Latest / ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence / Connor Joyce on How Behavioral Science is shaping the core of Product Development (#030)

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Connor Joyce on How Behavioral Science is shaping the core of Product Development (#030) · ContraMinds Podcast - Unlocking Personal Growth and Professional Excellence

• 0:00
  SPEAKER_02: Choice is such an important thing.
• 0:02
  SPEAKER_02: We as humans love autonomy and agency.
• 0:06
  SPEAKER_02: And so giving us the opportunity to choose something, but then help me follow through with it is huge.
• 0:16
  SPEAKER_02: It's a great introduction to the principle of nudge.
• 0:29
  SPEAKER_01: Hey everyone, welcome to a new episode of Carter Mind.
• 0:32
  SPEAKER_01: In today's episode, we are in conversation with Collin Joyce.
• 0:36
  SPEAKER_01: Collin is a behavioral science evangelist.
• 0:38
  SPEAKER_01: His area of interest and expertise is to drive user transformations using tools and techniques in the world of design, economics, and computer science.
• 0:46
  SPEAKER_01: Collard is also the co-founder and executive committee member of the Applied Behavioral Sciences Foundation.
• 0:52
  SPEAKER_01: In this episode, we speak to Collett about behavioral science, the four pillars of choice architecture, heuristics and biases, and the structure of social norms.
• 1:01
  SPEAKER_01: So without further ado, here is our conversation with Collar Choice.
• 1:07
  SPEAKER_02: Thank you, Vignash.
• 1:09
  SPEAKER_02: Pleasure to be on the show.
• 1:10
  SPEAKER_01: You're most known for your work and uh evangelization, if I might use a term, uh for the adoption and use of behavioral science.
• 1:18
  SPEAKER_01: So uh my first question to you is behavioral science is uh it's almost like an umbrella term, so to speak, where you're actually using a lot of different techniques and different uh skill sets and different knowledge and frameworks from related fields.
• 1:33
  SPEAKER_01: So um just to start things off, could you tell us what are the similarities and differences uh between behavioral science, UX, and data science?
• 1:43
  SPEAKER_02: Yeah, absolutely.
• 1:44
  SPEAKER_02: And and it's so true, I'd say at a very high level.
• 1:48
  SPEAKER_02: I've been thinking about this a lot.
• 1:50
  SPEAKER_02: Is is a is applied behavioral science its own field, or is it actually just a specialization of other fields?
• 1:58
  SPEAKER_02: And and the reason that I that I think that is, you know, I see more and more firms starting behavioral science teams.
• 2:05
  SPEAKER_02: At some point I'll probably talk about my work at Microsoft and and at Tonal, where I've been a part of and helped build the first behavioral science teams.
• 2:14
  SPEAKER_02: But I also see a lot of firms that don't want to go and create a dedicated behavioral science team and instead want to try to help get their PMs and their user researchers and other data scientists, etc., get them equipped with behavioral science skills uh so they can weave that into their work.
• 2:34
  SPEAKER_02: And and so it's uh it it is, I think it's the jury's still out, we'll say, on on which is is more.
• 2:40
  SPEAKER_02: I think that ultimately there will probably be a place for applied behavioral scientists as a whole, and then there will also be others who uh just become specialized in it while still maintaining their main department.
• 2:51
  SPEAKER_02: But but if we look at the the state of the field as of today, we'll say, and and what are the differences between an individual who calls himself an applied behavioral scientist, such as myself, uh, compared to people in user research and data science.
• 3:06
  SPEAKER_02: I think let's talk about the similarities first.
• 3:09
  SPEAKER_02: All of them think about user needs and trying to discover patterns of user behavior uh just at different levels.
• 3:18
  SPEAKER_02: And and I think that all of the teams are also research teams.
• 3:21
  SPEAKER_02: They they use research principles.
• 3:24
  SPEAKER_02: Uh, I think that it it differs depending on the actual company, but but all of them pursue some sort of research in in how they approach their projects.
• 3:35
  SPEAKER_02: And and that's another similarity that I think is really important to think about is that all of those teams are auxiliary units.
• 3:42
  SPEAKER_02: Uh, they they may help with cost savings, but none of them per se build products.
• 3:48
  SPEAKER_02: So they don't actually address the bottom line.
• 3:51
  SPEAKER_02: And instead they pick up projects to help those teams like your product management, your engineering, your marketing company uh places, departments like that that are actually creating value, creating a and bottom line value or actually creating money for the firm.
• 4:08
  SPEAKER_02: So I think all of them have uh an important role to play in creating those projects.
• 4:13
  SPEAKER_02: And then the last similarity on that line is that all of them have to be very good at stakeholder management to ensure that they really do have uh the right project to impact what is most important for those revenue generating departments.
• 4:31
  SPEAKER_02: Uh so those those are some of the similarities.
• 4:33
  SPEAKER_02: Like I said, there's there's many more, but I think those are some of the ones that I see most common.
• 4:39
  SPEAKER_02: Now, when we look at the differences, and again, this is just my take.
• 4:44
  SPEAKER_02: It does depend on the company.
• 4:47
  SPEAKER_02: It depends on the how advanced each of these units are.
• 4:51
  SPEAKER_02: It depends on a lot of variables.
• 4:53
  SPEAKER_02: I've seen UX done extraordinarily well.
• 4:57
  SPEAKER_02: I've also seen UX done where it was was really just, hey, let's go survey some people and and see what happens.
• 5:03
  SPEAKER_02: Same thing with data science.
• 5:04
  SPEAKER_02: I've seen data science being nothing more than just traditional descriptive analytics, all the way to you see some of these large tech firms that have huge ML models that that uh have the potential to you know truly change the world and and coming out of data science teams.
• 5:21
  SPEAKER_02: Uh so I think that that I'll condition everything I'm saying with with it really does depend on the actual company.
• 5:28
  SPEAKER_02: But I believe that behavioral science has a uh a great difference in that it focuses on specific user outcomes.
• 5:38
  SPEAKER_02: And and what I what I mean by that is if I look at user experience, they're generally looking for user needs.
• 5:46
  SPEAKER_02: They're looking for the opportunities to understand what the user wants.
• 5:50
  SPEAKER_02: And and again, even with user needs, the things that that are missing from that user's life.
• 5:57
  SPEAKER_02: If I look at data science, they're looking at aggregates, they're looking at the high-level overview of what are the patterns of behavior.
• 6:05
  SPEAKER_02: Could also be patterns of desires or needs, etc., uh, but they are looking at the high level.
• 6:11
  SPEAKER_02: And I see applied behavioral science as fitting in between that spectrum of low-level individual user needs, high-level aggregate user needs, but focusing on what are the specific intentions that a user is bringing to whatever product that they're using, and is that product satisfying that need.
• 6:33
  SPEAKER_02: And again, I will once one for one last time condition, it depends on the group.
• 6:38
  SPEAKER_02: There, I have seen UX teams focusing on that, I have seen data science teams focusing on that.
• 6:43
  SPEAKER_02: But when they are not, applied behavioral science at its core is all about thinking about outcomes, all about what is the specific behavior that has been changed.
• 6:53
  SPEAKER_02: And so applied behavioral science brings that very unique perspective.
• 6:57
  SPEAKER_02: The one other piece that I'll that I'll say is that uh user experience generally leans more qualitative.
• 7:04
  SPEAKER_02: Data science generally leans more quantitative.
• 7:08
  SPEAKER_02: And I found with applied behavioral science, since it's so outcome driven, that it really depends on the project, on which methodology is necessary.
• 7:18
  SPEAKER_02: For that, I'll reference that that an applied behavioral scientist really has to be a mixed method researcher, bringing in the principles of what is the gold standard of research, which is an RCT, bring in those elements and utilize those elements across both quantitative and qualitative experiments.
• 7:39
  SPEAKER_02: The last thing I'll I'll touch on, and you you Vignesh, you brought this up in the beginning of the question, but the the last main difference that I see of applied behavioral science is that behavioral science is a loosely defined field in the sense that it really is an applied social scientist.
• 7:56
  SPEAKER_02: There's there's principles from psychology.
• 7:59
  SPEAKER_02: I just finished this week.
• 8:00
  SPEAKER_02: I've been at an economics conference, and I've been surprised how many, how many times I've heard principles that I've heard of in behavioral science applied in economics too.
• 8:10
  SPEAKER_02: So it truly is, it takes understandings from all of the other social science fields.
• 8:16
  SPEAKER_02: And I believe that that's a unique characteristic of being an applied behavioral scientist is you have to have familiarity with economics, social uh sociology, psychology, a little bit of anthropology.
• 8:27
  SPEAKER_02: Uh there's you have to have an uh uh the surface-level understanding of all of those different fields that then you can bring to those user problems.
• 8:36
  SPEAKER_01: You spoke about mixed method research, right?
• 8:39
  SPEAKER_01: Mixed methods research.
• 8:40
  SPEAKER_01: Could you give us a detailed uh definition of what do you mean by mixed method research for the clarity of our listeners?
• 8:46
  SPEAKER_02: Briefly on what a mixed method researcher is.
• 8:49
  SPEAKER_02: It's an individual who is able to use both qualitative, and and I'll use the example of user interviews, focus groups, things like that, and quantitative.
• 9:03
  SPEAKER_02: And quantitative can be using modeling within like a data science machine learning approach.
• 9:09
  SPEAKER_02: It could also be just running basic statistics on like survey results.
• 9:14
  SPEAKER_02: A mixed method researcher is able to identify what is the deliverable that the team is trying to create, what insights have to be within that deliverable, and then what is the best method to actually achieve that?
• 9:30
  SPEAKER_02: I find a lot of researchers begin to specialize in one area, either qualitative and quantitative.
• 9:37
  SPEAKER_02: And I think that's fair.
• 9:39
  SPEAKER_02: It's still how research teams generally organize themselves.
• 9:42
  SPEAKER_02: But a mixed method researcher can still have a focus area, but is also able and ready to go and jump onto the other side if it's necessary and have some skills in doing whatever that type of research is.
• 9:56
  SPEAKER_01: So when we look at traditional product management, uh, we always have this concept of a product requirements document and a feature document and user stories and stuff like that, right?
• 10:06
  SPEAKER_01: The structure of the user story is largely written to define the requirements that needs to be given to the developer so that there is accuracy in what's being asked and what's being built.
• 10:14
  SPEAKER_01: In the context of user outcomes, uh could you just talk to us a little bit about the difference between user stories and user outcomes?
• 10:21
  SPEAKER_01: And how do you tie user stories to user outcomes to uh design and deliver useful products that really change customers' lives?
• 10:30
  SPEAKER_02: It's a great question, and and I I've seen both good user stories and I've seen also rather basic ones.
• 10:37
  SPEAKER_02: If we think about a user journey just in a basic flow of a user, we'll we'll say we'll take a an app.
• 10:47
  SPEAKER_02: So a user picks up, uh actually we'll I'll I'll use the I've been using Lyft a lot.
