Ming Hsu is an associate professor at UC Berkeley, both in the acclaimed Haas School of Business and the Helen Wills Neuroscience Institute. His current research focuses on neuroeconomics and neuromarketing. In combining a new biological approach to consumer behavior theory, his research provides insight into previously abstract thoughts and feelings towards decision making. Currently, in addition to papers in progress, he is involved with Marketing for the Center of Equity, Gender, and Leadership at Haas. One afternoon, I was lucky enough to interview him about cross-departmental research, applications of cognition, and Mcdonald’s.  

I stumbled across your research in neuromarketing at Berkeley, and found it really interesting. Could you start off with: what inspired your interest in neuromarketing? Before coming to Berkeley, did your research skew more towards economics, business, or neuroscience? 

Yeah, that’s a great question. To answer your first question, I came to neuromarketing through economics and neuroscience. When I was an undergrad, I was actually working in the neuroscience lab, but took economics classes. There was a research presentation that I still remember very clearly, by a group of economists who turned out to be actually quite well known. But at the time, it was obvious that they didn’t really know anything about the brain. As an undergrad, it’s not often that you can say “Oh, I know a lot more about this topic than the professors do.” So I started talking to them about it. It was one of the first studies looking at people’s decisions in a brain scanner, so they could see activities and what brain regions were involved. It turned out that, you know, they really needed to add people who knew more about the brain than they did. 

I thought, at first, the answer would have been obvious. It turned out that basically, no one was studying neuroscience and economics. And it seemed like a great opportunity when I applied to graduate school for an Economics program, through connections from that particular lab. So it was very serendipitous for me to get in from the beginning of this particular field. And so from there, how I got into marketing was that I was working in neuroeconomics. At the time, I was at the University of Illinois, and Berkeley was looking for a Berkeley Marketing Group. At the time, I didn’t really know much about marketing. I knew what marketing roughly did, but I had no idea that marketers would be interested in my research. But, you know, they reached out through academic networks. They asked, “oh, would you be interested in the company marketing department?” I didn’t see myself living in Urbana Champaign, the Midwest for the rest of my life. So I said “sure, sounds good. Here I come.” Half of career changes are intentional. Half of it is serendipity. 

While you didn’t originally expect to work in neuromarketing, does it combine many of your interests?

I definitely saw myself working on questions related to neuromarketing, because in many ways, marketing is not so different from economics. The foundational elements of what motivates everything in economics and marketing are really the same thing: how consumers make decisions. So it doesn’t really matter if you’re talking about how you make a choice for, say, your cell phone plan, or stocks – it’s the consumer choices that kind of drive the world. So, they clearly have relevance to each other. I study choices, but I think it’s also a matter of [consumer] tastes as well. Compared to tastes, the choices I study are less abstract. When you see economic models, oftentimes, it’s very stylized and mathematically motivated. Whereas for marketing, because of the real world, relevance is more important. For example, we might ask people to choose a fast food or beverage brand or rather than choose these artificial items that you’ve never heard of. So it’s more of a change in degree than kind.

I see how your work kind of focuses on being more applicable to the real world. What are some ways you take these scientific findings, and then communicate them to business owners outside of academics?

Most in a university setting are doing basic research, as opposed to applied research. So even though we try to be more relevant, you can almost think of engineering research, where we’re not mass producing stuff for the consumer market, but to try and understand basic principles. It’s not necessarily something we can do to say, “Okay, you have to do X” to a manager right? But what we do is just make knowledge more concrete. For example, one of our more recent studies is about how to  think about and measure the value of being “top of mind”.  If you’re a brand, that’s something that’s relevant to probably every company. But the way that “top of mind” is important for each company is pretty different. So we try to come up with some basic principles. One of the things that is built from economics is that, in your standard economic models, you’re the person making the decisions. People sometimes call this “homo economicus”. “Homo economicus” doesn’t have any memory constraints, so being “top of mind” probably isn’t going to be very important. But people do have memory constraints. Suppose I asked you to make a decision about where to go for lunch? What’s a popular cuisine around campus? What restaurants are most popular for it?

I guess Thai food is popular right now. Imm Thai is the first I thought of. 

Okay, Thai. Now, suppose I give you a menu of all Thai restaurants in Berkeley. You might want to pick another one instead of something popular on Yelp, Imm Thai. What’s another one that’s not as well known, a hidden gem?

Many people haven’t gone to Little Plearn before. 

Let’s just imagine you like Little Plearn better than Imm Thai. But you forgot about Little Plearn at that particular moment when I asked you because for Imm Thai, maybe like everyone talks about it more. Right? So that’s a case where you actually prefer something that you didn’t choose. You didn’t choose it because of the memory constraint.That is a very commonplace type of decision there. That hints at how ubiquitous and how powerful being “top of mind” might be in terms of our choices. We look at these for a lot of categories. An economist might ask people to rank some fast food chains in terms of preference. McDonald’s is actually not particularly high, but everyone knows about McDonald’s. Most people don’t particularly like McDonald’s. But everyone remembers. In our experiment as well, if you looked at people’s stated preferences, when they have everything on a piece of paper, McDonald’s is middling in the pack. So, what’s the “top of mind” value for McDonald’s? It’s not like people are suddenly going to like McDonald’s. But once you tell them “Oh, this is a McDonald’s burger”, they see it as a Mcdonald’s branded burger. When asked to pick a fast food without being given the menu, the share of people who choose McDonald’s skyrockets, in fact, it just about doubles. It goes from 15%, to about 30%. 

