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Bo Li is currently a Senior Behavioral Scientist at Dell Technologies where he leverages behavioral science to create better customer experiences for consumers who shop for Dell products online. Previously, Bo was a Behavioral Economist at Eli Lilly & Company where Bo conducted behavioral science research to help patients be more adherent to medication. Bo started his career as an Assistant Professor of Marketing at University of Louisville. Bo obtained his Ph.D. from the University of Washington in Seattle in Marketing. His work has appeared in the Journal of Consumer Research, Journal of Consumer Psychology, Journal of Retailing, Marketing Letters, and several other premier business journals and conference proceedings. Bo’s research interest lies in the intersection of emotions, persuasion, and behavioral change.
Transcript (Semi-automated, forgive typos!)
John: Bo, welcome to the show.
Bo: Thank you so much, John. Thank you for having me. I'm excited.
John: It's pleasure. Yeah, and for people, right now Bo is in China, I'm in America. We are 12 hours apart and we are on the opposite side of the planet. And amazingly, we're still having this conversation.
John: Really amazing world we live in. Alright, Bo, so for people who don't know, you haven't had a chance to meet you, maybe a good place to start would just be for you to talk about your background and how it is that you're really in a field which I think is very related to the work that we do in sensory and consumer science. So how did you get to be where you are?
Bo: Yeah. I'm in the field called behavioral science which is a collective discipline of psychology, marketing, neuroscience, health sciences, all these things. And I originally started out as an E-con major. I went to Tech, Virginia Tech for my economic Ph.D. and midway I start to realize that this is not how I see the world. When the professor was talking about some models and I was thinking, why is an emotion there? And while I was asking questions, I realize that may not be a very smart question to ask, but the professor was like, that's an interesting question. We just don't really incorporate that into our models. I see the paradigm differences. Then I think that point I started to look outward to see maybe there is a different discipline that I would enjoy a little more and how I got into marketing and specifically, behavioral science or consumer psychology at the point.
John: Okay. So you were in academia for a while, and at that point, you were studying kind of pure marketing? What was your emphasis then during your academic career?
Bo: It was actually more psychology than marketing. You know, in the graduate programs, we do a lot of research on how people make decisions. Not necessarily in marketing specific context, how to sell people things they don't need. I think that's one of the misconceptions about marketing. A lot of times, the research we do is to help people make better decisions. They may or may not realize they have a need. We help them. We're here to help them kind of review their need and fulfill the need. And some of the topics will work on are related to social behaviors. How can we nudge people to do things that are good for the greater good? So marketing is one side of it. But we also just focus on behaviors in general.
John: That's interesting. Actually, I have a question about nudges. I was thinking about which is, are there any examples of where there was a concerted effort to nudge people towards doing something? And after some amount of time, it was realized that was a mistake. That maybe that wasn't a good idea to get everybody to do. Do you know any examples? We can just move on if you don't. But I just wonder, like, for example, the thing that comes to mind for me would be low-fat diets, right? There was a big push toward low-fat diets. Everyone thought that was a good idea. It turned out, actually to be a very bad idea. Another one might be butter where there was this idea of butter are bad, margarine's good, big push. We made a mistake. So is this something that gets really discussed in the nudging community about whether or not, the thing we're nudging people to do is, in fact, actually a good idea, or is it pretty much consensus on? I mean, I think smoking is clearly bad for you. Some nudging people away from that is probably good. But is that something that's discussed?
Bo: I think the community is more concerned about how to nudge people in terms of what's nudge has less of focus, absolutely clear idea of what to nudge.
John: Right. Now, that's interesting. Yeah. I feel that way, too. You know with the AI is like, okay. We work on developing products that are useful. We work with companies to develop products and people want and we'll buy or whatever. I don't know, maybe it's a little bit of a cop-out for me to say, well, it's up to them what they want to buy. Okay, let's get back on topic. So then you went on to Eli Lilly. Could you tell us a little bit more about that job? I mean, was that working directly with hospitals, or what does that look like on your kind of day-to-day work?
Bo: I was actually an internal consultant to support market teams. So, one of their biggest challenges is that patients don't take medication as directed. And that has been the problem since the 50s. So, I was there to help them uncover the drivers of these decisions. What are those emotions, potentially or motivations that prevent people from taking medications? So it's more about helping the marketing team to maybe refire marketing messages or a lot of times really just educational materials, even though we call them marketing in general.