• 10:52
  SPEAKER_02: I I actually really like Lyft.
• 10:54
  SPEAKER_02: I I think that the I like their design, I like their flow, I think they have a great data science team.
• 11:00
  SPEAKER_02: I believe they even have some behavioral scientists, or at least again, a data science team that has some specialization in in behavioral science.
• 11:06
  SPEAKER_02: But when I pick up my app, what am I trying to do ultimately?
• 11:11
  SPEAKER_02: And and that's what that user outcome is.
• 11:14
  SPEAKER_02: When I have what intention am I bringing to lift?
• 11:19
  SPEAKER_02: It's generally I need to get somewhere as easy as possible, potentially as cheap as possible, potentially I need to schedule something.
• 11:29
  SPEAKER_02: There is there's there's different needs and outcomes that I'm looking to have, but we'll use the outcome of I need to get to my destination as easy as possible.
• 11:40
  SPEAKER_02: Now, if that's the outcome, how will we measure that?
• 11:43
  SPEAKER_02: We could look at satisfaction scores.
• 11:46
  SPEAKER_02: Well, first of all, we'll we'll take it.
• 11:47
  SPEAKER_02: I have kind of a diagram in my head of different levels of outcomes.
• 11:51
  SPEAKER_02: So the first outcome could be just usage.
• 11:54
  SPEAKER_02: Did I use Lyft to get from A to B?
• 11:57
  SPEAKER_02: Okay, so that tells us something.
• 11:59
  SPEAKER_02: We'll we can we can dive in a little bit more with that.
• 12:02
  SPEAKER_02: Now, was I happy with using Lyft?
• 12:05
  SPEAKER_02: And that would be the next level of outcome.
• 12:08
  SPEAKER_02: And and I I generally call that like a customer satisfaction.
• 12:11
  SPEAKER_02: CSAT is a is something that many may be familiar with.
• 12:15
  SPEAKER_02: But does that still actually tell me did I achieve the outcome of feeling that Lyft made my process of getting from A to B easier?
• 12:26
  SPEAKER_02: It doesn't.
• 12:26
  SPEAKER_02: It doesn't.
• 12:27
  SPEAKER_02: It tells me, yes, I used it, but maybe it was maybe every other available option wasn't available or it had some huge wait time because maybe I'm in Chicago right now, maybe maybe a big festival is going on, and Lyft just happened to have the shortest wait time.
• 12:44
  SPEAKER_02: Is it is it easy for me to be able to to the offer we're ultimately asking the outcome of how easy was it for me to use the app and get my intention getting to the destination B?
• 12:59
  SPEAKER_02: How easy was it?
• 13:00
  SPEAKER_02: That could be done with surveys.
• 13:03
  SPEAKER_02: It could ask me after I've used the app, did I actually find it that I it was the easiest option for me?
• 13:10
  SPEAKER_02: Could ask me what were the primary drivers of me deciding to use Lyft.
• 13:14
  SPEAKER_02: But they could also look at behavioral data.
• 13:17
  SPEAKER_02: How long does it take for an individual once they open the app to select which ride that they're going to do?
• 13:23
  SPEAKER_02: How long does it take for that car to get there, and then for that car to drop them off?
• 13:28
  SPEAKER_02: So there's all there's other metrics that could be included.
• 13:31
  SPEAKER_02: I think of them if we go down that list, there's usage, there's CSAT or customer satisfaction, there's the direct outcomes, what did I ultimately do and what what were the behaviors that I took after interacting with the app?
• 13:45
  SPEAKER_02: And then there's those true behavioral outcomes, which is how do I, how did it change whatever intentions that I brought to the app?
• 13:53
  SPEAKER_02: Have I found a new faster way to get to my destination?
• 13:58
  SPEAKER_02: And so connecting it back to your original question of how is that different than a user's user journey or a user story?
• 14:05
  SPEAKER_02: A user, a user story at its basics and most basic, is just what does Connor do when he interacts with the app?
• 14:13
  SPEAKER_02: It's just what is the flow that he is taking within that app.
• 14:18
  SPEAKER_02: And that can be really helpful at deciding will Connor use the app, and potentially even helping decide, will Connor enjoy using the app?
• 14:29
  SPEAKER_02: But it doesn't always mean that it's going to be satisfying the intention that I'm ultimately bringing to the application.
• 14:38
  SPEAKER_02: And and I hate to beat up on social media, it seems like they generally are the one that always gets chosen, but I think it actually delineates the best example of if I go onto a social media app with the intention of I want to feel entertainment, I want to feel good, whatever that might be.
• 14:57
  SPEAKER_02: And then I wound up spending time on the app watching videos that make me angry or invoke other negative emotions.
• 15:05
  SPEAKER_02: Did I actually, yes, I used the app, and maybe if you ask me, I even say I enjoyed using the app, but did I fulfill the in the initial intention of wanting to have entertainment and social connection?
• 15:19
  SPEAKER_02: No.
• 15:20
  SPEAKER_02: Instead, I just filled my time with with other uh emotion-evoking we can call it entertainment or evoking content, we'll say.
• 15:29
  SPEAKER_02: So um so I think that the the that's the primary difference is that a user story is focused on that usage and customer satisfaction, where user outcomes are really thinking about is it satisfying the problem the user brought.
• 15:42
  SPEAKER_00: Connor, um taking off from what uh you just explained, there are three parts that I see a behavioral scientist needs to do.
• 15:49
  SPEAKER_00: One is understanding peoples and habits, the second one is designing the elements that enhance the usage, and the third one is deploying and measuring behavior which you spoke about.
• 16:01
  SPEAKER_00: But what is critical from what I see, from what you're saying, is that the behavioral scientist really starts far ahead than the product team because you're really looking at an outcome and a behavior, and a change in behavior is what behavioral science really talks about.
• 16:19
  SPEAKER_00: So, is that the primary objective and the goal of a behavioral scientist?
• 16:25
  SPEAKER_02: I think that's that's exactly how I view it is that a behavioral scientist and really research as a whole should start before the product development cycle and then remain after the product ships.
• 16:39
  SPEAKER_02: And everything in between, there's still a place for research to play, but I believe that the biggest value comes at the beginning.
• 16:46
  SPEAKER_02: I'll give an example.
• 16:47
  SPEAKER_02: When I was at Microsoft, one of the one of the projects that I did was what I called a habit diary study.
• 16:54
  SPEAKER_02: And I think that that's the best example of what you're talking about there.
• 16:58
  SPEAKER_02: So I was on a team called Viva Insights.
• 17:01
  SPEAKER_02: Viva is a Microsoft product that's focused on helping increase employee engagement and well-being.
• 17:07
  SPEAKER_02: Viva Insights is a is a product that helps do that through the aggregation of de-identified worker behaviors.
• 17:17
  SPEAKER_02: So time spent in collaboration.
• 17:19
  SPEAKER_02: One of the biggest features we created was the focus time auto-booking feature.
• 17:25
  SPEAKER_02: Um, for some people who are familiar with Microsoft Teams, you may know it as MyAnalytics or Viva Insights, it was what it was rebranded to recently.
• 17:34
  SPEAKER_02: We were left with thinking, what are different features that we could create that would help with that mission of engagement and well-being?
• 17:43
  SPEAKER_02: And there's a couple ways to approach that.
• 17:45
  SPEAKER_02: One is to just go out and see what features that users are requesting, saying, hey, I like this idea.
• 17:52
  SPEAKER_02: Another way is just to get a bunch of smart people in a room and say, hey, what what do you think we should build?
• 17:58
  SPEAKER_02: And and both of those options have potential to create success.
• 18:04
  SPEAKER_02: But for something, especially like the problem that Viva Insights has of trying to really change employee behavior in a positive way, you need to go deeper.
• 18:14
  SPEAKER_02: You need to go earlier than even thinking about the user of your product, and instead think about the worker in the world of work.
• 18:25
  SPEAKER_02: And what I mean by that is you need to think about how do people actually approach their work, what are the problems that they may have within that work, and then what are opportunities for which features can be built to connect into whatever problems or habits that they that they're experiencing, habits that they want to increase, habits that they want to decrease.
• 18:50
  SPEAKER_02: So I created what I call a habit diary study, and I call it habit because it was focused purely on the continuous or recurring behaviors that workers are experiencing as they're entering a hybrid environment where they were working both at home and at the office.
• 19:06
  SPEAKER_02: And then I call it a diary study because it was multiple interviews across, I believe it was four, four to six weeks.
• 19:14
  SPEAKER_02: The reason I chose that component is if you ask someone, tell me some behaviors that you've been doing at work that you don't want to do, you ask them once, they're gonna tell you what is the freshest on their mind.
• 19:25
  SPEAKER_02: They're gonna say, Oh, I just spent all day in email and that made me upset.
• 19:29
  SPEAKER_02: That just happened yesterday.
• 19:31
  SPEAKER_02: Maybe that's only happened once in the last month or two, but that's what they're choosing because it's present bias, it's front of their mind.
• 19:38
  SPEAKER_02: So I added the temporal component, and I think the benefit of a diary study is you ask that person the same question over four weeks, and you get to start to see a pattern.
• 19:48
  SPEAKER_02: With that temporal component, you're reducing present bias, and you get to see what are the actual habits that this person wants to change.
• 19:56
  SPEAKER_02: And so within this habit diary study, that's what we focused on.
• 20:00
  SPEAKER_02: We looked at what were the behaviors that an individual was beginning to form in a hybrid work environment that they wanted to change.
• 20:09
  SPEAKER_02: And those became a great opportunity for us to say that's the areas that we should go and build features because we knew that as hybrid grew, that we were actually going to be able to have features out there to help people satisfy their needs.
• 20:25
  SPEAKER_00: So literally the habits and getting into the lives of people and actually looking at what their problems are or what their pain or uh you know what they are looking at is pretty much far ahead of the need to build a product with features.
• 20:41
  SPEAKER_00: So it's almost like a pre-feature study, right?
• 20:45
  SPEAKER_00: Uh, which is really, I think that's really where the maximum value of behavioral science starts to uh add value to a company, right?
• 20:54
  SPEAKER_02: Completely.
• 20:55
  SPEAKER_02: Yeah.
• 20:56
  SPEAKER_02: Yeah, I would say for in the the before section of that, that that that's a huge opportunity for applied behavioral science.
• 21:04
  SPEAKER_01: So we have a bunch of individuals with uh expertise and know-how of uh behavioral science tools and techniques.
• 21:13
  SPEAKER_01: And let's say that you get all of them into a room and you put them in a free uh pre-feature setup, or you're getting them to work with the product teams to understand why a product is not working, you're you're sending them to work with customers to understand what objectives are not being met.
• 21:27
  SPEAKER_01: So there are many ways to actually deeply implement uh behavioral science uh into the layers of the company's operations.
• 21:36
  SPEAKER_01: So my question is when you put these people together as a team, you call them, let's say, a behavioral science team.
