butter market, 65% share in Cheese with Market Leader and a 63% share in infant milk. 

Amul has been awarded the Guinness World Record for the longest running advertising campaign. The Amul girl – a cartoon figure, is used by the company to promote their brand. Amul’s Utterly Butterly girl has managed to keep her fan following intact for more than half a century! 

Good news for Chandler Bing – researchers have now said that sarcasm in fact displays the highest form of intelligence. In fact, even according to research in the field of Neuromarketing, humour, sarcasm and wit in an advertisement increases ad and brand recall, specially among youths. 

As you must’ve noticed, Amul generally uses the current news scenario with a bit of sarcasm and wit for its promotion which usually connects with the audience well. 

Amul Milk adverts have the tagline “Amul Doodh Peeta Hai India (India drinks the Amul Milk)”. 

There are commercials that encourage the children to drink the milk and they say   “ Amul raises a glass to child power ”. Another one is about Women empowerment that says “Amul raises a glass to India’s Women power.” And then there is “ Aage Badta Hai India… Amul doodh peeta hai India ”.

Psychology says that human beings always like to be acknowledged and appreciated, which is something that Amul does through its ads. It makes people of all ages feel special and noticed, which helps the brand always be at the top of the minds of people! 

Amul started the tagline ‘Taste of India’ and taught Indians how butter could be used in our day to day life and that it’s not something that’s only a part of the English breakfast.

It created the requirements of its products. In this case, Amul also came up with the famous Amul song ‘ Sapne ho gaye hai sakar ’ where it showed how the brand works and connects all the milk producers in Gujarat. This ad further reinforced what Amul has done for India and hence became very popular among the Indians. After all, who wouldn’t like a brand that’s associated with pride and prosperity of his or her country? 

As we know, Indian culture and tradition is  one where family is given utmost importance, unlike American individualistic culture.

Hence, when the target audience is family-oriented, neuromarketing research says that advertisements involving families lay a great impact, especially those creating an emotional connection with the audience. 

Amul had launched the “ Har Ghar Amul Ghar ” campaign in January 2014 as a part of its social media strategy to reach out to a new generation of consumers in the digital media.

The ads are a line of films, beautiful and emotional stories centered around families, with funny twists. 

For instance:

“ Har Ghar Amul Ghar ” – Is a drama comic on parenting. 

“ Pehla Pyaar, Amul Pyaar ” – Is about how 2 teens fall in love. 

“ Har Bachpana Amul Bachpana ” – IIs on how grandparents are still like kids. 

“ Har Dosti Amul Dosti ” – Is on the friendship of 3 Old People. 

“ Har Umar Amul Umar ” – Is on neighbors of different ages (a kid and an old uncle) and how they get close.

Once again, applications of neuromarketing has come to play! 

Moreover, Amul, just like the findings of Neuromarketing, has successfully broken the popular myth that having celebrities endorse a brand leads to higher sales. 

Without having a public figure endorse it for millions, Amul ads and their concepts are captivating in themselves. The appeal of the ad lies in its simplicity. There is clarity in the message. In 2011, Amul was named the Most Trusted Brand in the Food and Beverages sector in The Brand Trust Report published by Trust Research Advisory on 18th January 2011. Amongst India’s top 20 brands, Amul has maintained to be in the top 5, even with such heavy marketing competition and clutter! 

Moral of the story: apply neuromarketing is the key to make your company/brand the Amul of your arena!  Reach out to us now !

Neuromarketing is the process of researching the brain patterns of consumers. Learn all about neuromarketing, and it’s common myths .

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