Bridging Silos and Strategy in Pharma Research with Shawn McKenna

Transcript

Shawn 0:00 I think that's just regular human nature that if you're really good at something and somebody else tries to come and do your thing, regular human nature is just like, why are they doing what I wanna do? Why are they doing my job?

Stephanie 0:13 Hello, fellow insight seekers. Welcome to the Curiosity Current, a podcast that's all about navigating the exciting world of market research. I'm Stephanie Vance.

Matt 0:23 And I'm Matt Mahan. Join us as we explore the ever shifting landscape of consumer behavior and what it needs for brands like yours.

Stephanie 0:31 Each episode will get swept up in the trends and challenges facing researchers today, riding the current of curiosity towards new discoveries and deeper understanding.

Matt 0:41 Along the way, we'll tap into the brains of industry leaders, decode real world data, and explore the tech that's shaping the future of research.

Stephanie 0:48 So whether you're a seasoned pro or just getting your feet wet, we're excited to have you on board.

Matt 0:54 So with that, let's jump right in.

Stephanie 0:58 Today, we're excited to welcome Sean McKenna, senior director of analytics at Curex Pharmaceuticals and a seasoned expert in pharma market research, advanced analytics, and commercial insights.

Matt 1:10 Sean has built an impressive career spanning both the supplier and client side of the industry, leading advanced quantitative research at Adelphi, driving insights jazz. Pharmaceuticals, and now shaping the commercial analytics strategy at Curex. His expertise covers everything from patient journey analytics to integrating data science into market research teams.

Stephanie 1:30 In this episode, we're diving into some of the biggest challenges and opportunities in pharma research, how companies are balancing primary and secondary data, the evolving role of AI, and what it really takes to structure an insights team that seamlessly integrates data science, primary and secondary research, and business strategy.

Matt 1:49 If you are curious about the future of farmer research and how technology is reshaping the industry, you won't want to miss this conversation. Sean, welcome to the show.

Shawn 1:57 Thanks, guys. Happy to be here. Thanks for having me.

Matt 2:00 Of course. We're very happy to have you. I kind of alluded to it in our intro, but you've seen a lot of different perspectives in in the insights and data analytics universe, all as it pertains to pharmaceutical research. How has your work on both sides shaped your approach to research and insights? I know that's a question we like to ask folks who have this kind of dual perspective. What are the particular challenges or opportunities that you've you've recognized sort of switching sides of the fence?

Shawn 2:29 Yeah. I think it's a great question. It's a common one, but I think it's a really great one, right, because folks on the supplier side might eventually wanna do stuff on the client side, and folks on the client side might wonder what it's like on the supplier side if they've never actually done it. I think that's just regular human nature that if you're really good at something and somebody else tries to come and do your thing, regular human nature is just like, why are they doing what I wanna do? Why are they doing my job?

Stephanie 0:13 Hello, fellow insight seekers. Welcome to the Curiosity Current, a podcast that's all about navigating the exciting world of market research. I'm Stephanie Vance.

Matt 0:23 And I'm Matt Mahan. Join us as we explore the ever shifting landscape of consumer behavior and what it needs for brands like yours.

Stephanie 0:31 Each episode will get swept up in the trends and challenges facing researchers today, riding the current of curiosity towards new discoveries and deeper understanding.

Matt 0:41 Along the way, we'll tap into the brains of industry leaders, decode real world data, and explore the tech that's shaping the future of research.

Stephanie 0:48 So whether you're a seasoned pro or just getting your feet wet, we're excited to have you on board.

Matt 0:54 So with that, let's jump right in.

Matt 1:49 If you are curious about the future of farmer research and how technology is reshaping the industry, you won't want to miss this conversation. Sean, welcome to the show.

Shawn 1:57 Thanks, guys. Happy to be here. Thanks for having me.

Shawn 2:29 Yeah. I think it's a great question. It's a common one, but I think it's a really great one, right, because folks on the supplier side might eventually wanna do stuff on the client side, and folks on the client side might wonder what it's like on the supplier side if they've never actually done it. ? Yeah. I think it's a great question. It's a common one, but I think it's a really great one, right, because folks on the supplier side might eventually wanna do stuff on the client side, and folks on the client side might wonder what it's like on the supplier side if they've never actually done it.

Matt 2:43 Sure.

Shawn 2:44 I think the main takeaway that I have after doing both of them is just having a recognition for the limitations of what primary research can do. Not everything can be answered no matter how much everybody wants it to be answered by a survey or a call. I just think it's good to know that you can have really great questions, and you can get them pretty far along. But on the supplier side, you can only really do so much sometimes. I think it's really beneficial to focus on one big thing and do that really, really, really well. One big question instead of, like, the 32 questions that client me might have. Because I you get caught in the trap sometimes, and I was caught in the trap sometimes of you wanna try to please and you wanna try to answer everything. But sometimes that's just not possible.

Matt 3:35 Yeah. That really resonates. I know both Stephanie and I spent time on the client side as well. One difference I always feel is, like, you're on the agency side, you have maybe an increase in breadth, which is nice. You have a lot of different topics that you can tackle, a lot of different challenges and and verticals, and you work with a lot of different teams. But you don't get to go particularly deep in any one particular strategic solution. And, you know, sometimes you kinda you get taken away from the implementation of the work a little bit early on, which is sad because we you know, we're all, like, research nerds and we're we we love studying people and you kinda miss out on the implementation of of the work that you do.