• 21:42
  SPEAKER_01: So my question would be what would be some of the intellectual skills that will be required to be a fine behavioral scientist?
• 21:49
  SPEAKER_01: And uh how do they really contribute?
• 21:51
  SPEAKER_01: How does the behavioral science team really contribute to business impact?
• 21:56
  SPEAKER_02: Yeah, yeah.
• 21:57
  SPEAKER_02: So so I'll parse that out into the in Individual and then the team.
• 22:02
  SPEAKER_02: I've done a lot more thinking about the individual, but I'm happy to touch on the team there too.
• 22:08
  SPEAKER_02: Applied behavioral science, we've talked about a lot, still very undefined.
• 22:11
  SPEAKER_02: And I'm trying to help to put some definition to the field.
• 22:15
  SPEAKER_02: And I started a group called the Applied Behavioral Scientist with a few other leaders in the field of trying to really set out what are some standards around what actually makes this field.
• 22:26
  SPEAKER_02: I think that that uh some people may have seen the data science Venn diagram, uh, which if I remember correctly, it has uh machine learning, uh programming, and statistical capabilities is is kind of fits what a data scientist is.
• 22:40
  SPEAKER_02: I feel like there was a seismic shift once it actually was like, oh, there's a way to define this thing that everybody is so excited about.
• 22:48
  SPEAKER_02: And I'm hoping we can do the same thing.
• 22:51
  SPEAKER_02: And in the interim, I've created my own Venn diagram for applied behavioral science.
• 22:56
  SPEAKER_02: And I think the three pieces are mixed methods research.
• 23:00
  SPEAKER_02: We talked about that, I'll touch on it again.
• 23:03
  SPEAKER_02: Uh domain expertise at the expertise, which is is understanding the behavioral science principles and understanding how the various concepts and how they come together.
• 23:17
  SPEAKER_02: I'll I'll touch on that a little bit more.
• 23:20
  SPEAKER_02: It also I believe includes having at least one industry expertise.
• 23:25
  SPEAKER_02: And and again, I can touch on that a little bit more, but I think that it's it's because uh behavioral science is very domain-specific.
• 23:34
  SPEAKER_02: Uh health behavioral science has its own intricacies, just as uh labor behavioral science does.
• 23:41
  SPEAKER_02: And the last piece is communication.
• 23:44
  SPEAKER_02: And and I'll start there with saying communication is is such an important piece for any researcher, but it's especially important for an applied behavioral science researcher, because as I said, I do see it in many ways as being this applied social scientist.
• 24:00
  SPEAKER_02: And and I have seen so many academic articles passed around businesses that people may, may just read the abstract, if that if anything, and that's it.
• 24:15
  SPEAKER_02: And it's because there's there is the average person doesn't have the time or the desire to dive into the the deep jargon that's needed to be able to understand average social science journal article.
• 24:31
  SPEAKER_02: So the role of the applied behavioral scientist is to read that paper and then distill the most actionable insights.
• 24:38
  SPEAKER_02: I truly believe that you lose people when you can't distill a set of insights into one PowerPoint slide.
• 24:47
  SPEAKER_02: I think that that, and maybe I I started my career in consulting, and so maybe that's a little bit of a bias, all about the slide presentations, but that is my focus is it I need to get at least one, if not two or three, insights onto that one slide.
• 25:03
  SPEAKER_02: And if I can't do that, I doubt that people are gonna really care that much.
• 25:07
  SPEAKER_02: Or or even more, maybe they'll care, but I doubt it will actually be actionable.
• 25:12
  SPEAKER_02: So communications, one of those pieces of the Venn diagram.
• 25:16
  SPEAKER_02: As I said, mixed methods is the next, and and we already kind of described what mixed methods is, but the piece that I'll hit on significantly is that the applied behavioral scientist has to be able to choose what is the best way to approach a research project, and then even more so has to be able to make the trade-offs that are required of doing research in an applied setting.
• 25:41
  SPEAKER_02: I should say what I see commonly happen is is academics that they'll go and get a PhD, maybe do a postdoc, and then they'll come to a company and be like, How do I how do I get any research done?
• 25:54
  SPEAKER_02: I have two tight timelines, I have the inability to actually control the environment, and it's because the reality is, is I have never seen a full-scale RCT be done by in-house researchers.
• 26:09
  SPEAKER_02: I've seen it as collaborations between professors and in-house researchers, but the average in-house researcher doesn't have the resources to do an academic level research project.
• 26:21
  SPEAKER_02: And that's just the reality of it.
• 26:23
  SPEAKER_02: It's because companies are not intended to create perfect level new knowledge.
• 26:28
  SPEAKER_02: They're intended to make money.
• 26:29
  SPEAKER_02: For that reason, an applied behavioral scientist or a mixed method researcher has to be able to make those trade-offs and say, okay, I have six months to get this done.
• 26:38
  SPEAKER_02: Well, I'm gonna have to throw out some of the things that are the gold standards of RCTs right away.
• 26:44
  SPEAKER_02: And now with those six months, I have to make the trade-offs that maybe I'll be able to control these variables or I'll be able to collect this sample.
• 26:52
  SPEAKER_02: Maybe maybe with six months I can do a random sample, but I'm still gonna have sampling bias here or there.
• 26:59
  SPEAKER_02: And there's there's a lot of these trade-offs.
• 27:01
  SPEAKER_02: So it comes with experience and with education to just be able to understand what is the best that I can do with the constraints that I have.
• 27:11
  SPEAKER_02: And I'll touch just briefly on the ethics piece of this because that's a question that comes up a lot is will they say, well, if you're doing that, then you're creating bad information.
• 27:20
  SPEAKER_02: And and I think we could have a scientific debate all day on creating evidence and creating research.
• 27:28
  SPEAKER_02: But my rule of thumb, my ethical guideline is if I'm creating knowledge that is better than nothing, then I feel I'm doing all right.
• 27:38
  SPEAKER_02: And and and so I want to make sure I don't create knowledge that's actually pointing in the wrong direction.
• 27:44
  SPEAKER_02: And if I think a research study is going to do that, that's when I will really put the brakes on it and say we can't do this.
• 27:50
  SPEAKER_02: But as long as the evidence that's being created is is gonna help the decision maker better than if they had nothing and we're just running on intuition, then I'm comfortable pushing forward with that.
• 28:01
  SPEAKER_02: So we talked about communication, we've talked about mixed method research, and the last piece is of the actual domain expertise.
• 28:07
  SPEAKER_02: For domain expertise, I see four main components of behavioral science.
• 28:12
  SPEAKER_02: I posted this on LinkedIn and I had so many different opinions come.
• 28:16
  SPEAKER_02: And so this is just Counter-Joyce's take.
• 28:19
  SPEAKER_02: But I believe that applied behavioral science includes choice architecture, which, if you've ever heard the book Nudge, that's that's all about choice architecture, uh, libertarian paternalism, nudges, choice architecture are all in the same wheelhouse.
• 28:34
  SPEAKER_02: There's biases and heuristics, which that's gonna be the um Amos Traversky and Daniel Kahneman's uh work.
• 28:41
  SPEAKER_02: Uh, it's it's encapsulated in the book Thinking Fast and Slow.
• 28:45
  SPEAKER_02: If you've ever seen those big diagrams with a bunch of different biases and stuff like that, you don't need to know as an applied behavioral scientist, you don't need to, I've never been quizzed where someone's just like, hey, what's present bias?
• 28:56
  SPEAKER_02: Or, you know, what's choice overload?
• 28:58
  SPEAKER_02: But you should have familiarity with those biases so that you know how to apply them when you're when you're thinking about creating messages or different interventions, etc.
• 29:07
  SPEAKER_02: The next one is habit formation.
• 29:10
  SPEAKER_02: And and there's been some great books that have been written on um uh you have like James Clare and the Habit Loop, uh, you have NIRAL and the hooked habit model.
• 29:21
  SPEAKER_02: Uh, there's a lot of different takes on habit formation, but but one should understand the basics of how do habits form and how do habits break.
• 29:31
  SPEAKER_02: And then the last piece that I think is really important is social norms.
• 29:34
  SPEAKER_02: And that's the newest of the of them all.
• 29:37
  SPEAKER_02: Um and but it's also the most fast growing because as a field in this domain, people are recognizing that you have to focus on not only the individual, but the context and the environment that they're in, and social norms does a great job with that.
• 29:55
  SPEAKER_02: So so to wrap up the individual, I think an applied behavioral scientist again is a good communicator, a mixed methods researcher, and then has domain expertise.
• 30:07
  SPEAKER_02: So a team is going to have to have those three capabilities.
• 30:14
  SPEAKER_02: And and it is sometimes you will have an individual who can who can do all three of those.
• 30:22
  SPEAKER_02: It is rare.
• 30:23
  SPEAKER_02: It is there are few people.
• 30:25
  SPEAKER_02: Humbly, I consider myself one of them, and it's because of the kind of odd career path that I had where I started in consulting, and that was where I learned how to really become a good communicator with taking advanced insights and turning them into slides ultimately.
• 30:42
  SPEAKER_02: Uh, I did a master's, and that's where I've got the domain expertise, and I've read a lot of books, attended courses, really kept up with the applied behavioral science field.
• 30:50
  SPEAKER_02: And then research has just come with the actual researcher roles that I've taken.
• 30:55
  SPEAKER_02: I believe there are definitely people out there.
• 30:58
  SPEAKER_02: Anytime that you see an individual applied behavioral scientist running their own team, they are likely kind of a unicorn where they have all three of those skills.
• 31:07
  SPEAKER_02: But until the field has a lot of master's programs, right now there's only like three or four, until the field has a solid pipeline, something that I'm trying to build with the Applied Behavioral Science Association, but that is still years away.
• 31:20
  SPEAKER_02: I think it's unfair to assume that there's a giant talent pool of people that have all three skills.
• 31:26
  SPEAKER_02: And so when thinking about a team, I see the team as having to comprise a group of people who you may have a specialized mixed method researcher.
• 31:37
  SPEAKER_02: You may have someone who is an applied behavioral science PhD that has a deep domain knowledge and very advanced knowledge of the field and can keep up with it.
• 31:47
  SPEAKER_02: And then you may have a great business analyst or someone who can distill all the insights from that group into simple insights.
• 31:55
  SPEAKER_02: So when looking at applied behavioral science teams, I think it's more of just taking what I just described for the individual and making sure all three have it.
• 32:05
  SPEAKER_02: The last piece, and I'm going to be writing about this soon, I'm excited to share more about this because I want to define a maturity curve for behavioral science teams.
• 32:16
  SPEAKER_02: Is I think that as an applied behavioral science team grows, it goes from just sharing behavioral science to actually applying behavioral science.
• 32:25
  SPEAKER_02: And once you hit that, you may want to pull on designers, you may want to pull on program managers and other people like that onto a behavioral science team to actually help take those insights and get them into whatever product is being built.
• 32:40
  SPEAKER_02: The next stage is experimenting with those insights, experimenting with whatever interventions are developed.
