Episode Transcript
[00:00:00] Speaker A: Foreign.
[00:00:10] Speaker B: Welcome to Hacking Kaizen. I'm Graham Newman. On this week's program we're Talking to David McCann, a Bangkok based business strategist and behavioral design expert with extensive experience in marketing, product design and culture change programs across high growth Asian markets. Also, David is co founder of the Make It Toolkit, a gamified framework that integrates behavioral science into product and service design to effectively influence user behavior. The practice of behavioral science focuses on understanding and influencing human actions. It's a critical area of study today as individuals and organizations navigate complex decisions, adopt new habits and adapt to rapidly evolving environments. We'll examine how behavioral science drives impactful outcomes in decision making, particularly by addressing emotional conflicts and barriers to change. For example, how can it help people overcome frustrations or guilt when change attempts fail? We'll also discuss cultural and geographic influences on the effectiveness of behavioral interventions from product design to workplace habits. And we'll explore the intersection of behavioral science and generative AI. How can AI tools be designed to nurture curiosity, trust and user empowerment or not? Will tackle ethical considerations including the delicate balance between influence and manipulation and how AI reshapes perceptions of trust and authenticity in human machine interactions. This discussion offers valuable insights for leaders navigating today's challenges. Stay tuned for everything you need to know about behavioral science.
[00:01:57] Speaker A: Let's start by summarising for a non specialist audience. What is the practice of studying behavior and why is it so important right now?
[00:02:06] Speaker C: So the practice of behavior practice of studying it is really this is part of our mission statement of embedding behavioral science into products and services. And the reason that I came to behavioral science was so much of what we do is digital and we still have caveman brains. We over consume under save and make silly decisions at work because the tools we use are more advanced than our brains and so we are not as successful as we could and should be. In a consumer example, we buy things that we don't intend to buy, we don't save as much as we should, we eat too many bags of chips because Grab can bring to me anytime. And so the premise is if you want to build digital products and digital experiences for people to actually be more successful, then you need to understand the psychology of their decisions as they make them through time. And so the practice of behavioral science is understanding why a person is making a decision through understanding by and large two elements. We refer to it as the mental model which is just the approach that a person individually takes to making a choice. And we describe their choice environment meaning the time and place in which a decision is being made. So when we understand those two things, we can design much more effective digital products and services.
[00:03:33] Speaker A: I guess I'll jump in and be immediately provocative with what you've just said. Is this a kind of dark science of marketing to basically extract more consumption?
[00:03:43] Speaker C: There's certainly an element of that. We refer to these as dark patterns or sludge, where you're trying to make somebody do something that they wouldn't otherwise do. And you certainly can use behavioral science in that way. In fact, for this reason, there's a masterclass that we offer both to people that want to learn to train others as well as to people that want to use it in their jobs. We train in this module called Don't Be Evil. The basic premise is don't use predatory practices that rely on psychological insights because you can trap people in, let's say, habit patterns of addiction. You can get people to pick up a loan that they don't need. Now, that being the case, that's the dark side. The maybe fundamental or maybe most famous piece about behavioral science is this concept called a nudge. And when we're talking about nudging, here's a maybe some truth in a somewhat pedantic kind of way of explaining it. You can't nudge the unwilling. The basic premise of a nudge is I am going to make some small change in that user's choice environment such that they take the decision that they want to take. But sometimes habit or temptation causes them not to take. Give you the example of a sustainability version, most, let's say people who are doing home delivery don't need the cutlery, that single use cutlery, but they end up getting it anyway because they didn't click the button that said I don't want it. And so sometimes changing one small thing of an opt out of that cutlery to an opt in of where you have to click a button to actually receive it helps a person achieve some kind of sustainability goal. In this case that they set out. They don't want to use plastic, and so now they don't because of a small change in the choice environment.
[00:05:27] Speaker A: So are we in the space of atomic habits? James Clear?
[00:05:31] Speaker C: Well, we're trying to drive towards that. I mean, atomic habits are certainly really, really helpful when we're talking about learning. And this, I think is a lot of what we do when we talk about the usage of behavioral science. What we're trying to do is to either make something very complicated, let's say, for example, like changing your health behaviors, changing what you eat or how you exercise. And we're trying to make it so that more people can be more successful when using digital products to help them succeed. Similar concepts are being applied in the financial services to help people save more money because they said that they want to. That is the ultimate goal though, of using what he describes as atomic habits in an ethical and effective way. Digital products and services in terms of.
