Has Neuroscience Solved Advertising? A Conversation with Dr. Christopher Demetrakos

After 13 years at Dentsu, Christopher Demetrakos left advertising to complete a PhD in cognitive neuroscience and consumer behavior. His conclusion: The advertising industry has spent decades optimizing for the wrong outcome.  

Advertising has a fundamental problem: People can love an advertisement and still not buy the product. The industry celebrates creative awards, memorable campaigns, and brand awareness, but none of these necessarily produces customers.

His work now combines neuroscience, cognitive traits, and marketing to identify what actually motivates people to act.

I recently spoke with Chris about why conventional advertising fails, the difference between liking and wanting, and whether artificial intelligence will make neuroscience more or less important in marketing.

Q: You say you've solved advertising. What exactly have you solved, and what does conventional marketing still get fundamentally wrong?

Christopher: The objective of marketing is to create customers. That sounds obvious, but it's not how the advertising industry actually operates.

At Dentsu, we were constantly experimenting with new technologies. We invested in early digital platforms, virtual worlds like Second Life, and emerging online businesses. We were hungry to discover what worked.

But the industry's fundamental problem remained unchanged. Advertising campaigns are typically approved through consensus. The client likes the campaign, the creative director likes it, the agency likes it, and everyone agrees it's wonderful. Then it runs.

If it fails, everyone can hide behind that consensus. Nobody takes responsibility because everyone agreed it was a good idea.

The industry rewards liking rather than performance.

My approach starts with a different question: What causes someone to become a customer?

Neuroscience helps answer that question. We need to understand the neural processes that produce action, rather than simply creating advertisements that people say they enjoy.

I didn't really solve anything. I connected some dots.

Q: You describe customer creation as a neurobiological process. What happens in the brain that turns someone from a non-customer into a customer?

Chris: The critical distinction is between liking and wanting.

These are different neurobiological processes. Liking involves pleasure, supported by neural systems that include opioid and endocannabinoid signaling in specialized regions called hedonic hotspots.

Wanting involves motivational systems, particularly dopamine pathways that help translate motivation into action.

These processes can occur together, but they don't have to.

You can like something without wanting to acquire it. Conversely, you can be motivated to obtain something without particularly enjoying it.

Advertising generally concentrates on liking. It creates entertaining content and assumes that positive reactions will eventually produce purchases.

But that connection is not automatic.

When a campaign is popular and produces substantial sales, both systems are working. But sometimes advertisements produce sales without receiving much praise.

Paul: I've demonstrated this using Immersion data. An advertisement that audiences dislike can produce a substantial increase in sales.

People are surprised because they assume their conscious evaluation of an advertisement reflects the underlying neural response that drives behavior.

It doesn't necessarily.

I also think the brain's tendency to conserve energy is central to your approach. When advertising aligns with how someone already processes information, it reduces the resources required to make a decision.

Chris: Exactly. We're trying to remove friction.

Think about persuading a lifelong Coca-Cola drinker to switch to Pepsi. That's not simply a product decision. You're challenging an established preference, perhaps even part of someone's identity.

Effective advertising aligns with existing cognitive characteristics so the new choice becomes natural rather than effortful.

Q: Neuromarketing is now a well-established field. What does your approach add that existing methods don't capture?

Chris: Much of neuromarketing adds neuroscience measurements to an existing marketing process without changing its underlying objective.

I call that sprinkling third-generation technology on second-generation marketing.

You might measure eye movements or attention, but if you're still optimizing for whether people like an advertisement, you haven't solved the fundamental problem.

The objective needs to be action.

My approach starts with the neurobiology of decision-making and works backward to identify the messages that activate the relevant processes.

We've identified approximately 400 cognitive traits that we use to understand audiences and construct resonant messages.

Instead of relying primarily on demographics or conventional psychographic categories, we focus on characteristics relevant to the decision itself.

Paul: So you've essentially reversed the traditional process. Rather than creating advertising and measuring how people respond, you start with the neural processes associated with action and design marketing around them.