• 32:47
  SPEAKER_02: And again, as a team, if the company doesn't already have it, that team should be thinking about how do we build up our experimentation capabilities, how do we build up our A-B testing capabilities.
• 32:58
  SPEAKER_02: You may want to pull on people who aren't just researchers, but who actually understand how to get these large-scale tests done at scale.
• 33:06
  SPEAKER_02: It's much more difficult to deploy a research study that has a hundred thousand people compared to one that has a hundred.
• 33:12
  SPEAKER_02: And then the last piece is personalization, and that's that's where I kind of see the future of behavioral science going.
• 33:18
  SPEAKER_02: And at that point, you need to have everything I listed out either in that team or close close collaborators with that team, because it really takes a cross-departmental approach to be able to collect all of your data and then target interventions separately.
• 33:36
  SPEAKER_02: But then as it scales, as it desires to make more impact, it will have to pull on different resources too.
• 33:43
  SPEAKER_00: So, Connor, uh, one of the observations from whatever you are saying just now, it looks like a data scientist probably needs to know a bit of behavioral science.
• 33:53
  SPEAKER_00: And uh behavioral scientist needs to know a bit of data science, and uh the designer needs to know a bit of behavioral science because when you start collaborating, the knowledge flow between these professions become very critical.
• 34:07
  SPEAKER_00: And uh, would you think that's important for the success of getting the outcome of the behavior that we intend to achieve?
• 34:17
  SPEAKER_02: Yeah, so a hundred percent.
• 34:18
  SPEAKER_02: I could talk about this all day, and I don't want to get too theoretical, but I I really don't know if if applied behavioral science is really better to be just a specialization of those different groups.
• 34:31
  SPEAKER_02: So if we look at the Venn diagram I just described, that domain expertise piece could just be sprinkled across all of the other.
• 34:41
  SPEAKER_02: You could have a mixed method researcher that you just sprinkle on some domain expertise.
• 34:45
  SPEAKER_02: You could have a business analyst or another individual whose goal is just to create the sharing of information, have them have some behavioral science expertise.
• 34:55
  SPEAKER_02: And again, like you said, with designers, and and there is definitely potential for success of not having a single person that can do all those things and instead just empowering a team to do it.
• 35:04
  SPEAKER_02: I believe only time really is going to tell which is the better model.
• 35:08
  SPEAKER_02: Because I still see the argument that you'd need at least one individual who's gonna teach those people the the specialization, and so you may need someone to teach that.
• 35:19
  SPEAKER_02: And then B, you may still need an individual to kind of choreograph and make sure that there are parameters that are set up around how do we approach our mixed methods research?
• 35:32
  SPEAKER_02: What are some of the best ways of answering these behavioral related questions?
• 35:38
  SPEAKER_02: Um, so I don't have a solid answer, it's something I'm constantly thinking about.
• 35:42
  SPEAKER_02: Um, but I do agree with the premise of your question as a whole, that I I think that it will only improve the quality of products, especially products that intend to create behavioral outcomes, which most do, when people are empowered with this information, even if their primary business title isn't behavioral X.
• 36:03
  SPEAKER_00: If you look at uh some of the market leaders, literally they've been disrupted by small startup that probably uh you know takes away a big business because they have not seen the behavioral change that is happening with consumers.
• 36:20
  SPEAKER_00: So, for example, let's take the you know the watch industry, right?
• 36:25
  SPEAKER_00: And uh suddenly uh you've got the smartwatch coming in, and uh the big players uh have got disrupted because they never saw saw the smartwatch change coming in while consumers were wanting to have that kind of a product for large legacy corporations.
• 36:45
  SPEAKER_00: How can a behavioral science or a department build this uh scaffolding, if I were to call it, to ensure that they are at a step ahead in actually looking at the changes that are happening in habits?
• 37:00
  SPEAKER_00: As you rightly said, you did a habit study.
• 37:03
  SPEAKER_00: How do they start preparing themselves to so that they can reduce these kinds of disruptions?
• 37:08
  SPEAKER_02: Yeah, yeah, and I think that that it's so true.
• 37:10
  SPEAKER_02: I mean, we've seen this disruption, and I've seen some some great disruptors who are behaviorally inspired too.
• 37:19
  SPEAKER_02: I think Lemonade is an insurance company, at least here in America.
• 37:23
  SPEAKER_02: Um, I believe that they may operate outside of America too, but it's it's uh Dan Arielli played a role in it.
• 37:29
  SPEAKER_02: There was a few other applied behavioral scientists who who played a role in it, and and they've blown up as an insurance company.
• 37:36
  SPEAKER_02: I mean, they are very they've gotten much more popular, and it's because they they took a behavioral approach to insurance and and thought about how they can use the various more behavioral economics principles uh of how we evaluate costs and benefits, but utilize those and bring them to the insurance field.
• 37:57
  SPEAKER_02: And so I totally agree that that there is uh a lot of disruption happening just by companies, new companies, thinking about how to more properly solve these behavioral problems.
• 38:08
  SPEAKER_02: This said, I've also seen some some companies start to build behavioral science teams.
• 38:14
  SPEAKER_02: I have a friend who's gonna be starting, I believe I should say at this point, he's already started at Cars.com, and and they're a more historic company uh that is building out a behavioral science and experimentation team.
• 38:25
  SPEAKER_02: Um, and I think that's great for them because companies like Tesla that are coming in with their own direct-to-consumer model, uh much more focused on building a great experience, helping ensure people actually achieve whatever intentions they're trying to bring to that car buying experience.
• 38:45
  SPEAKER_02: Um and so this company is building an experimentation team, and I would say that that is the best approach.
• 38:51
  SPEAKER_02: Even even outside, I think again, here behavioral science is is auxiliary to the main piece.
• 38:59
  SPEAKER_02: The main piece is experimentation, I would say.
• 39:02
  SPEAKER_02: As a legacy company, if you're not building out ways to test with your users to see how you can better their experience, you're gonna get disrupted.
• 39:13
  SPEAKER_02: I at this point, I think it's inevitable.
• 39:16
  SPEAKER_02: I think the classic example I use with Lyft too, Lyft and Uber just totally disrupted the cab market because it it there was this experience that most people didn't really enjoy having to find a cab.
• 39:28
  SPEAKER_02: You get in, it might not be the most the best experience.
• 39:31
  SPEAKER_02: You have a bad instance, there's no one to call, or there's no reconcile, there's no good customer service.
• 39:37
  SPEAKER_02: And so companies come in and they say, we're gonna make this really simple.
• 39:41
  SPEAKER_02: You you call it on your phone, you're able to, we have customer service there, they've constantly enhanced their safety features, et cetera, et cetera.
• 39:49
  SPEAKER_02: And so, um, but how did those companies get there?
• 39:52
  SPEAKER_02: Uber and Lyft have some of the most built-out experimentation teams.
• 39:56
  SPEAKER_02: And so, again, as a legacy company thinking about this as much as it is going back to if you can do things like these habit diary studies or other sort of work where you're really getting a chance to investigate what are the problems, what are the habits that are forming or the habits that people want to break in your industry, and you're getting ahead of those by building new products and services and features that can address those, I think that's going to help prevent disruption.
• 40:24
  SPEAKER_02: Because if you're not doing it, someone else is going to be doing it and they're going to be finding those problems, they're going to be solving for them, and then people will gravitate towards those.
• 40:34
  SPEAKER_02: Um, so I believe at the end of the day, experimentation is what is necessary, and then sprinkling in behavioral science, thinking about how biases and heuristics lead to those problems and those habits, thinking about how the choice environment that someone's faced with is going to lead them to make certain choices.
• 40:52
  SPEAKER_02: Um, I could go down the road how social norms in that context are pushing someone to decide to choose something that may not be in their best, their best interest, where you could help alleviate those social norms and have them choose the whatever is best for their personal context.
• 41:09
  SPEAKER_02: Um, all of those sort of things could be thought about within experimentation.
• 41:14
  SPEAKER_02: But to even do that in the first place, you need to be able to experiment.
• 41:18
  SPEAKER_02: So if I could provide any advice to any legacy company thinking about trying to prevent disruption, it is establish some sort of experimentation wing and just start trying to figure out what types of problems you could be solving for your users or your customers.
• 41:44
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• 41:52
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• 41:56
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• 42:07
  SPEAKER_01: And now back to the show.
• 42:12
  SPEAKER_01: So just dovetailing from that point you made where you're talking about different skill sets and sort of really understanding what is the business requirement, what is the strategy that you're leveraging, and how do you bring design and development together, and then how do you test it out and iterate again and again and again.
• 42:28
  SPEAKER_01: Then uh having the entire behavioral science tools and techniques layered into the process.
• 42:33
  SPEAKER_01: What might Connor Joyce's behavioral science 101 curriculum look like?
• 42:38
  SPEAKER_01: Because if you're talking about applying these things into the industry, we gotta have some context of how to do it in the first place.
• 42:44
  SPEAKER_02: Yeah, it's a great question.
• 42:46
  SPEAKER_02: I spend a decent amount of my time more thinking about, we'll say like a 201 of specifically focused on trying to get interventions to be more personalized.
• 42:58
  SPEAKER_02: But I've deployed a couple of these sort of one-on-ones, both at Microsoft and now at the company I'm at Tonal, uh, with with my colleagues trying to just disseminate behavioral science knowledge.
• 43:11
  SPEAKER_02: And so that was where I got this idea of the four main pillars of behavioral science.
• 43:16
  SPEAKER_02: Again, choice architecture, biases, heuristics, habit formation, and social norms.
• 43:23
  SPEAKER_02: I think that those four are the necessary entry to consider yourself a domain understanding, and then getting deeper knowledge in that is when one becomes more of a domain expert.
• 43:37
  SPEAKER_02: Um, so I think that that those are going to be essential.
• 43:41
  SPEAKER_02: I think that there's also some great frameworks out there that that I I can't take credit for, but that greater minds have created, like um Behavioral Insights team.
• 43:51
  SPEAKER_02: A lot of the work that behavioral insights team creates is is great.
• 43:54
  SPEAKER_02: They've got a lot of a lot of good content for introductory.
• 43:57
  SPEAKER_02: Same with a company called Ideas42.
• 44:00
  SPEAKER_02: They have a lot of tools and and workbooks.
• 44:03
  SPEAKER_02: They're a nonprofit.
• 44:04
  SPEAKER_02: So they they have a lot of great free resources.
• 44:09
  SPEAKER_02: They're one of the partners with the Applied Behavioral Science Association, too, so I can only have only say good things about them.
• 44:15
  SPEAKER_02: But this being said, of actual frameworks that are are really really helpful.
• 44:21
  SPEAKER_02: I think that there's a um there's a book called Designing for Behavioral Change by a gentleman named Steve Wendell.
• 44:30
  SPEAKER_02: Steve created the behavioral science team at Morningstar.