Shawn 4:18 And I just think that's more contingent on team client to involve everybody as much as possible. I think that's also the the main thing. Like, nobody on the supplier side actively wants to do a bad job. You really do a great job, but you can only do as good of a job as the information you're given and as included as you are. It's just I think contingent on team clients. Really involved folks. I mean, those are my favorite clients from when I was a supplier on the agency side or the folks who brought me along all the way through, and I just strive to hopefully do that. Hopefully, if any of my friends and suppliers are listening, they would agree. ? Yeah. I think it's a great question. It's a common one, but I think it's a really great one, right, because folks on the supplier side might eventually wanna do stuff on the client side, and folks on the client side might wonder what it's like on the supplier side if they've never actually done it.

Matt 2:43 Sure.

Shawn 4:18 And I just think that's more contingent on team client to involve everybody as much as possible. I think that's also the the main thing. Like, nobody on the supplier side actively wants to do a bad job. You really do a great job, but you can only do as good of a job as the information you're given and as included as you are. It's just I think contingent on team clients. Really involved folks. I mean, those are my favorite clients from when I was a supplier on the agency side or the folks who brought me along all the way through, and I just strive to hopefully do that. Hopefully, if any of my friends and suppliers are listening, they would agree. ust I think contingent on team clients. Really involved folks. I mean, those are my favorite clients from when I was a supplier on the agency side or the folks who brought me along all the way through, and I just strive to hopefully do that. Hopefully, if any of my friends and suppliers are listening, they would agree.

Stephanie 4:57 I love that. Another question we have is, so it's a bit unique that you've worked across primary and secondary research, forecasting, omnichannel analytics, patient journey mapping. In pharma, I think like in a lot of industries, we think of data as just being so fragmented across a lot of different stakeholders. What do you find is the biggest challenge in creating an integrated insights function like that? And how do you see organizations overcoming the types of silos that can occur with, like, your primary research team and your secondary research team and your journey mapping, you know, function that might sit in a totally different department?

Shawn 5:36 I think it's really hard. First, it's just recognizing that's hard and that there's one size or one magic solution won't be the same for everybody. People like to work in silos. Silos are somewhat comfortable. But if you and if somebody crosses those, it sometimes feels like you're being attacked, and you might need to defend that. I think that's just regular human nature that if you're really good at something and somebody else tries to come and do your thing, regular human nature is just like, why are they doing what I wanna do? Why are they doing my job? And that's sometimes just scary. I don't think there's many magic solutions besides just hopefully being an interested and open individual that can hopefully recognize and understand that people are different and that people wanna work on different areas. People work differently, and then communicating and sharing is pretty big. And then just a recognition that if you approach other departments or you try to work across silos or break those barriers down, there's a really big difference between saying, like, I'm right, and you've been doing it wrong versus, like, here's something I thought of. What do you think? It could be wrong. I don't really believe, Stephanie, there's, like, the magic solution to how you break it down besides just having a really good group of people who are really open and willing to work together.

Stephanie 6:57 That makes a ton of sense. Another question that I have. So on the supplier side, I personally have worked across a a lot of industries in my career, but not pharma critically. And I have this perception that pharma research is highly constrained by regulatory and compliance challenges, which I would think would make it harder to move at the same speed as we are seeing other industries be pressured to move at. So I'm curious about how you deal with the challenges of developing strategies that are agile enough to provide timely insights, right, in the at at points of time, right, while also still maintaining that rigor that you have to have in pharma. ust I think contingent on team clients. Really involved folks. I mean, those are my favorite clients from when I was a supplier on the agency side or the folks who brought me along all the way through, and I just strive to hopefully do that. Hopefully, if any of my friends and suppliers are listening, they would agree. I love that. Another question we have is, so it's a bit unique that you've worked across primary and secondary research, forecasting, omnichannel analytics, patient journey mapping. In pharma, I think like in a lot of industries, we think of data as just being so fragmented across a lot of different stakeholders. What do you find is the biggest challenge in creating an integrated insights function like that? And how do you see organizations overcoming the types of silos that can occur with, like, your primary research team and your secondary research team and your journey mapping, you know, function that might sit in a totally different department?

Shawn 7:37 Well, the fun answer to this question is that because I've only done pharma for nine ish, ten ish years, and then two or three ish years, and then, you know, three ish more years now, I actually don't really have a great concept of how quickly y'all can do things in pharma. So so maybe my just whole expectation and my whole frame of reference is a little bit different for what I think is quick. Personally, I when it comes to agile or quick, I personally think it's very reasonable to think about quant in terms of, alright. I'm gonna write an RFP, give folks time to come up with a good solution or proposal, evaluate and commission in a week, then we're gonna write the thing in a week, then we're gonna fill it in one to two weeks depending on incidents, then we're going to top line in three to four days, and then maybe we have some fuller report a week later. I mean, that's a month. That's month and a week. That's not awful in my personal opinion. And then if we do qual, you know, we can still knock out the same upfront bit, but then we just have a little bit shorter because we can recruit and then we can field on an ongoing basis. And then as long as everybody's comfortable with maybe recording requirements that it doesn't need to be this amazing over the top PowerPoint, but it could just be bullets at the end of the interviews or the days, and we kinda build and iterate. I mean, now I've just shaved a week off of, say, Quap. So I know maybe you guys in team consumer or the folks out there, team consumer, like, a week, you know, a month month and a week. That's so long.