[00:06:14] Speaker A: Behavioral change, David, can you expand a bit on what internal or emotional conflicts do people often experience when making these decisions and how does behavioral science inform them to feel more secure?
[00:06:26] Speaker C: I actually have a really, I think a really good one on that particular question. It's about people that struggle with lifestyle disease. So if you're living in an urban environment, it doesn't really matter what urban environment it is. We happen to have done a product and a study on Mumbaikers so people from Mumbai, India. And what often happens as people approach, let's say for example, diabetes is they have lived a life that I think most urban people could empathize with in some way. They go back and forth every day, we're on the train, on the bus, 90 minutes, two hours a day. They go out with their co workers and their friends at night for drinks and dinners and that kind of stuff. And for about 30% of the population, people begin to succumb to, let's call it, that kind of lifestyle. Their bodies just can't deal with that much ultra processed food, that much inactivity. And as people get unhealthier and unhealthier and as they move towards this pre diabetes and diabetes diagnosis, what often happens is their world shrinks. And what I mean by that is they stop hanging out with those friends that were going mountain climbing. They stop hanging out with those very fit friends because they feel a sense of shame, say and their world get a little bit smaller and smaller and smaller. And as this happens, it's quite easy to develop certain disorders. It's quite easy to get trapped in an identity that is not what you thought you were. It could be that I don't feel I'm able. And so this can often trap people from making a significant change in their life. Now sometimes you have really excellent success stories. So here's a great one. They call themselves the diabetic marathoners. It's this small group of middle aged men in Mumbai decided this isn't us, we're not, we will not be insulin dependent for the rest of our lives. This is not going to be us. So they decided to create for themselves a system as support group. But ultimately an identity which was, yes, I have diabetes, but I am a marathoner and that is my identity. And having that identity informed a number of decision making approaches that they would take. No longer were they tempted by the sweets, because a marathoner doesn't eat sweets. And so it ceased to be, let's call it a temptation, because their identity is not someone that goes for sweets. And so identity can have a very significant effect on the types of decisions that we make. Now. Changing an identity is hard work. It is possible, but it's still hard work.
[00:08:57] Speaker A: So aligned to what you've just said, which is absolutely fascinating. As a behavioral scientist, how do you know when an intervention has truly resonated with people on a personal level rather than just creating a momentary shift?
[00:09:11] Speaker C: The true answer to that is you need what we refer to as unmoderated tests. So either you need to observe the behavior happen for a window of time, or you need to be able to measure the outcome in some other way. If you're not sort of like watching them every day, for example. So you can think of these like a longitudinal study, for example. This is the way that you have to measure it. One piece that's relevant with behavioral science is it does have one foot in the world of psychology, for sure, but its other foot is in the world of economics. And for that reason, it's nice to know that we've changed somebody's opinion. If they now feel differently about topic or a particular goal, great. But we really only care about did that intervention produce the desired result. And so, like any other scientific practice we set, we have a problem that we're briefed on. We set a design hypothesis, and then we seek some kind of testing approach, some kind of way to design an experiment where we can empirically test whether our intervention worked or not. So if you're talking about, let's say, for example, a health outcome, talk about our diabetic marathoners. There's a couple of ways or, well, there's many ways that you could probably measure whether you had the impact that you saw it. You could say, well, did they, did they through some survey that we ask at intervals of time, did they go back to needing insulin? You could measure based on did weight loss result? Was it, for example, was it sustained? Did they revert after some time? And in health and financial, and let's call them work outcomes, the measurement period can change. So for some people, let's say, for example, quitting smoking, if that would be a goal, well, we might need to watch for at least six months for things like work behaviors, maybe a year can be required for certain activities. So it varies. Sometimes we can know in two weeks with some degree of confidence that an intervention has worked.
[00:11:09] Speaker A: You mentioned the case study there, David, in Mumbai. And as a proud Canuck, having worked in Canada and spent an extensive time in India and talking to you now in your apartment in Bangkok, I want to maybe unpack. What cultural values or attitudes do you find most challenging to work with when designing these interventions?