John: Right. And do people typically state that they want to take their medications but then they don't or what?
John: So they say yes, I want to do this but then they just don't?
Bo: Yeah. I mean, I even call myself not taking vitamin C. You know the different determination to yourself.
John: Yeah, that's interesting. Yeah. We use smart speakers to remind us that's how we do it here in our house.
Bo: That's a nudge right there.
John: It is a nudge and it works. It definitely works. So, it's interesting. So then let's move on then to your work at Dell. You're telling you before the show that's kind of related to UX, but not exactly UX. So how is it? And I would say, UX and sensory are kinds of I think they're kind of sister disciplines in some sense because sensory to a large extent is focused on the chemical senses. You know, it is mainly taste and smell. A little bit of touch, a little bit of sound. But the focus is chemical senses and I think UX, sensory and focus on sight and sound. Right? The senses are more relevant to communication technology. So, could you tell us then a little bit about what you're doing, what exactly is UX?
Bo: I'm actually under the design org but I'm not exactly a UX researcher.
John: I should say where is it? We are into the present now.
Bo: We call ourselves behavioral scientists. So we do solve UX-related problems. For example, how can we create insight to inform better website design? So the products that work with are, let's say, cart checkout or home page or search functions. These are considered products within our org. My contribution is to look at past data, whether it's interviews or AB testing to figure out what kind of information that consumers find important to make decisions so that we can provide them with better information or help them with their decision making just in general versus the UX, I think a lot of the efforts are directed to making things easier. To read, easier to interpret but if you don't have the right information for customers, no matter how easy it is.
John: That is interesting. So UX as you're saying is kind of more focused on design, right? That it's more about the design interface. Maybe industrial design creating something that can be used by somebody. But you're trying to get a little bit deeper more into psychology. You're trying to understand what's going on when people are making decisions and how can you help them to make decisions as opposed to.
John: That's actually very related to consumer science. So what are some of the big lessons? That is quite interesting, actually. Maybe there's an analogy here. Because we have sensory and consumer science. Sensory is very design-oriented, I think. Yeah. So that's interesting. Okay, what are some of the tools then that you are using on a regular basis? Is it very data-driven? Or is it more, you are focused on the individual and doing deeper dives? I mean, what sort of information goes into your work?
Bo: I look at data coming from user room testing or user testing. Sometimes surveys like qual tracks and AB testing which is what our engineers and designers are helping us do in terms of analytical tools, I would say, R. I don't pull the data itself from our database but especially for survey kind of data, we use R to analyze it. Nowadays, I focus more on the front end of things, how to interpret data, how to generate hypotheses. Once the data come back, how do we interpret the data to see whether it confirms or refuses the hypothesis?
John: Right. So are you designing research also?
John: I see, so you design experiments and then there'll be, you work with the data scientists maybe.
John: So what are some of the keys, in sensory and consumer science we tend to be very interested in liking. Liking is a metric that for whatever reason, people are interested in. What are some of the key outcomes you're interested in? Like when you're trying to, if there was something you were trying to predict for your users, what will be some of the key targets that you focus on?
Bo: You know in tech, they focus on conversion and click-through rate. Why I said "they" because I'm very new to tech. Traditionally in my academic world and also a little bit Eli Lilly, we look at a lot of psychological constructs, like a brand connection, brand loyalty, and here we started looking at that as well to understand what was preceding the conversion of click-through rate. So, liking would definitely be an important predictor of people's behaviors and attitudes. That would be one example of an attitude. But there are instances where the decision is not really driven by attitude. You know, people may act towards something without knowing they have a positive attitude toward it. Something very automatic, right? If we brush our teeth every day, not because we think that's a positive behavior or we have a positive attitude, It's just because we do that automatically.
Bo: So that's one thing the marketers are trying to do or just, in general, is to encourage this automatic behavior toward an outcome. We are neutral about the outcome at this point just from the other side's perspective, how can we not cultivate that kind of automatic response?