Stephanie 9:10 Yeah. We're hitting a week these days. Yeah. It's bananas over in consumer world. Yeah.

Shawn 9:16 And my personal perspective is sort of like, would that be amazing? Yeah. Sure. It would be really amazing. But if we have a question that I think needs to be answered by hearing the perspective of people living with conditions or health care practitioners, if it's that critical that we really need to hear from them and we can't make an educated decision, I'm okay waiting a month to make sure we get that answer done very correctly and very accurately and very well because it's a really important decision that we might have to make. That's my perspective on it. If it's something small and something quick and, also, pharma doesn't really move that fast. Right? We have strong regulatory presence for really good reasons because we don't want to do anything incorrect or harm anybody. We wanna make sure things go as well as possible to make the best possible decisions. And if we can't wait a month, a month and a week, I think that's kind of a failing on our planning side versus the ability to need to force you all or suppliers to, like, break their backs. t the same speed as we are seeing other industries be pressured to move at. So I'm curious about how you deal with the challenges of developing strategies that are agile enough to provide timely insights, right, in the at at points of time, right, while also still maintaining that rigor that you have to have in pharma. Well, the fun answer to this question is that because I've only done pharma for nine ish, ten ish years, and then two or three ish years, and then, you know, three ish more years now, I actually don't really have a great concept of how quickly y'all can do things in pharma. So so maybe my just whole expectation and my whole frame of reference is a little bit different for what I think is quick. Personally, I when it comes to agile or quick, I personally think it's very reasonable to think about quant in terms of, alright. I'm gonna write an RFP, give folks time to come up with a good solution or proposal, evaluate and commission in a week, then we're gonna write the thing in a week, then we're gonna fill it in one to two weeks depending on incidents, then we're going to top line in three to four days, and then maybe we have some fuller report a week later. I mean, that's a month. That's month and a week. That's not awful in my personal opinion. And then if we do qual, you know, we can still knock out the same upfront bit, but then we just have a little bit shorter because we can recruit and then we can field on an ongoing basis. And then as long as everybody's comfortable with maybe recording requirements that it doesn't need to be this amazing over the top PowerPoint, but it could just be bullets at the end of the interviews or the days, and we kinda build and iterate. I mean, now I've just shaved a week off of, say, Quap. So I know maybe you guys in team consumer or the folks out there, team consumer, like, a week, you know, a month month and a week. That's so long.

Stephanie 9:10 Yeah. We're hitting a week these days. Yeah. It's bananas over in consumer world. Yeah.

Stephanie 10:32 Yeah. That's such a good point. I think in particular, your point about special audiences is such a critical one. Right? It's not like you're going out to GenpOp eighteen to sixty five to find out about, like, a shampoo, uh, scent.

Shawn 10:45 You know? Yeah. That sounds fascinating. But but I don't think I've ever act. I can actually count the number of times that I've been involved with gen Genpap on a sample basis. I think there's maybe three three times

Stephanie 11:01 Oh, wow. In the

Shawn 11:02 other twelve years that we've really genuinely gone to Genpap. It's just not the same when as long as we adjust our expectations. My personal feelings are if a month is too long, we've kinda messed something up in planning and in thinking it through.

Stephanie 11:19 Gotcha. Makes a lot of sense.

Shawn 11:21 Unless everyone's team's supplier and not trying to make them do things super quickly.

Matt 11:26 Hey. We'll take it.

Stephanie 11:27 That's right. We love that.

Matt 11:31 Speaking of speed, my role on the podcast is to make sure that no guest escapes without talking about AI. It's my show of fealty to our technological overlords.

Shawn 11:40 Sure.

Matt 11:41 AI has been positioned as a game changer for everything. Certainly, I'm sure by some, including in in your industry, in in pharmaceutical research and healthcare in general from a lot of different perspectives. You described it as being in an Inuit stage. So, you know, not quite fully developed yet, still requiring a lot of human oversight. Was Was wondering if you could just talk a little bit about that. Like, where do you see the value of it? So today, is it gonna change? Is are there areas where AI might play a role, like maybe, you know, in recruiting for special audiences, something like that? Are there particular areas where it's gonna work better than others? But overall, what's your take on on AI and pharma?

Shawn 12:27 Yeah. So in pharma overall or in pharma for certain market research insights?

Matt 12:33 Well, I'd be curious on on your perspective on both, really. I sense you have an opinion on both.

Stephanie 12:37 Well, yeah, it seems like it's really exciting in pharma overall. I would love to hear you talk about both too.

Matt 12:43 Very optimistic.

Shawn 12:44 Pharma overall, I think great. Now mind you, my area of expertise is is definitely not medical ish things. Right.

Unknown 12:53 Right. nt to do anything incorrect or harm anybody. We wanna make sure things go as well as possible to make the best possible decisions. And if we can't wait a month, a month and a week, I think that's kind of a failing on our planning side versus the ability to need to force you all or suppliers to, like, break their backs.

Stephanie 11:01 Oh, wow. In the

Stephanie 11:19 Gotcha. Makes a lot of sense.

Shawn 11:21 Unless everyone's team's supplier and not trying to make them do things super quickly.

Matt 11:26 Hey. We'll take it.

Stephanie 11:27 That's right. We love that.

Matt 11:31 Speaking of speed, my role on the podcast is to make sure that no guest escapes without talking about AI. It's my show of fealty to our technological overlords.

Shawn 11:40 Sure.