[00:11:29] Speaker C: It's an interesting question, Graham. I can say I can speak about things that I found personally challenging. Professionally, research itself is valued differently based on industry and to a degree, based on country as well. So I think, for example, the practice of doing market research or user research might be more highly prized in Canada. And so my experiences there would suggest that while research is still possibly not the most important thing in a business's mind ever, I think when I compare Canada to India and Thailand, perhaps the value of research, research is a touch higher in Canada than it is here. But I don't want to say that that's universally the case. I've worked with many businesses in Asia that absolutely value research. That being said, cultural difference from a behavioral science perspective does matter. For example, when we designed an intervention in Canada to help Canadians save more money, what we built it on was an existing savings behavior that didn't exist in India. And that when we tried to copy paste that into the Indian market, it didn't, it didn't really work. So I think for me, the struggle of being the outsider is that I don't have the cultural reference intuitively. I didn't grow up in India or Thailand, and so I don't know about the small little creative things you might be able to do without going through the process of research, which is not necessarily valued to the same level as might be the case in Canada. And so for me, professionally, the struggle is always what I refer to as cultural translation. The concept is I don't know if a particular creative approach is going to resonate unless I have insight into the culture of the product or service that we're working on. And that often means I need to bring in a larger group of people to work with me, which makes it more expensive and more complicated.
[00:13:31] Speaker A: I think that really resonates with innovation design practice as well, because I feel designers of all stripes, the five step approach, you know, the sheen on this has rubbed off recently. And who's to say that what is successful in California or London can be rinsed and repeated in Asia Pacific. And it requires a, it requires customization, it requires cultural sensitivity. So I think we're on the same page there. Kind of leads me on to thinking really behavioral science should be part of design practice. I think there's a symbiotic relationship there to create value in the innovation space.
[00:14:10] Speaker C: Here's the truth. When we were first setting up MakeIt, which is my second company that does, we have a behavioral design framework. We had been building this thing for years, testing it in universities. And when we decided to set out commercially to use this framework, we sat down. My co founder's name is Massimo. And so him and I sat down and said, okay, if we're going to embed behavioral design into product and experience, who is going to do that? Because obviously it won't just be us alone. We want a movement. And so to create that, we said, well, who's most likely to use this? Probably people that sit in the innovation space, whether they're in corporates or they're in startups. Okay. That then led us to how do they work? And that then led us to well, they work in scrum, they work in sprints. Okay, we're going to base this on the Google Design Sprint methodology for a couple of reasons. One, when somebody has to learn something new, that's a friction that by itself is a barrier to the usage of this thing. And so for us the concept was we don't want to teach them a new way of working and a new subject matter because it's not going to work. And this had been, this actually has been the problem with behavioral science. There has been many bespoke frameworks that have been created that require a post grad in some kind of social study of some variety in order for you to be able to effectively use it. And that largely restricted its audience to such a small chunk of society that behavioral science would never reach its potential. And so we built a behavioral science framework or a behavioral design framework on top of Design Sprints, Google's Design Sprint. And in that way, listen, there's a couple of approaches. I'm sure you've used both of them yourself, but you can have excellent market and consumer psychological research prior to the start of a sprint. You can inform the design lead and the product owner and you take it from there. Or if you don't have it, you can take the lean approach and you can sort of use your experience and judgment and do your best. For both of those scenarios we built a product called Make It. But in that second scenario, what we did Was we built it so that if you don't have the research, you can still create a really clean journey map and still identify what we call behavioral problems and ideate behavioral solutions to those without necessarily having that research, though we generally recommend that you have it. Sorry, that was my sales pitch for the day.
[00:16:35] Speaker A: If we take established companies, David, you worked a lot with them. If we take large family owned, traditional businesses listed companies, where does the behavioral designer, the behavioral scientist sit? Or where would they sit? Because I haven't come across a large organization where this role, you know, it's got to fit in the bucket. It's either in new product development, it's in marketing, you know, it's in risk, it's in compliance, it's in legal. Where does this role sit and how can we get more traction and how can we get more expertise into large organizations to deliver this?