John: Yeah, that's interesting. Well, I think about brushing my teeth in a larger sense, if I were to think about going to bed without my teeth brushed, I would feel uncomfortable. I have to do it, right? I wouldn't say I want to do it. I have to do it. And so I suppose that's the extreme form of automatic behavior. It's inconceivable. So then you're working with someone who's laying out a website. I suppose maybe you're working with UX or you're working with a designer. Then are you looking for a kind of a natural flow? Are you more focused on the actual purchase decision, or are you more thinking about how do we help somebody to find the product they're looking for identify their needs? Are you more on the kind of exploration side, or are you more on the final decision to buy?
Bo: Yeah. That's a good question. I think it will be a little bit both, even though now is somewhere on the former. So if customers are able to differentiate the product, all the product options were presented at dell. Then if you're more confident in putting things in the cart and then go ahead and check out.
John: Right, that's a good point.
Bo: In a situation, if things are done correctly.
John: Right. There's a class of examples. Steve Jobs At one point, I think when he came back to Apple, they had many, many products. Nobody could tell the difference between the many products. And he went to a whiteboard and he said, I think it was something like industrial versus personal. And then it was like big or small or something. They decide they're going to make four products. And it was going to be very clear with the four quadrants. There were all four products to buy when he first came to buy and that you could say, all right. Yeah. So that was an extreme version of clarity. I suppose that might have been oversimplification because who's to say those are the relevant dimensions. But I can definitely relate to that. You know, Amazon, it's a total mystery what the difference is between these computers. And so I feel very uncomfortable with your computer. So I can definitely see that as a benefit. Now, what are some of the lessons from kind of behavioral science, behavioral economics that you think that consumer researchers would benefit from knowing? The kind of the key things you've learned over the years that you feel like have helped you to understand people when they're making choices.
Bo: I think from both theory and methods angles. So when it comes to theory, theory really just it sounds like a big word, but really just a collection of past research, past data, and they try to make sense try to develop a framework of it. And I really enjoy theory work, even though I'm not in academia more. I just find that theories provide a lot of clarity. Help us reduce a lot of inefficiencies. So we don't have to go out and do the studies on our own. So kind of theory I think is relevant is information processing theory. It's a whole bunch of theories that put together its information processing, you know how people process information. We don't always process things in a piecemeal format, but we look at it holistically a lot of times or pick up cues they're most salient or relevant to us. So how can we intervene in that process so that direct consumers to the right path? I mean, there really is the right one, but to the path that will save the most energy and effort, we don't necessarily embark on something that takes a lot of effort, a lot of burden. And also we try to create a better experience in terms of making them feel connected, needed, belonged, unique, and All these good things that we, as human beings want. We want these things from our interaction with human beings. Why don't we create that same kind of experience where they interact with computers or products?
Bo: Yeah. So that's about a theory. I think from methods, the behavior science field has created a whole lot of measures. A lot of kind of scale types of measures have been validated over the years, and they're actually also very specific because when people say about measures that say, it's just people's self-reported. So it's not really meaningful, but actually, for something like, hey, loyalty or sense of uniqueness, we cannot really pinpoint behavior that represents that kind of sentiment. We got to measure these things, right? So we need to develop these measures and we need to refine them. I think that could be valuable because what we're looking at is an outcome. Whatever that might be purchase-related or through sensory. But there's got to be an intermediary level kind of variable or process, and that's something that the behavioral science field can help us identify and measure.
John: Yeah. Okay. That is fascinating. There are a lot of things to say in response to that. I mean, I think, for one thing, try to think about many lessons that can be learned from that for our audience. I think that we don't do enough time in sensory. We don't spend enough time in sensory and consumer science thinking about that intermediate layer. I think, okay, there are light changes in the purchase decision. But for example, what will cause somebody to tell their friends about a brand. About you know try some drink at a store and okay, you like it. That's one thing. But another thing if you're going to become a brand ambassador, right? And so what is going to cause you to try to what's going to motivate you to talk to your friends about it, right? And I think these are more detailed, nuanced questions that I think would really benefit sensory and consumer scientists, Have we thought about that more? So I think that's valuable. I also think your idea is about clarity. As far as you know, what does this product do? That's I think very importantly and we've got all of these brands. Think about going to the grocery store, right? You go to the juice style. There might be two or 300 juices right there on the shelf. Can a juice really differentiates he can explain clearly what it does? What is it that, so what's the benefit of this juice and that makes it different and even within the line, what's the difference between one flavor of something like you've got several juices in a product line, What's the difference between the juices and the product line? These are helping people to get to process information more efficiently so that they can make a decision, helping people get to a decision. Yeah. I think that's definitely those are very important ideas.