Shawn 12:27 Yeah. So in pharma overall or in pharma for certain market research insights?

Matt 12:33 Well, I'd be curious on on your perspective on both, really. I sense you have an opinion on both.

Stephanie 12:37 Well, yeah, it seems like it's really exciting in pharma overall. I would love to hear you talk about both too.

Matt 12:43 Very optimistic.

Shawn 12:44 Pharma overall, I think great. Now mind you, my area of expertise is is definitely not medical ish things. Right.

Unknown 12:53 Right.

Stephanie Well, yeah, it seems like it's really exciting in pharma overall. I would love to hear you talk about both too.

Matt 12:43 Very optimistic.

Shawn 12:44 Pharma overall, I think great. Now mind you, my area of expertise is is definitely not medical ish things. Right.

Unknown 12:53 Right.

Shawn 12:54 You know? I would defer to some of my medical friends and colleagues and experts who probably know a little bit more. But, you know, when it comes to the ability of AI to intake a lot of information and then provide it back in a usable or an interactive way, or when it helps to trying to find patterns in clinical data that may not exist or and this is so far beyond my understanding, but when it comes to things like drug discovery or things

Matt 13:25 like

Shawn 13:25 that, I believe those are really wonderful areas because AI is fundamentally really good at pattern recognition. So patterns that we couldn't figure out, no matter how good I think I am at it, are and intake of data and synthesis and then response. I think I might be good at it, but I'm not as good at it as some of these things that are being thrown out there now.

Matt 13:50 Right. Right.

Unknown 13:51 When it

Shawn 13:51 comes to commercial aspect of things, on the insights aspect of things or survey data, personally, I've not seen and I'm probably opening myself up to about 38 cold calls on LinkedIn. I have not seen incredibly compelling use cases using NLP to code OE is not really new. There were papers on that from the early seventies, mid eighties on the use of NLP, those sorts of things. That's not really new or not. It can be done more quickly and more thoroughly per perhaps, but that's not really new. Using it to check for patterns of bad respondents, that's kind of new, and that's kind of interesting. Using AI to design surveys, I don't really or write questions. Could it get some of the basics out there? Sure. But at the end of the day, AI is not going to understand the new or in my opinion, on what I've seen, AI won't understand the nuances of, like, eGFR positive drugs for lung cancer and the relationship of some of the newer data to some of the older data. To use, like, a completely random example, it has nothing to do with my current job.

Unknown 15:10 But Well

Stephanie Well, yeah, it seems like it's really exciting in pharma overall. I would love to hear you talk about both too.

Matt 12:43 Very optimistic.

Shawn 12:44 Pharma overall, I think great. Now mind you, my area of expertise is is definitely not medical ish things. Right.

Unknown 12:53 Right.

Matt 13:25 like

Matt 13:50 Right. Right.

Unknown 13:51 When it

Unknown 15:10 But Well derstand the new or in my opinion, on what I've seen, AI won't understand the nuances of, like, eGFR positive drugs for lung cancer and the relationship of some of the newer data to some of the older data. To use, like, a completely random example, it has nothing to do with my current job. But Well

Shawn 15:11 You know? It I just don't think it would be able to pull out exactly what some folks might be interested in that realm. On the bright side, do I use AI on data and commercial stuff? Yeah. Sure. I mean, we just used it to put 50 to 75,000 drug codes into categories that made sense for our business. And AI got us pretty far. It saved me a bunch of work, but somebody had to sit there at the end of it, kinda scan it, and go like, wait. Is that drug actually in this category or not? And until you get to the results end of things, you don't really know. Right? So there still is a supervision level. The the current hot topic for what I've seen more recently in market research or primary is simulated or synthetic data, whether they think about that in terms of qualitative responses or even simulating quant. If anybody has a really trustworthy and real assessment of it, I'd love to see that. But what I'd love to see is you run the same study with real health care practitioners or real patients. You run the same line with what you purport to be simulated or synthetic. We get input on the design of the surveys because sometimes those survey questions could kinda be very simplistic and adaptive, so then you have very comparative answers. And then we get the raw data, and then we know how it's trained on it. If we did all that, you can maybe win me over. But at the end of the day, I personally think I'm going to still take two or three people from a trusted market research company Yeah. Who I know are clever, insightful, intelligent people and have been doing this for a while, say, where I used to work at Delphi or, say, another company, partners in my group, I'm going to take them over a computer program from somebody who thinks they know it better because these people have been doing this job for five, ten years, and they're very good at it. And they know what we're after, and they know what's important and what's not important. That's very important and very critical for me, so I'd love to see AI get to this level of helping understand data that might come back in surveys more than just, say, summaries or frequencies.

Matt 17:28 Right. If you

Shawn 17:29 could do that, that'd be wonderful. But hit me up on LinkedIn with somebody Yeah. Yeah. Yeah. Somebody's got it. I haven't seen it. So I'd I'd love to see that. derstand the new or in my opinion, on what I've seen, AI won't understand the nuances of, like, eGFR positive drugs for lung cancer and the relationship of some of the newer data to some of the older data. To use, like, a completely random example, it has nothing to do with my current job.

Unknown 15:10 But Well

Matt 17:28 Right. If you

Shawn 17:29 could do that, that'd be wonderful. But hit me up on LinkedIn with somebody Yeah. Yeah. Yeah. Somebody's got it. I haven't seen it. So I'd I'd love to see that. el of helping understand data that might come back in surveys more than just, say, summaries or frequencies.