[00:17:09] Speaker C: So it's dependent on the industry. So for example, in government it's quite common for that behavioral science head to sit in an office of innovation. In family owned businesses, quite often that person would also be in that innovation team. The concept being when you are a, let's call it, mature business with a good revenue stream and a somewhat stable kind of profit margin, even when there is desire to disrupt, let's say for example, we need to undergo a digital transformation because AI is here and all that kind of stuff, there is still a reluctance to make too many changes to the business that's keeping you afloat. And so what many family businesses wind up doing in their transformation efforts is they, they will look to de risk it. And there are many ways to de risk innovation. Often it means let me create a sub brand, let me create a different product and then let me try something crazy and creative with that. And if that's successful, then we'll gain some confidence and we can look at some internal structures as well. But embedding behavioral science in a family business across the entire organization is, in my experience, it's been an incre approach like that. Start with something new that's a bit less risky and then expand accordingly. In banks, that office of behavioral science, they can sit within the product team, they can sit with the COO's office, they can report into the CMO. That largely depends on whether the organization is a little bit more product led or a bit more marketing led or a bit more sales led. But quite often I think what's important about the behavioral science world is that it's so specialized that there may not be one right place for them to exist. In every organization they tend to be kind of like a services team that might be, you might, you might think of them as where the customer experience team sits. Sometimes they're their own team, sometimes they're in marketing, sometimes they're with the product people. And so we've seen a couple of models. We've seen the kind of the, the, what do you call it, the command and control model where you've got like a hard team of behavioral science folks that sit in one office and they report to a C suite of some kind. We've also seen a distributed model and in distributed models, in, let's call them more product led companies, typically what you're there is you're finding that the people that need to have that competency are the product owners and the design heads. And if they have it, it's less important for C suite to have this particular expertise.
[00:19:35] Speaker A: Going back to the make it toolkit, David, the purpose of this is knowledge acquisition. You go in, you run a series of workshops. It's to support continuous learning and development inside organizations. Or is it something that's more Train the trainer in terms of getting impact done autonomously without you?
[00:19:55] Speaker C: The train, the trainer model is the one that we like for a number of reasons. We are primarily problem solvers. Sure, we like training. All of us have taught in various universities around the world. But ultimately impact for the behavioral science world is what's needed to validate to a broader audience that behavioral science has value. And so our focus has been on how can we equip others to succeed so that we can go help solve problems directly ourselves. And so we have a couple of products but this one here, the Make It Game or the make it box, it's largely to help with problem identification and ideation for solutions. That's really what we built it for so that others can just take it. Now there's a number of ways to learn how to use it. The reality of behavioral science is some learning is required before you can play. It's not such a simple simple. A simple experience is that though we've tried, we built a product called make it GPT to help people with that too. But the premise is you could take training from us in a train the trainer kind of variety and we'll take you through 24 hours of very comprehensive behavioral design techniques and approaches and we'll help you through or you could just learn a little bit. So we have a self paced 10 minutes a day, 30 day course which will be enough for you to learn to use this thing. Without us. And then run your own design sprint and begin to apply behavioral design directly into whatever you're working on so that you can have impact and hopefully tell us about that impact so we can shout it to the rafters. Tell everybody.
[00:21:28] Speaker A: You mentioned GPT, David. And this obviously leads on to the topic of the impact of generative AI within the behavioral science space. I'm getting a bit tired of generative AI already and I'm just wondering, I'm wondering what's your take on it? I mean, it's crossed the chasm, it's here with us and I'm trying to find.
[00:21:53] Speaker C: I think I felt a little bit about what you were feeling back in the summer, the hypes, let's just say the hype cycle was definitely in full swing up until about maybe the summer. And then people began to realize, ooh, this thing is not quite as magic bullet simple as we thought it would be.
There is a great deal of additional complexity that needs to be addressed in order to make this thing work properly. Okay. So like you, I also felt that maybe GPT had been a touch overhyped and that's normal for new pieces of technology. That being the case, it's here, it is going to be huge.