• 44:35
  SPEAKER_02: He's no longer there, but he uh but he created this what I look at as the gold standard of a behavioral science team.
• 44:45
  SPEAKER_02: And I think that it's only even though he's left, it's only proven to be true because what I've found is a lot of the behavioral science teams that exist, they actually generally s stop existing once the leader leaves.
• 44:58
  SPEAKER_02: And I've seen that with a few companies at this point, and Steve's is still going strong, even though he left.
• 45:06
  SPEAKER_02: And the same thing with uh at Allstate.
• 45:09
  SPEAKER_02: They also have Allstate's insurance company, too.
• 45:12
  SPEAKER_02: They have a behavioral science team uh by a gentleman named Florent.
• 45:16
  SPEAKER_02: And even though Florent has left, that team is still going, and I think that that's a great example of teams that they're doing enough good work that they remain, the company wants to make sure that they remain even when the manager leaves.
• 45:29
  SPEAKER_02: Um, but anyway, to address your question, Steve wrote a book designing for behavioral change, and he has a framework in it called Define, which I can't remember each step of it, but uh it starts with it's all about defining the problem and creating an intervention to solve for that.
• 45:48
  SPEAKER_02: And so in an aggregate, on top of having domain expertise, I think that teams need to that that any company that's trying to do this sort of work needs to either go and find that defined model or a model similar to be able to start with identifying an area of interest.
• 46:08
  SPEAKER_02: We want our users to be doing this because it leads them to this positive outcome, or we want users to be doing this specific behavior, which we can measure because we know it leads to this specific user outcome that we know users want.
• 46:28
  SPEAKER_02: It's those intentions that they're bringing to the product.
• 46:31
  SPEAKER_02: And even better when you can say we it leads to that this user outcome that we know when it's satisfied, people buy our product more or they continue to use it.
• 46:40
  SPEAKER_02: So, again, the specific measurable behavior connects to the user outcome, which then connects to a business outcome.
• 46:48
  SPEAKER_02: If you can, if you can identify one of those and you're like, okay, we want users to do more of this specific behavior, then you can go and consult a framework like that defined framework or or the many others that are out there that exist to say, say, how do we define this context?
• 47:07
  SPEAKER_02: How do we figure out the best way that we can deploy an intervention?
• 47:13
  SPEAKER_02: This an intervention can be a nudge all the way through a policy or something, but to have that individual begin to exhibit that behavior, which again in turn is going to lead to more sales or increased retention, etc.
• 47:29
  SPEAKER_02: So it's the domain expertise mixed with the ability to deploy interventions.
• 47:34
  SPEAKER_00: So, Connor, uh, if uh I were to request you to apply your uh behavioral science lens and look at the failure of, say, Google Glass as a product.
• 47:46
  SPEAKER_00: Or it could be even Google Plus.
• 47:48
  SPEAKER_00: There must have been some user studies, some habits, and it's a very interesting experiment.
• 47:53
  SPEAKER_00: There's always a lot of talk about successful experiments, but if I were to take experiments that did not work well, what could have been done better?
• 48:02
  SPEAKER_00: And how do you think uh you know we could avoid some of these uh mistakes?
• 48:08
  SPEAKER_02: First of all, it's one thing to have a phone in your pocket and to have that away from the world, we'll say, and then you can pull you pull it out when you need to.
• 48:18
  SPEAKER_02: It's another to take a, and I'm wearing glasses and I'm holding my glasses right now because it's another thing to say, I'm going to take this technology and put it on my head, and I'm going to begin to use this technology in a way that that everyone can see on a daily basis.
• 48:38
  SPEAKER_02: And I think that that is actually one of the biggest things that's stopped Google Glasses.
• 48:44
  SPEAKER_02: And I'm really excited to see how Meta approaches this because Meta has all these ambitions to create uh AR glasses.
• 48:52
  SPEAKER_02: And you know, Snap actually, they did their Snap spectacles.
• 48:56
  SPEAKER_02: Snap does a good job overall connecting with the hip crowd and kind of making sure that whatever they're building connects into influencers, connects into popular culture.
• 49:08
  SPEAKER_02: Google didn't really do an attempt at that.
• 49:10
  SPEAKER_02: They kind of did.
• 49:11
  SPEAKER_02: I actually applied for this.
• 49:12
  SPEAKER_02: It's funny you used the Google Glass.
• 49:14
  SPEAKER_02: I applied for that.
• 49:15
  SPEAKER_02: I was in college, I was in my undergrad when they they first released it, and they said, we're gonna do this program where if you can go out and become a brand ambassador for Google Glass, you can go out and you can you can talk about how much you love it, you can show it off to everyone.
• 49:32
  SPEAKER_02: You know, we'll send you, and you can be one of the first early adopters of it.
• 49:35
  SPEAKER_02: And I applied for it because I was like, oh, I'm on campus.
• 49:37
  SPEAKER_02: I was student body president at the time, and I was like, I'll wear them to all the meetings so all the students see uh what Google Class does.
• 49:45
  SPEAKER_02: So they tried.
• 49:46
  SPEAKER_02: Um, you know, who knows why why that program didn't take off?
• 49:50
  SPEAKER_02: It may have truly just been a little too early for its time.
• 49:53
  SPEAKER_02: But but the social norms are are very, very important in something that is so publicly visible.
• 49:59
  SPEAKER_02: If it is not a norm to wear technology on your face, it is going to not be desirable because people are gonna feel weird doing it.
• 50:11
  SPEAKER_02: And and social norms again play such a big role.
• 50:14
  SPEAKER_02: It it is it's social norms are why it's appropriate to use an umbrella when it rains in most cities, but it's kind of weird to do it in Seattle, where I'm based at, because it rains so much in Seattle, people just wear rain jackets, and that and you it's uncommon to see people wearing umbrellas.
• 50:32
  SPEAKER_02: So it's it's funny how social norms really do dictate what is common and uncommon, and to wear technology on your face is just it's it's uncommon at this point.
• 50:43
  SPEAKER_02: So so any company that that approaches a product like that is going to have to first address that social norm and then build a great product.
• 50:54
  SPEAKER_02: I wear a ring that's called the Aura ring, big fan of it.
• 50:57
  SPEAKER_02: It does sleep tracking.
• 50:59
  SPEAKER_02: This is a great example.
• 51:00
  SPEAKER_02: You know, you can look up Aura, I assume it's Aura.com, O-U-R-A, and it's a very sleek ring that does a very practical thing.
• 51:11
  SPEAKER_02: It's something I wear every day, even though I only use it for my sleep tracking, but it's because it's a stylish ring.
• 51:17
  SPEAKER_02: And no one knows unless I pull it off and I show them that it has little sensors in it.
• 51:22
  SPEAKER_02: No one knows it's a sleep ring.
• 51:24
  SPEAKER_02: They just think, oh, that's just a cool Onyx ring that that guy's wearing.
• 51:27
  SPEAKER_02: Either they can address us the social norms of wearing the technology, if it's gonna have a big camera on it, or if it's gonna be thick and bulky, like I believe uh the Google Glasses were, they're gonna need to address that social norm, or they're gonna have to make it so sleek that you don't even know that it's a it is a wearable technology, similar to what the aura ring has.
• 51:50
  SPEAKER_02: The second piece that I'll just touch on is kind of that intentions is when it came out, to my knowledge, it was more hard to use than a smartphone.
• 52:00
  SPEAKER_02: And and if and if we think again about what are the intentions that most people bring to their smartphone usage, other than the rare cases of exploring behaviors or tools that people are using on their cell phone, they are using their cell phone as a means to an end.
• 52:15
  SPEAKER_02: I am using my cell phone to contact my wife to tell her that I'm gonna pick up dinner tonight.
• 52:22
  SPEAKER_02: Or I am using my cell phone to email my colleague to let them know that the project will be done at 5 p.m.
• 52:29
  SPEAKER_02: today.
• 52:29
  SPEAKER_02: The cell phone is a means to an end.
• 52:32
  SPEAKER_02: And and when you're looking at that's the intention that I'm bringing to whatever apps that I'm using, if I look at why am I not using the glasses, which I need to use a little doggle that's up by my ear, and I'm looking to my upper right to try to find the app that I'm looking for, whatever it might be, no wonder people just kept using their cell phones because it's a familiar interface that's faster and it's helping actually satisfy that intention that I'm bringing to device much better than the pair of glasses are.
• 53:00
  SPEAKER_02: So, in summary, they need to break the social norms and then they need to make it easier to use than a cell phone.
• 53:06
  SPEAKER_00: And when you contrast that with, say, a smartwatch, there's no social norm really because you just used the watch for looking at the time or things like that.
• 53:17
  SPEAKER_00: So, where do you think the social norm of a smartwatch or an Apple Watch, uh there was a tipping point for it to actually get adopted so much?
• 53:26
  SPEAKER_00: Because you never saw anybody wearing a watch tracking all the behavior and uh you know their fitness and things like that.
• 53:33
  SPEAKER_00: Was not a norm.
• 53:34
  SPEAKER_02: Yeah, it's it's a great question.
• 53:36
  SPEAKER_02: It's what Apple does really well too, and so that's why I I am as much as I'm excited to see whatever Meta creates, I'm even more excited to see what Apple creates once they create some sort of AR glasses because they are great at it.
• 53:50
  SPEAKER_02: So again, I remember back in my undergrad we had a hackathon, and the the first prize for the hackathon, or maybe it was the first one of the prizes for the hackathon, was a set of pebble watches.
• 54:04
  SPEAKER_02: I remember going, what is this pebble watch?
• 54:07
  SPEAKER_02: And then finding that it was a very simple, I think it was just black and white screen.
• 54:12
  SPEAKER_02: Maybe it was color, but it was a it's a very, very simple wristwatch that was the original smartwatches.
• 54:20
  SPEAKER_02: And I was I was like, wow, this is an uh interesting concept.
• 54:23
  SPEAKER_02: And I'll actually I'll never forget one of the people who won the hackathon, they took one of those Pebble smartwatches and they imported a I think it was Chipotle.
• 54:34
  SPEAKER_02: Chipotle was one of the first companies to make like an online ordering app, and they somehow were able to re-engineer the Chipotle Android app into this Pebble smartwatch.
• 54:46
  SPEAKER_02: And and I remember they won the hackathon because it was pretty cool.
• 54:48
  SPEAKER_02: You could order a burrito off your your smartwatch, and it was such a novel idea, right?
• 54:53
  SPEAKER_02: At that point.
• 54:53
  SPEAKER_02: I mean, now it I assume that Chipotle allows you to do that with an Apple Watch app, but to do that with a Pebble like eight years ago was pretty fascinating.
• 55:01
  SPEAKER_02: Ultimately, Apple was the one who really helped that tipping point.
• 55:05
  SPEAKER_02: It's just now the Pixel Watch came out, right?
• 55:08
  SPEAKER_02: And it's been how many years have Android fans been asking for a Pixel Watch?