Matt 17:28 Right. If you

Shawn 17:29 could do that, that'd be wonderful. But hit me up on LinkedIn with somebody Yeah. Yeah. Yeah. Somebody's got it. I haven't seen it. So I'd I'd love to see that.

Stephanie 17:39 I think that's such an interesting point you're making because a lot of what you're talking about really is it's driven by your experience in the very, like, specific vertical that you work in and, like, what the kinds of, like when we talk about survey design and and AI assistance in the world of survey design, it's actually quite good in my opinion in writing a consumer survey about, like, a concept test or innovation work and right? Like, that's that more formulate type of research where, you know, it's written in consumer language about something that, you know, AI models have been trained on quite a bit. But it's such a good point that, like, what you're doing is a lot more nuanced and the language that's used in that vertical is a lot more nuanced. And it it would be like writing a really complex b to b survey and expecting AI to do it without any intervention. Like, that's

Shawn 18:33 The devil's advocate position would be that I'm speaking as if I know everything and there's somebody out there who is doing it and that I also am coming from a position of alleged superiority or exclusivity where, you know, really, my stuff isn't that much different, so why can't we get away with it? That might be true, but I or somebody could argue that position rather. I just personally do believe that when it comes to design of a survey, it's not gonna get the exact wording correctly. It's not going to get the maybe the topics correctly on a clinical trial or opportunity design. It's not going to phrase things the way I might want to phrase them so I can be respectful of people's lived experiences, you know

Stephanie 19:21 Great one.

Shawn 19:21 The area that I am working in.

Matt 19:24 Yeah.

Shawn 19:24 And I just think that's a pretty important thing for folks to keep in mind. el of helping understand data that might come back in surveys more than just, say, summaries or frequencies.

Matt 17:28 Right. If you

Shawn 17:29 could do that, that'd be wonderful. But hit me up on LinkedIn with somebody Yeah. Yeah. Yeah. Somebody's got it. I haven't seen it. So I'd I'd love to see that.

Stephanie 19:21 Great one.

Shawn 19:21 The area that I am working in.

Matt 19:24 Yeah.

Shawn 19:24 And I just think that's a pretty important thing for folks to keep in mind. g to phrase things the way I might want to phrase them so I can be respectful of people's lived experiences, you know

Stephanie 19:21 Great one.

Shawn 19:21 The area that I am working in.

Matt 19:24 Yeah.

Shawn 19:24 And I just think that's a pretty important thing for folks to keep in mind.

Matt 19:28 The human in the loop concept is, you know, it's always part and parcel with any conversation on AI. But I feel like when you're talking about situations and industries where human empathy truly are really critical. They're core to what you do. It is like, you know, health care is just such a great example of that. That human in the loop plays an ever more important role. It's hard to imagine getting to a day where that's that's, like, not the case. Like you mentioned synthetic data, which is obviously a big topic right now. You know, it's interesting to hear that come out of out of pharma. Certainly, it's a big topic from other industries, other verticals. You know, there's it just seems like there's this sort of psychological barrier that people run up to when you start talking about, like, basically total control of the interaction, total control of the insights and the data that is just, like, fundamentally different than a conversation about drafting a survey or, um, helping me to understand what the open end results look like. There's just there's something there that appears to be different. I'm from Detroit, so I think of everything through the lens of the automotive industry. And it's like, you think about self driving cars, they haven't been adopted nearly as fast as was originally predicted because there's just this psychological barrier about giving over total control of the vehicle that you, your family, your children are in that's different from, say, letting your car help you park, which has been out forever. And it was, like, immediately adopted. People were, like, heck, yeah. Let me like, help me parallel park this car. That's a challenge I need I need solved. Help me brake if there's a stop occurring that I don't see. Adopt it immediately. So you saw all these, like, autonomous systems just take the industry by storm. Self driving was really somewhat of an incremental change on that, but yet at the same time, we're not all kicking back or reading the newspaper or scrolling our phones while our our cars drive us somewhere even though the technology is there.

Stephanie 21:26 It feels transformational, right, not in the hospital. Yeah. g to phrase things the way I might want to phrase them so I can be respectful of people's lived experiences, you know Great one.

Shawn 19:21 The area that I am working in.

Matt 19:24 Yeah.

Shawn 19:24 And I just think that's a pretty important thing for folks to keep in mind.

Stephanie 21:26 It feels transformational, right, not in the hospital. Yeah. iving was really somewhat of an incremental change on that, but yet at the same time, we're not all kicking back or reading the newspaper or scrolling our phones while our our cars drive us somewhere even though the technology is there. It feels transformational, right, not in the hospital. Yeah.