I can speak to from like a marketing perspective or from a behavioral science perspective, maybe that behavioral science is more relevant. I'll start with one of the initial findings. What did it do from a work context? To us, there was a really excellent MIT study in, I think it was March of 2023 that came out and it was describing it, doing three things in the average team's work cycle. So if you think of work, complex knowledge work, let's say, for example, in three phases you have a brainstorming phase where you think about what you're going to work on. And then you have, let's call it a first drafting phase where you put together your idea at some level of resolution and then you have your finished product, which will take however long, which will be the thing you might show to a customer or to your boss. What GPT did in those three phases was it took the time it took to brainstorm. Reduced it by a third classic example, a leadership off site, where a bunch of business leaders or organization leaders need to make some decisions about what they're going to do for the next year, year, three years, five years. In that day or several days. There is a lot of tedium where people go back and forth about this opinion. That opinion shouting's common, somewhat emotional, somewhat tiring, but what GPT does in that type of scenario is it cuts down some of the back and forth debating because it allows you to put up a whole bunch of ideas really fast and then everyone can look at them more calmly and quietly and decide what order anything need to be done in. Now, there's some error, there's some risk, a bias in that, in its own right, but that's what it does. The first draft piece of time, it takes that effort and cuts it in half. The amount of time it takes to get to a first draft of a report or a product is cut in half. Which is cool. Really, really cool. Very, very helpful and powerful for newbies. So if you're new in a job and you're just learning how to do it now, all of a sudden what's possible is senior folks can give to even more junior folks more sophisticated work than they could have been given before, which is fabulous from the perspective of work sharing from autonomy, it makes the young person feel a whole lot more powerful and important, which is great. But it then does one weird thing. And if you've used GPT a lot yourself, you might be familiar with the annoyingness of this. It takes the amount of time to create a finished product and doubles it.
[00:25:04] Speaker A: Because why?
[00:25:07] Speaker C: If you've listened to anybody who's a non native English language speaker or translate something into English using GPT, GPT writes in its stock form. Now, I'm not Speaking only of OpenAI's, I'm also speaking of Geminis and Anthropics and several others out there.
[00:25:29] Speaker A: Yeah, and I can smell it from a mile away. In my capacity as a professor at Chulalongkorn, every time I see the word fostering, I'm like, here we go again.
[00:25:38] Speaker C: Or delve.
[00:25:39] Speaker A: That's another great one.
[00:25:41] Speaker C: So the premise, and this is my silly observation of it, but to me it writes like a teenager. So if you want to write something professional to let's say a B2B audience, or to an audience that's already very, let's call it, sophisticated with respect to the product or service that you're offering, it is obvious that it's AI written. And so lots of editing is needed. Cutting, cropping, fine tuning, do it what you call it, to make it ready. Now, there are some solutions to this at the product level. We've built several that get around these kinds of sillinesses, but for the most part, this is the impact that it's had on work. Now, a little bit closer to home, I can speak to some interesting stuff about Asia which was neat from a usage behavior. So there's a trend now, you may have heard of the term bring your own AI, but here's what's happening. The majority of business leaders, so something between the 65 and 75% range around the world are now saying, okay, we need to do something with AI. We don't know what but or how, but we definitely have to do something. We get it, we see it, we'll do something. It sounds super cool, but they don't know what to do.
[00:26:50] Speaker A: Yeah, I mean 10 years ago it was okay, my retail bank has an app, somebody else wants it on or you know, my golf partner, their retail bank has an app, we'd need one too. And there's a kind of a pull of have we missed something? But really, I guess the comes back to what's the ROI on this thing?
[00:27:10] Speaker C: So this is actually the problem. It's really only the creative applications that have made any money. So if you think about, for example, the Dall E's of the world, the mid journeys of the world, the kinds of tools that are there to help you do something creative, this has been fantastic. So I mean, as a designer myself, I can say this, I can't draw, but I design strategy, I design change management, I design products at, let's call it the text level or the user journey level, but I can't draw. It has unlocked a way for me to communicate visually that I just couldn't do before. And so those business lines are doing really, really well with GPT. But when you're talking about GPT solutions away from the creative spaces, what you're largely talking about are productivity enhancers. And my view on them is, is listen, they're brilliant. So a small little anecdote about this. 78% of employees in SMEs across Asia. So small and medium sized businesses are bringing their own AI to work. They're using ChatGPT or Gemini or whatever. Whether or not the employer has provided it, they're figuring it out for themselves and they're just using it, at least for small tasks here and there. Now what, what that's doing is it's actually making them quite happy. They're happier at work. And so the question that follows is why are people happier using a tool that they may be spending their own money on at work? Well, the answer is it's taking away the tedium. There is a number of, let's call it, back and forth emails or forms that need completing or reports that need to be written up that they can now just feed to a bot and have done in seconds. So taking away what we describe as admin burden has made people who are ready to use AI very, very eager to take. And the neat part about it is it's actually developing Asia that's leading the charge here. When we look at developed Asia, if we look at Australia, New Zealand, Japan, Singapore, these types of countries are actually laggards in the usage of GPT products. It's China and India and Thailand that are burning path forward, saying, we love this, let's use it.