• 55:12
  SPEAKER_02: Because even when Google Watch OS opened up and you had companies like uh Skoggin, if I'm saying them correctly, they're they're a nicer watch company that creates an Android watch.
• 55:22
  SPEAKER_02: Fossil also did a few, but you've had these companies that are trying brand names, the ones that you used to go, oh wow, you're wearing that brand watch, them create with smart watches, and it never took off.
• 55:34
  SPEAKER_02: Whereas as you had the Apple Watch just really, really take off.
• 55:38
  SPEAKER_02: I'm actually writing a uh Apple Watch case study right now because I love their fitness piece.
• 55:44
  SPEAKER_02: And so I'll touch on that.
• 55:45
  SPEAKER_02: I don't think it fully addresses your question.
• 55:48
  SPEAKER_02: And I'll I'll admit I just actually switched over to iOS a year ago, so I'm I'm still getting familiar with the whole Apple ecosystem and the joys of having an Apple Watch.
• 55:59
  SPEAKER_02: They made it sleek enough and had the accessories that that allows an individual to wear a really nice watch band with their smartwatch, making it so it doesn't look that different than a regular watch.
• 56:17
  SPEAKER_02: And I think that that plays a big role.
• 56:20
  SPEAKER_02: I also think so actually I'll use the Google glasses.
• 56:23
  SPEAKER_02: So again, when we said with glasses, what was the social norm was it was weird because it was this big, bulky pair of glasses.
• 56:30
  SPEAKER_02: The watch doesn't look that different than a regular watch.
• 56:33
  SPEAKER_02: I think that that's and then the other piece was that the Google glasses weren't that practical, that it was still easier to do it on your smartphone.
• 56:40
  SPEAKER_02: It's actually easier to get things done on the watch at times than it is on a smartphone.
• 56:44
  SPEAKER_02: And so they've made it very practical.
• 56:46
  SPEAKER_02: They've made it very functional, we'll say.
• 56:48
  SPEAKER_02: I think those are the two big contributing factors.
• 56:50
  SPEAKER_02: There, and this is more of a secondary effect, but I think is why it only continues to grow, and that there's so much more potential with Apple Watch still, is the three rings within the activity tracker.
• 57:04
  SPEAKER_02: I I think is one of the best design elements I've ever encountered.
• 57:08
  SPEAKER_02: It takes complex information, calories burned, minutes doing vigorous activity, and hours that I've stood for at least one minute, and it condenses it into a very easy to understand ring.
• 57:25
  SPEAKER_02: And which also a ring is a great thing because it relies on the principle of closure, which is something that's innate to humans.
• 57:32
  SPEAKER_02: We like closure, and so now you're taking these elements of an intention that I have of me wanting to work out more, and you're distilling it into a design element that encourages me to follow through with those intentions because as a human, I say, I want to close these rings, I want to do whatever is necessary to ultimately close those rings.
• 57:55
  SPEAKER_02: And then you take it even more, and what can I do?
• 57:57
  SPEAKER_02: I can share my rings with my friends.
• 58:00
  SPEAKER_02: So now my friends know when I'm closing my rings.
• 58:03
  SPEAKER_02: A buddy of mine had an arms race for a little while where we each raise our calories for how many calories we would burn in a day by 10.
• 58:12
  SPEAKER_02: So he raised it 10, I raised it 10, he raised it 10 to the to the point now that I think mine is almost it's so it's 1,060 calories.
• 58:22
  SPEAKER_02: So we we went back and forth and and now but what did it do with that that race now made it?
• 58:28
  SPEAKER_02: So I push myself pretty hard now to ensure that I close those rings every day, and it's and it's helping me maintain my weight, which again was the intention of why I ever wanted to engage with a fitness tracker in the in the first place.
• 58:42
  SPEAKER_02: So um so bringing it back home, I would say that um from my view that the the the watch has taken off, thanks to Apple, of their good design, mixed with making it a very functional device for the intentions that people bring to why they want to wear a fitness tracker like that.
• 59:02
  SPEAKER_01: I think it's great that you even touched upon the Apple Watch as an example because I think the two key elements to what makes a product like the Apple Watch successful are the concepts of choice and behavior.
• 59:15
  SPEAKER_01: People choose to be healthy, people choose to be fit, people choose to wake up on time.
• 59:20
  SPEAKER_01: So when they make that choice, the choice has to be backed by a behavior.
• 59:24
  SPEAKER_01: So it's almost like they're allowing people to choose the behaviors that they want, and they're giving them the nudges on how do they get to that behavior or that desired state.
• 59:35
  SPEAKER_01: So, speaking of nudges, can you talk a little bit about how the theory of nudges or how choice architecture actually came into play?
• 59:44
  SPEAKER_01: And why is it such a core cornerstone sort of a framework or an idea that is at the heart of behavior science?
• 59:52
  SPEAKER_02: Yeah, yeah, Vignesh, you said it so well too.
• 59:54
  SPEAKER_02: I I love that is that it is choice is such an important thing.
• 59:59
  SPEAKER_02: We as humans love autonomy and agency, and and so giving us the opportunity to choose something, but then help me follow through with it is is huge.
• 1:00:11
  SPEAKER_02: And and then and that really does, it's a great introduction to the principle of nudge.
• 1:00:17
  SPEAKER_02: So nudge is this book, it's I should say it's been around for a while, but the actual nudge of our current age started with a book that came out in the early 2000s with a gentleman named Richard Daylor and another gentleman named Cass Sunstein.
• 1:00:32
  SPEAKER_02: And they're they're the grandfathers of Nudge.
• 1:00:36
  SPEAKER_02: And their philosophy was libertarian paternalism.
• 1:00:41
  SPEAKER_02: And so libertarians are are people who believe in small government, free agency, let people be themselves.
• 1:00:48
  SPEAKER_02: Here in America, we've got a big it's actually really big libertarian kind of underpinning if you look at the history of the country.
• 1:00:57
  SPEAKER_02: And then paternalism is the belief that a government or a larger body should give you instructions on how to act.
• 1:01:08
  SPEAKER_02: Paternalism, you have parent in the name.
• 1:01:11
  SPEAKER_02: It's what parents generally do, is they give you structure, they give you, they sometimes force you to choose the best option, etc.
• 1:01:17
  SPEAKER_02: So those two those two words come together, and you're like, well, don't they contradict themselves?
• 1:01:22
  SPEAKER_02: And they do to a degree, but but when you put them together and you give a spin, then it ultimately becomes nudge.
• 1:01:29
  SPEAKER_02: And it's saying give people the option to choose whatever option they want, but focus and create the choice environment, or as they call the choice architecture, so that one decision is easier, more highlighted, more accessible, more obvious, more et cetera, than all the other options.
• 1:01:52
  SPEAKER_02: And that's the paternalism part.
• 1:01:54
  SPEAKER_02: So libertarian is being able to choose any option, paternalism is the choice architect choosing one option to focus on and encourage people to choose.
• 1:02:03
  SPEAKER_02: And so the theory of nudge at its core is just any different tweaking of the choice environment to focus on one option over all of the others.
• 1:02:16
  SPEAKER_02: The classic example of this that was in Nudge was the use of defaults in organ donations.
• 1:02:23
  SPEAKER_02: So if you ask the average person, do you want to be an organ donor?
• 1:02:27
  SPEAKER_02: It helps save people's lives.
• 1:02:29
  SPEAKER_02: The average person's probably gonna say, I think, I think I wanna do that.
• 1:02:33
  SPEAKER_02: And and they did surveys that showed the average person is interested in being an organ donor.
• 1:02:38
  SPEAKER_02: But when you look at the rates of organ donation, people who have said, I will be an organ donor, it was significantly lower.
• 1:02:45
  SPEAKER_02: And so they investigated, well, why is that?
• 1:02:47
  SPEAKER_02: And they found, well, when do you indicate that you are going to be an organ donor?
• 1:02:52
  SPEAKER_02: Here in America, it's when you go to the DMV.
• 1:02:55
  SPEAKER_02: And in a lot of other countries, it's when you're going to get your motor's license.
• 1:02:59
  SPEAKER_02: And and the DMV, especially here in the States, is known as a place you want to get out of as quickly as possible.
• 1:03:07
  SPEAKER_02: You just want to do your thing and you want to get out of there.
• 1:03:10
  SPEAKER_02: And so any additional friction or any additional barrier that you're gonna face at the DMV, you're not gonna give your full conscious effort to.
• 1:03:20
  SPEAKER_02: You are going to do whatever is possible to get it over with as quick as possible.
• 1:03:25
  SPEAKER_02: The organ donation, they ultimately took the default of so I'm if I'm getting my license, it'll ask me a question, Connor, would you like to be an organ donor?
• 1:03:34
  SPEAKER_02: And most people, the the default there is no, because you're asking me a question that I have to provide additional effort to ultimately agree with.
• 1:03:46
  SPEAKER_02: And so I just say no and move on because I'm trying to get out of the DMV.
• 1:03:50
  SPEAKER_02: And so the simple nudge that they did to tweak that environment was to change the default to yes by asking the question instead, Connor, do you want to opt out of being an organ donor?
• 1:04:04
  SPEAKER_02: And by saying that opt-out rather than an opt-in, they saw the rates of organ donation skyrocket.
• 1:04:12
  SPEAKER_02: I can't remember the specific numbers, but we're talking it went from 10, 20% to 80, 90%.
• 1:04:19
  SPEAKER_02: And so that is the canonical example of a nudge of a simple reframing of a step in a process that yielded significant behavior change.
• 1:04:30
  SPEAKER_02: I'll give one other brief example, the one that I love.
• 1:04:33
  SPEAKER_02: Um, and it's funny, my my wife now works at Google, and so I've I've been asking her questions about their cafeteria.
• 1:04:40
  SPEAKER_02: But there Laszlo Bach was Google's chief people officer for a while.
• 1:04:44
  SPEAKER_02: And in his book, uh, I think it was called Work Rules, he talks about this classic example that's been written about a fair amount now, which is at the Google campus, they wanted to encourage people to eat less calories.
• 1:04:56
  SPEAKER_02: And everybody knows now that Google's known for their like great food in their offices.
• 1:05:01
  SPEAKER_02: But what does free food mean?
• 1:05:04
  SPEAKER_02: It means that there's generally increased consumption because you're like, ah, I'm hungry.
• 1:05:08
  SPEAKER_02: Well, there's no cost, so I'll go and grab a handful of MMs, a Twix bar, I'll grab a cookie over here, maybe I'll grab some chips.
• 1:05:15
  SPEAKER_02: But they wanted to reduce the calorie intake while still giving the full spread of options to individuals.
• 1:05:22
  SPEAKER_02: And so what they ultimately did was they put healthier options at eye level and within simple reach of any individual that came into the kitchen, where they took the MMs and the more unhealthy options and either put them behind cabinets or above eye level so that people would know it would not be the first option that came to mind.
• 1:05:44
  SPEAKER_02: And they found that simple reorganization of the choice environment led people to eat less calories.