Shawn 21:30 Yeah. I think it's pretty asymptotic. Right? Like, you can get things pretty far in being really advanced and quicker and quicker and quicker, and then you start making little tiny incremental gains in the quickness to be able to do something. And if we're going to sit here and say, alright. You know what? You don't need to get insights from real human beings. It can all be simulated. And the way we're gonna do this is you're gonna ask a question to a set of data that are archetypal defined sample is gonna live in this mystery database. The mystery database has been trained on a mystery number of large sets of consumer information before, so we know how people will react to this historically. And all you need to do is ask it a question, and it'll tell you the answer, and then you can move forward. Like you said, Stephanie, that might work in, say, a shampoo fragrance or what people tend to want or what people in a certain area or a certain group of people, men versus women or what they might prefer. It's not I just don't think it's gonna work so hot when I'm talking or want to know about the future of a certain area of health care and what someone might do or not do, it's a lot more layered and emotional. And I think some people out here listening, if they do pharma, definitely might go, well, yeah, but, historically, again, the devil's advocate position would be like, no. It can be reduced to a formula. That can be you know, if the drug is first in the market, it's going to do so well. If the drug is second in market without a defining advantage, it's gonna do so well. But I think there's plenty of examples where that hasn't been the case that to trade it all as one uniform block that a group of synthetic or trained data on historical people could do just as well as real people. I don't know. I'm I'm personally not super convinced just yet when it comes to health care. I think you can get super close like you used in the car example now. You can get really, really close to something that is automated or something that helps streamline or something that's more efficient. But people aren't just gonna I'm not gonna jump in the fully automated r because I'm scared to do a bit because what because I'm trusting things that have happened four or five years ago, and I'm trusting that the company's survey data is representative, and I'm trusting that it has similar responses or built on the same way to what might happen in the future. That's a lot of trust for the savings of three weeks. Right? And that's that trait personally that I run through. iving was really somewhat of an incremental change on that, but yet at the same time, we're not all kicking back or reading the newspaper or scrolling our phones while our our cars drive us somewhere even though the technology is there.

Stephanie 21:26 It feels transformational, right, not in the hospital. Yeah.

Shawn 21:30 Yeah. I think it's pretty asymptotic. Right? Like, you can get things pretty far in being really advanced and quicker and quicker and quicker, and then you start making little tiny incremental gains in the quickness to be able to do something. And if we're going to sit here and say, alright. You know what? You don't need to get insights from real human beings. It can all be simulated. And the way we're gonna do this is you're gonna ask a question to a set of data that are archetypal defined sample is gonna live in this mystery database. The mystery database has been trained on a mystery number of large sets of consumer information before, so we know how people will react to this historically. And all you need to do is ask it a question, and it'll tell you the answer, and then you can move forward. Like you said, Stephanie, that might work in, say, a shampoo fragrance or what people tend to want or what people in a certain area or a certain group of people, men versus women or what they might prefer. It's not I just don't think it's gonna work so hot when I'm talking or want to know about the future of a certain area of health care and what someone might do or not do, it's a lot more layered and emotional. And I think some people out here listening, if they do pharma, definitely might go, well, yeah, but, historically, again, the devil's advocate position would be like, no. It can be reduced to a formula. That can be you know, if the drug is first in the market, it's going to do so well. If the drug is second in market without a defining advantage, it's gonna do so well. But I think there's plenty of examples where that hasn't been the case that to trade it all as one uniform block that a group of synthetic or trained data on historical people could do just as well as real people. I don't know. I'm I'm personally not super convinced just yet when it comes to health care. I think you can get super close like you used in the car example now. You can get really, really close to something that is automated or something that helps streamline or something that's more efficient. But people aren't just gonna I'm not gonna jump in the fully automated r because I'm scared to do a bit because what because I'm trusting things that have happened four or five years ago, and I'm trusting that the company's survey data is representative, and I'm trusting that it has similar responses or built on the same way to what might happen in the future. That's a lot of trust for the savings of three weeks. Right? And that's that trait personally that I run through. appened four or five years ago, and I'm trusting that the company's survey data is representative, and I'm trusting that it has similar responses or built on the same way to what might happen in the future. That's a lot of trust for the savings of three weeks. Right? And that's that trait personally that I run through.

Stephanie 24:16 I hear that. Well, to move on from the topic of AI, which always is a big win for us just because of its prevalence right now. Another, you know, to broaden out, like, our lens and time a little bit. One of the biggest shifts we've seen in the past decade is the blending of data science with traditional insights modalities. And it seems that you've built teams that integrate both. And I'm curious if you could talk a little bit about what it takes to create a structure where data scientists and insights professionals work seamlessly together. And we talked about that a little bit in the in the silos question, but I'm just like, how do you build that team?

Shawn 24:53 First, to to rephrase, I like to think I've been a part of teams that have done it well. I don't think I can take Gotcha. All the credit for everything. I think I would get yelled at by my friends and colleagues. To answer your question, I when I worked at a certain place, it was it's a lot of what we already spoke about, Stephanie. It was, no. No. No. You handle the primary. Somebody else will handle the data aspect. You stay out of it. Then it was kinda, you know, no. No. No. No. No. No. You you live over there on commercial insights. What happens maybe afterward is not necessarily your thing. If you do that, you end up answering just little bits and pieces of the puzzle instead of trying to work it all out. And I think as long as you approach things in an open and transparent way on why you want to involve or certain things, I personally do optimistically think people might be more open to that. So how do I approach it, or how do I like to think I try to approach it with people that I work with, friends that I work with, or former groups? Start at a big problem. Figure out everybody that's involved. Involve everybody that's involved. Break it into little bits and pieces. Understand that you're not the one who might know better than anybody else, but at the same time, you know, be open to somebody who might have a better idea than you. Figure out as much information as you have internally available on your internal resources. Figure out what you can buy from numbers or data. If you can't do those two steps, then you need to talk to people and answer questions. Then you do that, and you involve all the stakeholders all the way through. I do think it just comes down to clearly identifying a problem, clearly identifying those folks who are involved, making sure everybody the experts are involved. It takes just time, effort, communication, a willingness to be wrong and say that you're wrong and somebody else has a better idea, and then move forward from there. I I I personally just think those are ways to work that lead to better outcomes. appened four or five years ago, and I'm trusting that the company's survey data is representative, and I'm trusting that it has similar responses or built on the same way to what might happen in the future. That's a lot of trust for the savings of three weeks. Right? And that's that trait personally that I run through.