Bo: I think you touched on a great point, John, about why people are making the decisions. Once we know the why, we don't necessarily have to stay on the surface level variables or a certain design. But we can change something about the why. That's the beauty about the causal kind of inference. I think a lot of Sciences are using the causal kind of framework to sciences and behavioral science is definitely one of it.
John: Yeah. So that's definitely good well, you definitely give me to think deeper, to go deeper. So I think that's definitely valuable. So we actually are almost out of time. I want to make sure that we get what you're excited about for the future. I mean, you're in tech base, you've got the background in kind of behavioral economics or behavioral science. So when you look kind of outward, what are the things you're excited about for the next few years? Are there other technologies you're interested in or the new ideas, what are the things that you're looking forward to using in your own work?
Bo: Yeah, I love to see more collaboration between behavioral scientists and machine learning or AI scientists. A lot of times when we look at data, I try to interpret it. It is a manual process. When customers say something, I try to pull the information that I know about users about their psychology to match what the users tell me to create a hypothesis or interpretation. That's essential to the mental process that's going on right now. But I think we can automate it. If a machine can learn the kind of framework that I use or maybe not the entire of it or maybe some aspect of it. For example, every time there's a mention of this kind of work that's going to be an indication of certain emotion that's going on then we can build a library and then and also once we can connect that to behavior, the machine can learn the connections and it can speed recommendations automatically. So I think that can really speed up a lot of the product development process and create really innovative products.
John: Right. Well, you and I are speaking the same language there for sure because I'm interested in the exact thing in the consumer science work. I think our data are a little bit less clean. I think that if you've got data from certain websites, AB testing, etc. I think you're going to have large amounts of data that are probably easier to use for machine learning. But they're kind of ready to go but yeah, that's definitely good. Okay, so the last question we always ask is advice for young researchers or young sensory scientists, young marketers, what advice would you have for somebody who could be the 30-year-old version of yourself. You're not that old, much younger than 30, but 27- year-old version of yourself?
Bo: Yes. If I could go back in time, I would read more broadly to learn how other fields look at the same problems. What are their lenses? What are the things that I could contribute or things I can learn from them? Because behavioral science best self is really just a multidisciplinary subject. In a way, we take stuff from all over. So that's just a history of it. So I think as a researcher is good to follow that tradition and just read very broadly and to build very multi-facet skill sets.
John: Right. So go abroad so that you can go deep in your work.
Bo: Exactly. Right.
John: That speaks to me for sure. I mean, I've done maybe 150 great courses. Do you ever do the great courses? You ever done this? Do you know those?
Bo: I have suffered after grad school.
John: I don't believe that. I used to always do courses in the car whenever I was in the car and so that's how I learned information theory. That's how learn game theory. That's how I learned that said a lot of history, too. You learn a lot about the world doing history, you know. But I think it's definitely good. I read every night. It's very, very important to keep going.
Bo: It's all about time management, right?
John: Well, one of the keys is to not be on Twitter. I spent three days on Twitter over the weekend. I got totally depressed so I'm off Twitter for now.
Bo: Yeah, I saw your LinkedIn post. I can relate to that.
John: I just have the wrong personality for it I think. Alright, Bo, this has been great. So anything else? How can people get in touch with you if they want to follow up with you? If they got some questions.
Bo: I am on, I was going to say Twitter, but I don't post very much on Twitter but I do have an upcoming podcast called CB Talk China. Me and another friend who's Australian but spent most of his career and adulthood in China and I bringing the behavioral science perspective. We collaborate to understand how can we impact the new phenomenon, new interesting frontiers in China using behavioral science, behavioral economics. So if you guys can give us a follow on CB Talk China, that'd be awesome.
John: Okay, good And we'll make sure that we get that link into the show notes and then on LinkedIn? Could they reach out to you on LinkedIn?
Bo: Yes. Absolutely. Bo Li on LinkedIn.
John: Alright And we'll put that also into the show notes. So, Bo, this has been a pleasure so thank you so much.
Bo: Absolutely. It's my pleasure as well. Thank you so much, John.
John: Okay, that's it. I hope you enjoyed this conversation. If you did, please help us grow our audience by telling your friend about AigoraCast and leaving us a positive review on iTunes. Thanks.
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