• 1:05:51
  SPEAKER_02: And so again, libertarian paternalism, they gave all options were available, but they reorganized it in a paternalistic way that encouraged.
• 1:06:00
  SPEAKER_02: People to eat less calories.
• 1:06:02
  SPEAKER_02: And so, what are nudges ultimately?
• 1:06:05
  SPEAKER_02: They are those changes to the choice environment, and we see them all over the place now.
• 1:06:11
  SPEAKER_02: We see them in the notifications that we give.
• 1:06:15
  SPEAKER_02: I just released an A-B test at the company Tonal that I'm working at, where we were using different nudges.
• 1:06:21
  SPEAKER_02: We were using these different behavioral principles, one loss aversion, utilizing the fact that people are more likely to take action to prevent losses than they are to achieve gains.
• 1:06:32
  SPEAKER_02: We used social norms, the fact or social proofs, I should say, which is utilizing the fact that if you tell someone, hey, a lot of your friends are taking this activity, they're more likely to take that activity.
• 1:06:45
  SPEAKER_02: We use different behavioral principles like this in push notifications that we did, reminding people to work out.
• 1:06:51
  SPEAKER_02: And so those are nudges that are able to be leveraged, where they're actually bringing a new piece of information to an individual's choice environment, reminding them of an opportunity to go and follow through with their intentions, which in this case was working out.
• 1:07:10
  SPEAKER_02: And so we see nudges being leveraged again across a lot of the different technology that we use nowadays.
• 1:07:19
  SPEAKER_02: And yeah, the one piece that I want to condition or that I want to qualify this discussion with nudges with is that the defaults example with organ donation, huge, huge change, super simple.
• 1:07:31
  SPEAKER_02: Another classic example is with Choice Overlord and 401ks, where Vanguard was able to increase the amount of people subscribing to their 401ks by actually reducing the amount of choices of different investment opportunities.
• 1:07:46
  SPEAKER_02: The Google example that I gave, those are all very classic, very easy nudges that led to substantial changes in behavior.
• 1:07:56
  SPEAKER_02: The reality that we are beginning to see, and that again, I believe the future of behavioral science is personalization, is that most choices are not that easy, unfortunately.
• 1:08:08
  SPEAKER_02: If only it was these nudges could have that big of an impact.
• 1:08:11
  SPEAKER_02: Instead, is what we're realizing is that there are generally different types of users.
• 1:08:17
  SPEAKER_02: It doesn't have to be personalized down to the individual, but down to the archetype, or we'll say cluster of users, that different clusters of users respond to different messages better.
• 1:08:31
  SPEAKER_02: And I believe that that is actually the future of Nudge is that we'll have 10 different options and six different clusters, and we'll know cluster one responds to option A and option C the best.
• 1:08:46
  SPEAKER_02: Cluster two responds to option B and option E the best down the list.
• 1:08:52
  SPEAKER_02: And that is again kind of putting a bow to this whole conversation is why experimentation is really needed, why having lots of different domain knowledge and being able to try out different types of tests is all of that is so important, or I should say, different types of behavioral concepts, all of that is so important to know ultimately what types of users are going to respond to which types of nudges or which types of interventions the best?
• 1:09:21
  SPEAKER_00: How important is the cultural aspects of different geographies and different countries?
• 1:09:28
  SPEAKER_00: How do behavioral scientists work on that?
• 1:09:30
  SPEAKER_00: Because uh, I wouldn't know the cultural aspects of, say, the US, you wouldn't probably know as much about the South Asian culture.
• 1:09:38
  SPEAKER_00: So, how important is culture as a key ingredient in these interventions?
• 1:09:44
  SPEAKER_02: Yeah, it's huge.
• 1:09:45
  SPEAKER_02: It's it's huge, and it's an area that I'll be honest I don't know as much about.
• 1:09:49
  SPEAKER_02: Uh there are there are many others that are that have much greater knowledge on the different aspects of culture.
• 1:09:56
  SPEAKER_02: Uh, you know, the one that I've just experienced uh the most firsthand from my travels is the difference between individualistic and collectivistic cultures.
• 1:10:08
  SPEAKER_02: And that alone, there are some significant differences.
• 1:10:12
  SPEAKER_02: I actually wrote about one, and so I'll give this quick anecdote that I I thought was was really interesting.
• 1:10:17
  SPEAKER_02: So in in weight loss, in individualistic countries, we talk a lot about us.
• 1:10:23
  SPEAKER_02: It's weight loss is about you, it's about it's about you want to tell your friends, you want to you want to show that you as an individual have agency over your life.
• 1:10:33
  SPEAKER_02: You want to show that you are uh able to achieve your goals because you're powerful.
• 1:10:39
  SPEAKER_02: You, you, you, you, you.
• 1:10:41
  SPEAKER_02: But I was talking with my friend when I was when I was in India about her weight loss journey and and and being in a collectivistic culture.
• 1:10:49
  SPEAKER_02: She was talking about how for her, when she tells people she's losing weight, that it's actually embarrassing and that there's a lot of stigma that's associated with it because it's almost like why are the people around you allowing you to be unhealthy?
• 1:11:06
  SPEAKER_02: You recognize you're unhealthy, and that your community, your family, they're allowing you to keep eating.
• 1:11:13
  SPEAKER_02: They're allowing you to become this way.
• 1:11:15
  SPEAKER_02: And so she's on a weight loss journey right now, but she has to keep it very private because she doesn't want anyone to call her out, or even worse, call her family out.
• 1:11:26
  SPEAKER_02: And I should say she's a little bit of a younger person too.
• 1:11:28
  SPEAKER_02: So her family, the types of food and things like that do play a role in her choices.
• 1:11:34
  SPEAKER_02: And so we're in the individualistic culture, I don't see that playing out as much.
• 1:11:39
  SPEAKER_02: In that found it fascinating in the collectivistic culture, it makes sense since the family unit has such an important place in the community, whereas the individual is just so much bigger in like a Western culture like America.
• 1:11:53
  SPEAKER_02: Um, so just that one dimension, just one, just individualistic versus collectivistic, is playing a huge role in how we, how I, as a behavioral scientist at a at a company that's trying to help people achieve their fitness goals, has to think about how I would communicate to that.
• 1:12:10
  SPEAKER_02: Because a lot of the interventions that I'm thinking about deploying involve building community, involve helping connect people to others, sharing their journey, sharing their message, sharing their progress.
• 1:12:23
  SPEAKER_02: But all of those will probably not be that useful to someone in a collectivistic culture.
• 1:12:29
  SPEAKER_02: I believe it's Hofstedter.
• 1:12:30
  SPEAKER_02: I've been trying to think of who created these original cultural differences.
• 1:12:35
  SPEAKER_02: If you look up individualistic or collectivistic, that's just one dimension of many dimensions.
• 1:12:40
  SPEAKER_02: You know, when I was at Microsoft, I got to think about some of these things.
• 1:12:44
  SPEAKER_02: Um, I didn't have as much opportunity to test on them.
• 1:12:47
  SPEAKER_02: I'm doing a lot more testing in my current job at Tonal, but right now, Tonal is a startup that's only in America, and so I don't have to think about these larger cultural characteristics as much.
• 1:12:57
  SPEAKER_02: Um, as we expand, I'm excited to start to broach these subjects with weight loss and other fitness journeys.
• 1:13:03
  SPEAKER_02: Added aggregate, cultural uh uh differences play a really big, really big role.
• 1:13:08
  SPEAKER_02: And and the last piece I'll say on it is that there's this new philosophy of weird psychology.
• 1:13:15
  SPEAKER_02: And weird stands for Western, educated, industrial.
• 1:13:19
  SPEAKER_02: I can't remember what the R is.
• 1:13:21
  SPEAKER_00: Rich and Democratic.
• 1:13:23
  SPEAKER_02: Yes, rich and democra democratic.
• 1:13:26
  SPEAKER_02: Yes, thank you.
• 1:13:27
  SPEAKER_02: So so does and it's so true that so much of our understanding of psychology was done on college students that match all of those characteristics that we just talked about.
• 1:13:38
  SPEAKER_02: Even the science for which an individual like me bases my assumptions may have some cultural bias in it.
• 1:13:46
  SPEAKER_02: And if that's the case, we got a lot of work to do to really understand what is the best way to nudge a female in India who's on a weight loss journey.
• 1:13:54
  SPEAKER_02: There's a lot of work to uh to do in the cultural aspect.
• 1:13:59
  SPEAKER_00: Thanks, uh thanks, Connor, and uh a couple of our last uh rapid fire questions.
• 1:14:05
  SPEAKER_00: Uh, what does success mean to you?
• 1:14:07
  SPEAKER_02: Success ultimately means to me of contributing to this field and contributing to helping people actually achieve the outcomes that they bring to products.
• 1:14:20
  SPEAKER_02: I I read once about the triple win of behavioral science that's helping people achieve their user outcomes.
• 1:14:27
  SPEAKER_02: It's helping the business be able to continue increasing retention and selling products through achieving those outcomes, and then it makes the world a better place because people are achieving their outcomes.
• 1:14:39
  SPEAKER_02: And so for me, if I'm doing work that's helping with that triple win, it's it's success to me.
• 1:14:45
  SPEAKER_02: On top of enjoying the journey.
• 1:14:47
  SPEAKER_01: My follow-up question, Condor, is what are some of the books that have influenced you the most over the course of your life and career?
• 1:14:54
  SPEAKER_02: Yeah, definitely um the book that kickstarted this whole journey for me was Misbehaving, which is also by Richard Thaler.
• 1:15:02
  SPEAKER_02: Um, it's it's not as known as Nudge, but it talks about Thaler's journey, being an economist, asking all of these behavioral questions, and thinking, why are people not investigating some of these more in-depth and creating his career around that?
• 1:15:24
  SPEAKER_02: And and I I to a degree had a similar journey with being in consulting, doing management consulting, where we were doing these large-scale change programs, seeing some of the flaws and the approaches and saying, why are we not doing more of this behavioral science work?
• 1:15:39
  SPEAKER_02: And so uh I just it really connected with me.
• 1:15:42
  SPEAKER_02: Uh, and then the other one is Thinking Fast and Slow, which is one of the most in-depth behavioral science books that's out there, uh, just because it made me left me with realizing there's so many more questions that I had and so many more opportunities to explore.
• 1:15:58
  SPEAKER_02: And it ultimately led me to say, well, I gotta go and get a master's in this because there's there's so much more for me to learn in this field.
• 1:16:04
  SPEAKER_00: So, what would be the one piece of advice that you would give to an 18-year-old who is in a university today?
• 1:16:11
  SPEAKER_02: Really, the biggest thing that I've realized over my life, and it was probably the best advice that I was given in college that I've really seen is true.
• 1:16:22
  SPEAKER_02: It's important to follow what interests you in your career.
• 1:16:27
  SPEAKER_02: Because the reality is, unless you become really lucky with something, the harder you work at your job, it might get you a promotion, it might get you uh a little bit of a better cr better job at a different company, etc.