Shawn 24:53 First, to to rephrase, I like to think I've been a part of teams that have done it well. I don't think I can take Gotcha. All the credit for everything. I think I would get yelled at by my friends and colleagues. To answer your question, I when I worked at a certain place, it was it's a lot of what we already spoke about, Stephanie. It was, no. No. No. You handle the primary. Somebody else will handle the data aspect. You stay out of it. Then it was kinda, you know, no. No. No. No. No. No. You you live over there on commercial insights. What happens maybe afterward is not necessarily your thing. If you do that, you end up answering just little bits and pieces of the puzzle instead of trying to work it all out. And I think as long as you approach things in an open and transparent way on why you want to involve or certain things, I personally do optimistically think people might be more open to that. So how do I approach it, or how do I like to think I try to approach it with people that I work with, friends that I work with, or former groups? Start at a big problem. Figure out everybody that's involved. Involve everybody that's involved. Break it into little bits and pieces. Understand that you're not the one who might know better than anybody else, but at the same time, you know, be open to somebody who might have a better idea than you. Figure out as much information as you have internally available on your internal resources. Figure out what you can buy from numbers or data. If you can't do those two steps, then you need to talk to people and answer questions. Then you do that, and you involve all the stakeholders all the way through. I do think it just comes down to clearly identifying a problem, clearly identifying those folks who are involved, making sure everybody the experts are involved. It takes just time, effort, communication, a willingness to be wrong and say that you're wrong and somebody else has a better idea, and then move forward from there. I I I personally just think those are ways to work that lead to better outcomes. e folks who are involved, making sure everybody the experts are involved. It takes just time, effort, communication, a willingness to be wrong and say that you're wrong and somebody else has a better idea, and then move forward from there. I I I personally just think those are ways to work that lead to better outcomes.

Stephanie 26:59 For sure. I'm curious if, like so certainly the the collaboration between different team members who work in different areas, are you also seeing that, like, just generally upscaling, like, for instance, a a primary researcher to be able to do, like, some big data querying and to be able to sort of, like, work across modes. Is that something that becomes more important to where I have to be more of a a Swiss army knife of a researcher and an insights person? Or does it still feel like, no, these are different skills, and it generally works best for them to remain different skills, but we just need to collaborate and share across the skill set and roles.

Unknown 27:40 I think it'd be fantastic if folks did the former. I have R and SQL open on my computer right now alongside a survey. I mean, it's Perfect.

Stephanie 27:51 Yes. Yeah.

Unknown 27:52 I do think it's beneficial to have a general ability to work in those things. You don't need to be an expert in it. Not everybody needs to be able to write really complicated Python or Databricks or anything like that. There are people that are very be very good at it. I do think it's very beneficial for primary and focused individuals to have a general understanding. In health care, specifically, I do feel it's very beneficial for primary focused individuals to have an understanding of claims, numbers, data, sales target, those sorts of things. I just think it makes people stronger. And, conversely, I also think the more technically focused people, sometimes very technically focused people, sometimes think the technical fancy stuff is the cure all and answer to everything.

Stephanie 28:47 Yeah. Yeah.

Unknown 28:48 That's not true. Right? There's gonna be gaps and information behind the scenes, and you do need to just, hopefully, sometimes pick up the phone and talk to people that can fill in excellent gaps. Or sometimes you need you can get something as far as possible, but there's gonna be really concrete, identical gap gaps. I work in an area right now where there is just something fundamentally that you can't buy data on. It's a bit of a mystery. So how do you fill that mystery? We're doing some patient qualitative and patient journey work to try to fill in the gap that we know is out there, and then you just try to make the most of it. So, yes, Stephanie, I I do feel it's very beneficial to have experience. e folks who are involved, making sure everybody the experts are involved. It takes just time, effort, communication, a willingness to be wrong and say that you're wrong and somebody else has a better idea, and then move forward from there. I I I personally just think those are ways to work that lead to better outcomes.

Unknown 27:40 I think it'd be fantastic if folks did the former. I have R and SQL open on my computer right now alongside a survey. I mean, it's Perfect.

Stephanie 27:51 Yes. Yeah.

Stephanie 28:47 Yeah. Yeah.

Unknown 28:48 That's not true. Right? There's gonna be gaps and information behind the scenes, and you do need to just, hopefully, sometimes pick up the phone and talk to people that can fill in excellent gaps. Or sometimes you need you can get something as far as possible, but there's gonna be really concrete, identical gap gaps. I work in an area right now where there is just something fundamentally that you can't buy data on. It's a bit of a mystery. So how do you fill that mystery? We're doing some patient qualitative and patient journey work to try to fill in the gap that we know is out there, and then you just try to make the most of it. So, yes, Stephanie, I I do feel it's very beneficial to have experience. that you can't buy data on. It's a bit of a mystery. So how do you fill that mystery? We're doing some patient qualitative and patient journey work to try to fill in the gap that we know is out there, and then you just try to make the most of it. So, yes, Stephanie, I I do feel it's very beneficial to have experience.