So that’s kind of a really simple idea for the power of “top of mind”. You can trace this to both the cognitive aspect, which is memory, like people with memory deficits and memory errors, and also the cognitive neuroscience of memory, which are the certain brain regions responsible for memory retrieval and encoding. When you use it, you need to kind of combine different sorts of information to make your decision. So different parts that are kind of involved in allowing you to combine the retrieval aspect to your preference information with memory. You students are probably not going to have this problem very much. But, your grandparents probably have worse memories than you do. When people get old enough, they’re going to develop neurodegenerative disorders. So, what happens when the hardware starts breaking down? It’s going to show up in people’s poor financial decisions, and just poor decision making in general. That’s one of the biggest motivations for understanding how, as people are getting older, how consumers become vulnerable to different types of decision making errors. 

At the same time for managers on the other side, you can give marketers a better sense of the value of having a brand. There’s a lot of emphasis on short term financial returns. In digital marketing especially, you can measure certain things very well. You can measure kinds of price effects. If I can manipulate prices to incredible levels of granularity that I was never able to do, I can manipulate people’s attention very quickly. But memory works on a much longer timescale. 

Let’s go back to McDonald’s. Suppose you don’t run a single ad for the rest of the decade. I, after a year, people are still gonna think of McDonald’s as the most “top of mind”. By year 10, certainly, if you don’t do anything, people are gonna forget McDonald’s and it will no longer be “top of mind”. At that time, you can’t just turn “top of mind” on again. It’s not like pricing, where you can turn on and off the spigot very quickly. Once it’s gone, you might not be able to build it up ever again. So managing and cultivating the brand has always been a very big concern for traditional marketers. These days, there has been increasingly less emphasis, in part because it’s really hard to measure the importance of Top of Mind, and its financial impact.

It sounds like there’s a lot of cross departmental collaboration within your research. So when you’re actually doing the studies, you have people gathering the data, people processing the data, and then analyzing it. What are some skills you’ve learned that are important when it comes to working cross departmentally?

I actually did an interview with a very senior, very well known economist, and I asked him exactly the same thing. Hah, the table has turned. It’s going to differ depending on the people around you. Starting out, you want to develop a core expertise in some area, whether it’s neuroscience, whether it’s economics, or whether it’s data science. You want to have a core competency. Because there’s so much to read, and there’s so much to know, you need to really develop common sense for evaluating prioritization. What are the foundational knowledge and details you need to know about other fields, who can you ask about them?

 The 80/20 rule applies very well. Most of what you need is in that 20%. You can also ask people, but you also need to do your own due diligence, because what I consider to be foundational, could be completely off. We’re all sort of brainwashed in our own disciplines, just thinking, “Oh, this is what defines economics, right?” Thinking back, what most consider foundational economics are not necessarily emblematic for other fields. 

You can ask people in a different field, “what are the most important questions that your field is trying to solve?” What are the aspirational questions? That really forces people to move away from technical and narrow questions, which we spend a lot of time thinking about, but to zoom out and think about the big picture. One of the most important things for interdisciplinary research is that you need to have people from different areas to have the same goals. And it’s easier said than done, because the goals sometimes are pretty vague, and you prioritize different things. So this forces people to put their cards on the table and say, “okay, I think this is important”. Then, everyone can make an informed decision in terms of: How much do I care about this? Are there some ways that we can meet in the middle?

Not necessarily a compromise, but think of it as a selection problem. There are so many different ways to look at a particular goal. All you need is to pick one that everyone is excited about. That sometimes takes a long time, but once you find it, it’s great because everyone’s incentives are aligned. And everyone’s expertise. Everyone can also see where their own expertise fits into this broader picture.

The most common way interdisciplinary research fails is that people don’t speak exactly the same language. They aren’t necessarily specific about the subtle assumptions of their field.

Speaking of cooperation in research, are you working on anything right now?

For example, as we were talking about, we were working on a project to understand how consumers make decisions when they become vulnerable to different types of decision making errors. For example, for people with Alzheimer’s, their memory deficits influence their decisions, and offer a drag on their decision making capacity, which is a big deal as the world gets older. 

Another direction, which is of interest to undergrads and the Berkeley community is how we can think about diversity, equity, and inclusion in a more scientific way. Today’s universities tend to be very advocacy focused, which I think is totally fine. But what’s oftentimes under overlooked is a science behind diversity, and what it means to be diverse. What are the ways in which stereotypes and prejudices manifest? Currently, you have African American Studies studying one type of prejudice, and you have Latinx Studies, studying another one, Women’s studies a different type, etcetera. Economists study it, sociologists study it, psychologists study it, and they all study from very different directions and don’t connect to each other very much.

So part of what we try to do is thinking about this through very basic human cognitive processes. We think about how the way we perceive the world gives rise to all the different types of stereotypes and prejudice, and discrimination that you see in the world. Taking this, we are trying to establish some kind of a scientific foundation for a lot of the debates out there. It’s obviously much bigger than what we do, it’s one lab. But it’s something that is really missing these days, because it tends to get very emotional for obvious reasons. While it’s focused on what’s right and what’s wrong, you need a baseline for what’s the basic principles underlying everything. It’s like if you’re trying to treat a disease. It’s not just a matter of making disease go away. You also need to understand all the basic substrates of that disease. 

Featured Image Source: Berkeley Haas Faculty

Disclaimer: The views published in this journal are those of the individual authors or speakers and do not necessarily reflect the position or policy of Berkeley Economic Review staff, the Undergraduate Economics Association, the UC Berkeley Economics Department and faculty,  or the University of California, Berkeley in general.

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