• 1:16:44
  SPEAKER_02: And and the way that it was told to me was you might get to upgrade your your Cadillac for a BMW or something like that.
• 1:16:52
  SPEAKER_02: But unless you get really lucky, the harder that you work, you're only going to get marginal gains across your life.
• 1:17:01
  SPEAKER_02: So if if you are doing it for the money, it's never gonna feel enough.
• 1:17:07
  SPEAKER_02: If you're doing it because you care about what you're doing and it actually gives you an intrinsic motivation or or or or a positive feeling, it's gonna matter so much more.
• 1:17:18
  SPEAKER_02: And I s and I saw that in consulting, that I had this path to not even riches, but just path to a very solid income for the rest of my life, but I didn't like what I was doing.
• 1:17:30
  SPEAKER_02: And and then so I knew that even though I would one day get to upgrade to that BMW or etc., that that if I wasn't doing something that I was going to enjoy, then it was it was just gonna be marginal increases for the rest of my life.
• 1:17:45
  SPEAKER_02: And then choosing behavioral science, it I just I actually really enjoy what I'm doing, which is is wild to say about a career.
• 1:17:53
  SPEAKER_02: So it's I think that it's it's really important to money is obviously a big piece, but but it is not the only piece, and and in and really trying to find a path, a niche for an individual to find, um, if one can do that, it's it's a huge opportunity.
• 1:18:10
  SPEAKER_01: That is a phenomenal answer, Connor.
• 1:18:13
  SPEAKER_01: I mean, I think you kill two birds with one stone with just that answer alone.
• 1:18:17
  SPEAKER_01: So thanks for that.
• 1:18:19
  SPEAKER_01: Uh my follow-up question is uh Connor.
• 1:18:22
  SPEAKER_01: Um, if you were to invite three or four guests for a dream dinner, let's say four guests for a dream dinner, who would they be and why?
• 1:18:33
  SPEAKER_02: Wow.
• 1:18:35
  SPEAKER_02: Oh man, there's so many people who I've probably said I would love to do this with that are not going to come to mind immediately.
• 1:18:42
  SPEAKER_02: Michael Lewis is definitely one of them.
• 1:18:44
  SPEAKER_02: I really like Michael Lewis.
• 1:18:46
  SPEAKER_02: Um, he wrote uh The Undoing Project, which is about Daniel Kahneman and Amos Traversky.
• 1:18:51
  SPEAKER_02: He also wrote Moneyball, and um I love his writing.
• 1:18:54
  SPEAKER_02: I think he is uh someone who I would just love to chat with and see how his his line his mind works.
• 1:19:00
  SPEAKER_02: Also wrote The Big Short, which are a lot of people probably know the movie at this point.
• 1:19:04
  SPEAKER_02: Um I mean I've I've been really lucky that I've had a chance to meet a lot of the leaders in the behavioral science field.
• 1:19:10
  SPEAKER_02: The one person I haven't that I'd love to chat with is Daniel Kahneman.
• 1:19:13
  SPEAKER_02: So he would be another one.
• 1:19:15
  SPEAKER_02: He said some pretty profound things over the years, especially as he ages, thinking about like the most impactful biases.
• 1:19:22
  SPEAKER_02: He's also walked back some of his early thinking, um, which I really admire, that he's willing to accept that he he was wrong in places.
• 1:19:30
  SPEAKER_02: Um so he would be uh another person.
• 1:19:35
  SPEAKER_02: Um there's so many names bouncing around in my head.
• 1:19:39
  SPEAKER_02: The other one, I guess, I haven't met is Cast Senstein, and I would love to just understand how he works.
• 1:19:44
  SPEAKER_02: He, of all of the people in this field, just produce an insane amount of content.
• 1:19:50
  SPEAKER_02: And um he's written so many books, I think that that uh he yeah, he he would just be just to understand how how one creates that much content that still is really good and and is still sane.
• 1:20:03
  SPEAKER_02: And then the last one, and and this is more just because I finished watching his latest documentary recently, but I he is my favorite documentarian.
• 1:20:11
  SPEAKER_02: His name's Adam Curtis.
• 1:20:13
  SPEAKER_02: And uh he's a BB, he's done a couple of BBC documentaries.
• 1:20:18
  SPEAKER_02: Uh the most recent one being Can't Get You Out of My Head, and my favorite one is The Century of the Self.
• 1:20:24
  SPEAKER_02: And in this latest one of Can't Get You Out of My Head, he actually has a pretty strong critique of the behavioral science field.
• 1:20:31
  SPEAKER_02: And uh and I really enjoyed seeing somebody attack the feed, not uh you know, attack in an academic sense uh the field to call out some of its flaws.
• 1:20:43
  SPEAKER_02: And I love his work and I loved this critique, and it really left me thinking a lot about what what how do we satisfy some of his his concerns?
• 1:20:52
  SPEAKER_02: So those would be Michael Lewis, Adam Curtis kind of coming from the journalism uh media side, and then uh Kahneman and and Sunstein coming from the academic side.
• 1:21:04
  SPEAKER_01: That is quite an awesome dinner table.
• 1:21:07
  SPEAKER_01: I would love to be a flyer on that wall.
• 1:21:09
  SPEAKER_00: The last question that I have for you, Connor, is that uh what is one thing that uh others don't agree with you on?
• 1:21:17
  SPEAKER_02: So that's a really tough one, Swami.
• 1:21:19
  SPEAKER_02: I'm trying to think uh uh I think ultimately the biggest disagreement that I have, especially with other behavioral scientists, and and and I think this comes again from my consulting background, but also from just the years that I've been doing this.
• 1:21:36
  SPEAKER_02: I truly do believe what I said about the ethics before of that creating content that is better than nothing is is is my ethical guideline.
• 1:21:48
  SPEAKER_02: And I and and I have had people say that that is way too simple, that like how am I to judge that?
• 1:21:55
  SPEAKER_02: It's a very good critique.
• 1:21:58
  SPEAKER_02: It it is to a degree almost too businessy to approach it that way.
• 1:22:03
  SPEAKER_02: Um, but I also believe truly that businesses are going to go forth with decisions with or without research, and so getting something there that is better than just pure intuition is better than nothing.
• 1:22:20
  SPEAKER_02: I ultimately can't find a way to justify anything else, um, especially when the the train is moving and you know the train will continue to move.
• 1:22:30
  SPEAKER_02: I've also had people say, well, then you should just you know throw your hands up and say, well then then you don't want research.
• 1:22:36
  SPEAKER_02: And so I've gotten, like I said, a lot of critiques on that.
• 1:22:38
  SPEAKER_02: Um, but I do I it's something that I hold pretty true.
• 1:22:41
  SPEAKER_02: So um I think that that would be one of the the the biggest ones.
• 1:22:45
  SPEAKER_02: And then I uh you know, I would also just say with building the Applied Behavioral Science Association, I've had people suggest why are you trying to add boundaries to a field that's still so new?
• 1:22:58
  SPEAKER_02: Why are you trying to say you're a behavioral scientist, you're not a behavioral scientist when when we really don't really know what a behavioral scientist is?
• 1:23:06
  SPEAKER_02: And again, I think it's a good critique.
• 1:23:09
  SPEAKER_02: People are going to hire behavioral scientists because it is the nice, you know, I'm trying to think of a good word to describe it, but popular uh new job title.
• 1:23:20
  SPEAKER_02: So companies are going to do it with or without definition.
• 1:23:24
  SPEAKER_02: And I think that providing something is better than just a hiring manager going, Well, this is what I'm gonna decide as a behavioral scientist, and then ultimately hiring someone who is not gonna be able to do some of the core tenets.
• 1:23:37
  SPEAKER_02: Um, so so yeah, so those would be kind of the the two that fall similarly under the same line.
• 1:23:44
  SPEAKER_01: So my final question to you, Connor, is given how we've had our conversation and given the experiences that you had speaking to us, uh, who do you think would make a great guest on Contra Minds?
• 1:23:57
  SPEAKER_01: Who would you recommend join us and our listeners for an in-depth conversation?
• 1:24:02
  SPEAKER_02: I think another really good behavioral scientist who has a lot of oh, he and I share a fair amount of similar ideas, but I think we also have enough unique experience that we have enough of a different story to tell.
• 1:24:17
  SPEAKER_02: Um, his name is Florent.
• 1:24:19
  SPEAKER_02: I referenced him, he was former chief manager of behavioral science at Allstate.
• 1:24:24
  SPEAKER_02: He's got a French last name that I'm gonna butcher, but it's Florent Boussillon.
• 1:24:28
  SPEAKER_02: Uh so yeah, so he would be one individual.
• 1:24:30
  SPEAKER_02: You know, another person is Jim Gusha, um, who started his career as the as an actuary, then became really interested in behavioral science, decided to dedicate his focus to that, and now he's the chief data scientist at Deloitte.
• 1:24:46
  SPEAKER_02: Yeah, those are two people that come to mind.
• 1:24:48
  SPEAKER_02: Um I know I'm gonna be upset similar to the dinner table question later on.
• 1:24:52
  SPEAKER_02: I'll be like, oh, why didn't I think about this person?
• 1:24:55
  SPEAKER_02: But those are two people who've been good mentors to me.
• 1:24:57
  SPEAKER_02: They both have very unique stories, and I know I love the Contra Minds philosophy of trying to find people that kind of took a different, different than standard path.
• 1:25:07
  SPEAKER_02: So yeah.
• 1:25:08
  SPEAKER_01: So, Connor, I would like to thank you once again for taking the time and joining us on Contra Minds.
• 1:25:14
  SPEAKER_01: It was a pleasure and privilege to have you with us for all this time, and thanks for sharing all your ideas and your lessons and your aspirations and your journey with us, and we deeply appreciate it.
• 1:25:25
  SPEAKER_01: And thank you so much.
• 1:25:26
  SPEAKER_00: Was absolutely fascinating.
• 1:25:29
  SPEAKER_00: Your beliefs, your value systems, your authenticity with which you expressed your views and expressed your thoughts was amazing.
• 1:25:37
  SPEAKER_00: Really enjoyed it, and thanks a lot.
• 1:25:38
  SPEAKER_02: Vignesh Swami was it was a pleasure.
• 1:25:41
  SPEAKER_02: I enjoyed the question.
• 1:25:42
  SPEAKER_02: You all can be hard interviewers.
• 1:25:45
  SPEAKER_02: I'm joking, but uh no, it truly did.
• 1:25:47
  SPEAKER_02: I I enjoyed the enjoyed the conversation thoroughly.
• 1:25:50
  SPEAKER_02: Yeah, and uh yeah, I was pushed being on the show.
• 1:25:54
  SPEAKER_00: Thanks for listening to this episode.
• 1:25:57
  SPEAKER_00: For selected links and detailed show notes, visit www.contramines.com slash blog.
• 1:26:05
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• 1:26:28
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