[00:29:22] Speaker A: And along that same line of inquiry, David, with your behavioral scientist hat on, how do you think generative AI changes the way we think about trust and authenticity in future digital interactions?
[00:29:38] Speaker C: That's a good one, isn't that so? Trust we describe as a thing that's earned. That being said, it takes a long time to learn or to earn that trust from a branding perspective, let's say, but a very, very easy thing to lose. And so I'll tell you about maybe a small example right now.
[00:29:57] Speaker A: Now, I could also say Jaguar cars. Have you seen it?
[00:30:02] Speaker C: You know, I still haven't seen, I've just seen an ad mocking it. I haven't seen what they've actually done.
I do want to see it, though. So here's a small example of how you can lose a customer's trust very quickly with a poor execution of GPT. I think you might have heard the term hallucination. GPT products have a tendency of making things up and it's part of their design. The algorithms need, let's call it a degree of creativity in order to be able to answer properly. It's described as a logic or a type of thought process. But the basic premise is if you turn down its creativity to zero, so that it never, ever, ever hallucinates, it makes it much less useful. But I can give you a couple of small examples of it. There is a product, a foundation model out of the US that is focused on medical technologies or medical applications. It's about helping doctors provide prescriptions for ailments under, you know, a variety of scenarios and what they've done. When you build any kind of GPT product, you need to provide to it a set of rules. Let's say, for example, if a user asks this, then you respond that, and here's an example of what I mean. Now, they provided that set of instructions that come at the beginning. You please answer this way in these circumstances. But the second component of a GPT is that it, it is a reinforcement learning model. And so what that means is as users were asking questions as these doctors and nurses would say, hey, this is what's happening. Patient is presenting with these symptoms. The questions that they themselves were asking were changing the answers that the bot was providing. And so what they found was in year, one person would walk in and be presented as this person that has a cold and they'd say, well, you should take this medicine. But a year later, same rules in place, it was providing different answers now for a medical application that cannot. And so there are some problems that are worked out there, otherwise doctors will not have trust in it, even something less, let's call it less serious than medicine. We're working on a product right now for a UN agency and it also must not hallucinate because if it provides the wrong instructions at a particular stage of a project that a team is working on, it could burn weeks of effort before they find out they did it wrong, forcing them to go back and do it again, which would embarrass them. All kinds of bad stuff would happen. And so we know that if you don't get that balance right about providing use and making things way easier for users to, let's say, reduce the amount of time they take at work doing tedious administrative tasks, it needs to provide that utility. But if it hallucinates, you'll lose that user's trust and they won't use it anymore. That's maybe the most useful thing that I could say about trust and GPT products.
[00:32:54] Speaker A: So let's have some blue sky time. David, if you picture yourself in five years time, I want you to comment on where do you see see the sweet spot for behavioral science A from a corporate perspective, but also from a personal perspective five years out.
[00:33:10] Speaker C: What I see is behavioral science being known across, at very least Asia within the fields that I would describe as the most regulated. I want governments to be using behavioral science across the services they provide to their citizens. I see behavioral science being used in fintech and in the existing financial services services organizations across Asia. I see it being used in health and I hope that it is used well in marketing for retail applications. But I also see or hope that some degree of awareness will come in much the same way that nudges are things that many, many more are aware of in Europe. I hope that Asia has the same concept because it becomes much harder to use persuasive or deceptive techniques. So dark patterns and what we call sludge, making people do things they wouldn't otherw do would become harder to do. But this is a hope. And I'll kind of maybe conclude it on this. On the professional side, the behavioral science world has a problem. Well, many. But one problem that we like to talk about a lot is what we refer to as the weird problem. So you alluded to this a little earlier on when we were talking about culture. Most of the research we have about how the mind works, so the psychological research has been conducted on Western university campus. Most of what we understand about how the mind works comes from understanding students who went to LSE or Stanford.