Chris: That's right. The entire process needs to be built around creating customers.

Q: You spent 13 years at Dentsu before becoming a neuroscientist. What did neuroscience teach you that advertising couldn't?

Chris: It taught me that the advertising industry is operating with a fundamentally incomplete model of human behavior.

I compare it to the difference between a geocentric and heliocentric understanding of the solar system.

The industry keeps adding explanations to defend its existing assumptions rather than reconsidering those assumptions.

My turning point came during a campaign for HTC smartphones. We partnered with a Marvel film, Captain America, and created a campaign that everyone loved.

The commercials were popular. The client was happy. Every conventional measure indicated success.

But sales didn't move.

That was an extraordinary experience. We had done everything the industry told us to do, and the result was essentially zero.

I wanted to understand why.

Eventually, I entered a cognitive neuroscience PhD program. Even there, I encountered approaches that measured attention and visual responses without necessarily explaining purchasing behavior.

The key question remained: What actually produces action?

When I returned to the advertising industry with this knowledge, the reception wasn't particularly warm.

People didn't necessarily want to hear that their existing model was wrong.

That's part of why I started my own company. I wanted to build a different approach rather than keep defending the old one.

Q: AI can generate and optimize thousands of messages almost instantly. Does that make neuroscience more important, or will AI eventually make neuroscientists unnecessary?

Chris: AI is already discovering patterns in advertising performance.

It can generate messages, distribute them through advertising platforms, examine the results, and identify combinations associated with better outcomes.

But identifying a pattern isn't the same as understanding why it works.

Human decision-making isn't a spreadsheet.

We need humans to help define human problems and understand the cognitive characteristics that determine behavior.

AI can become extraordinarily effective at personalization. Imagine an advertisement assembled in real time around your particular cognitive characteristics, using language and messages that align with how you process information.

You encounter it and immediately recognize that the product is relevant to you.

That's where this is heading.

But the underlying understanding of human behavior remains essential. AI needs an objective and a meaningful model of the person it's trying to reach.

The opportunity is to combine the computational power of AI with neuroscience rather than treating them as substitutes.

The future of advertising is behavioral

Chris's journey from advertising executive to neuroscientist illustrates a broader problem in business: Organizations often optimize what is easy to measure rather than what actually matters.

Advertising awards, favorable surveys, and brand awareness are convenient metrics. Purchases are the outcome businesses ultimately need.

Neuroscience offers a different starting point by identifying the biological processes associated with decisions and actions. Combined with AI, this approach creates an opportunity to personalize communication around what people actually value.

The challenge for the advertising industry is not producing more content. AI has already made that easy.

The challenge is measuring why content causes people to act.

FAQs

1. What's the difference between liking an advertisement and wanting to buy a product?

Liking and wanting involve different neurobiological processes. Liking relates to pleasure and enjoyment, while wanting involves motivational systems that help drive action. Someone may enjoy an advertisement without feeling motivated to purchase the product, which is why positive reactions alone don't guarantee sales.

2. Why aren't traditional advertising metrics enough to predict sales?

Traditional metrics such as brand awareness, attention, and ad favorability measure aspects of how people respond to advertising, but they don't necessarily reveal whether someone will act. An advertisement can perform well on these measures without generating purchases. Measuring effectiveness requires understanding the processes that influence actual decisions and behavior.

3. How is neuroscience changing advertising effectiveness?

Neuroscience provides a way to study the processes underlying motivation, decision-making, and action. Rather than focusing exclusively on demographics, attention, or whether people enjoy an advertisement, neuroscience-informed approaches can help marketers better understand what motivates people to become customers.

4. Will AI replace neuroscience in advertising?

Not necessarily. AI can generate, test, and personalize advertising at scale, identifying patterns associated with better performance. However, identifying patterns doesn't necessarily explain why people respond as they do. Combining AI with neuroscience offers an opportunity to create more relevant messages grounded in an understanding of human behavior.