[00:34:39] Speaker A: That's exactly the same with looking at the ROI with design thinking.
[00:34:44] Speaker C: So the problem, and I don't want to drone on about it, but the problem is, well, listen, we are mostly the same. Most of the ways that we think about buying dinner at the end of the day, most of the ways we think about getting ourselves an education or saving for the future, most of these things are the same. Same. But the social element and the culture we come from absolutely have a bearing on what we do. And what we don't have enough of are examples of how we can apply behavioral science effectively at scale in Indonesia or Thailand or China or India. Don't get me wrong, there's some excellent behavioral scientists I know in all of these countries that are trying their level best to do it, but we're all throwing pebbles in the pond right now, hoping that eventually there's a wave big enough for it to finally roll and do real good. It's beginning. But we don't have enough examples of how a person in Manila navigates through a savings journey or how somebody in Bangkok navigates through a health journey and how that's different from how they might do so in New York or in Toronto. So from a professional standpoint, we're all railing to educate the market. So five years from now, we hope that there is much broader awareness and usage across the regulated industry and some more awareness in the other industries would be nice commercially. From a personal standpoint. Listen, we're building a behavioral science business and we hope five years from now to have a much larger team so that we can have much bigger impact in far more companies and organizations and countries than we can today from our small little corner of Bangkok.
[00:36:22] Speaker A: Last thing, David, is there anything that you would like to add or is there anything that we've missed?
[00:36:28] Speaker C: Maybe I'll ask you a question. What do you think would be the barrier to bringing behavioral science into a design thinking process?
[00:36:37] Speaker A: I don't. I see a convergence of computer science, behavioral science to inform design practice. And I mean the fourth order of design, which is the design of systems. I think we've been a little bit too complacent on the quantitative side over the last 20 years. I'm consciously aware of how we can decolonize behavioral science and design practice. And I feel the more knowledge that can be contributed from a non Anglocentric point of view I think is really, really important.
[00:37:10] Speaker C: It's a good point on the data. So marketers have talked about this over the years, but it's called personalization of one and I'll apply the same logic to talking about behavioral science and nudging. So I'll leave with a personal story. This will require significant amount of data in order to enable this. But the premise would be something like this. If I have decided that I need to lose 10 kilos 20 pounds for our American and British friends, I know that there are a number of behaviors or a number of barriers I should say, that are in my path that are going to block me from doing that. I'm a 45 year old man. I've got some bad habits that have grown throughout the years. They've been with me throughout the years anyway. Some of these date to childhood in many of us. And so. So I know that I'm in a world of temptation. My phone is never very far from me and I know that I can always have my particular snack of choice anytime I want it. Covid made it a little bit harder, but then hyperlocal delivery services made it very, very easy. So from that data angle, if I have decided to go down a particular path, I wish to be trim, healthy, I no longer wish to eat junk food. Let's say then what I would dearly love is for my phone to know that and help me. I would love for my phone to tell grab or Uber. Hey, don't show David the chips on the first page. Make him swipe two, three times to get that. If he does have a bad night of sleep, which I would know because he'd probably turn his phone on at three in the morning trying to get back to sleep, then maybe I should also know that that day he's probably more likely to to order the chips. And so can I intervene before that happens so that he's not tempted? Can I pre order a salad for him so that it shows up on his door right at the moment when he's going to say, or can I in the moment when he's going to order the chips, can I bury it three to four swipes down so that he's less likely to go get it. Can I make it harder for him to do it now? I can throw these examples across the board, but this is my dream with behavioral science. And one of the reasons that I went so hard at it was. Listen. The potential for digital technologies to help us lead healthier lives, happier lives, is so compelling to me. But in order to do all of these things, we need tremendous amounts of data. We need data sources that come from the full cloud. We need to pull from IoT devices, watches, phones, laptops, the whole nine yards. And there's more than it can do than just that. But from a data science perspective, that's predictive algorithms that are going to help with stuff like that. And that's the next frontier of behavioral science. And that's a space we like to talk about a lot.
[00:40:02] Speaker B: My thanks to our guest, David McCann there. Hacking Kaizen is produced by DSA. Nikki edited the program. We'll be back at the same time next week, but until then, from me, Graham Newman, many thanks for listening.