Matt 29:31 That is actually something I wanted to talk a little bit about. And I know don't wanna ask you to spill any proprietary beans here. But having done a a stint in pharma myself, I know one of the things that is challenging is that patient journey is so critical. I mean, we other verticals will talk about a consumer journey. It's important. It is core to the work that you do, I'm sure, and yet it's so hard to fill those gaps just because of the discrete nature of the population you might be interested in and yet the need to go so deep. You know, it's not just that there there is a limited sample, but you need to find this true depth of insights and knowing and understanding. Well, how do you do that? That's a challenge you alluded to. How do you kinda pull all these pieces together and draw that picture for your stakeholders? that you can't buy data on. It's a bit of a mystery. So how do you fill that mystery? We're doing some patient qualitative and patient journey work to try to fill in the gap that we know is out there, and then you just try to make the most of it. So, yes, Stephanie, I I do feel it's very beneficial to have experience. That is actually something I wanted to talk a little bit about. And I know don't wanna ask you to spill any proprietary beans here. But having done a a stint in pharma myself, I know one of the things that is challenging is that patient journey is so critical. I mean, we other verticals will talk about a consumer journey. It's important. It is core to the work that you do, I'm sure, and yet it's so hard to fill those gaps just because of the discrete nature of the population you might be interested in and yet the need to go so deep. You know, it's not just that there there is a limited sample, but you need to find this true depth of insights and knowing and understanding. Well, how do you do that? That's a challenge you alluded to. How do you kinda pull all these pieces together and draw that picture for your stakeholders? the need to go so deep. You know, it's not just that there there is a limited sample, but you need to find this true depth of insights and knowing and understanding. Well, how do you do that? That's a challenge you alluded to. How do you kinda pull all these pieces together and draw that picture for your stakeholders?

Unknown 30:25 Sure. I think I don't think it necessarily is proprietary. I think, generally, if you have a more an area with a, my opinion, if you have an area with a more well established treatment paradigm, so by that, I mean, the first thing folks might get from a doctor is this. The and that's generally true for ninety percent of the people who have an issue with this. And the issue with this the reason they get it is because it's been around. It's proven, and it's really great option for folks who suffer from blah. Then once blah stops working for whatever reason or some folks just don't have a great experience with it, you move to this or this. But the reasons you move to this or this are because of you tend to move to this one if you didn't have a great experience on, say, side effects or tolerability, or you tend to move to this month if folks unfortunately, they aren't responding efficaciously. It's not working for them really well. If you can and then, okay, now we get to say a third line of treatment where all of a sudden, let's just say there's one big thing left over. And, you know, maybe to make this maybe a little bit more concrete, we could kinda talk about maybe, like, a biologic, like, your Humira or your Enbrel or, you know, those drugs which are fantastic for folks. Sometimes those drugs might happen earlier, but let's just pretend those drugs happen after we've exhausted all of our stuff. If you can really concretely kinda rattle off how that'll play out, quant quant can do the trick. Alright. What proportion of people and survey a bunch of people, and we're gonna survey a bunch of people who've tried this, this, this, and what we wanna know is their treatment history and how long they were on those things. Or we can do ask doctors to talk about patients and what might happen. Cool. You could do quant. Flip side is let's talk about Quell or let's talk about a more interactive thing when we don't know that treatment paradigm or it's evolving or it's changing or we're trying to look to the future when stuff might happen. So we're trying to make people guess. So then we're gonna talk to health care providers, and we're gonna talk to folks living with us, both experience and not experience with treatment center. And for the folks living with it, we're gonna just play a bit of a what if game. You know, what if this had for the folks not experienced, we might be asking them to kinda lay out how they might do it. Then we're obviously gonna talk to probably really high up doctors, folks who are gonna lead the opinion, and then folks kind of in the middle ground too who might be on, like, day to day practicing. Say, alright. Two years from now. Yeah. This this this this this. What in the world are you gonna do? That's where we're probably gonna verge a little bit more into qual. Quant, I think, could be done. But to your actual question about patient journey, we can do it primary through primary through. We also have longitudinal data available on what folks are doing, but there's that what that's gonna miss is the why. So, like, we quantify pathways, but I don't know why somebody did this one to this one. I have no clue. Right? And then we're gonna have to talk to people in that qual example to, like, fill in that, well, why? Why did you do this to this? Or why is this changing so quickly? So it it's a little bit messier, but it is something we care about. And then the last thing I'll add, Matt, is just folks though I think everybody wants always to go back to research to fill out. At at some point, there is a bit of just logic or common sense on this that we can fill in. You know? Why do people not wanna do something? People don't wanna do something for pretty solid reasons. There's something that something didn't really work so hot. Some things cost a lot of money. The reasons why some people don't wanna do something are crazy, unique, or different, or novel, and I'm just common sense can fill in a little bit of the gaps as well. the need to go so deep. You know, it's not just that there there is a limited sample, but you need to find this true depth of insights and knowing and understanding. Well, how do you do that? That's a challenge you alluded to. How do you kinda pull all these pieces together and draw that picture for your stakeholders? Sure. I think I don't think it necessarily is proprietary. I think, generally, if you have a more an area with a, my opinion, if you have an area with a more well established treatment paradigm, so by that, I mean, the first thing folks might get from a doctor is this. The and that's generally true for ninety percent of the people who have an issue with this. And the issue with this the reason they get it is because it's been around. It's proven, and it's really great option for folks who suffer from blah. Then once blah stops working for whatever reason or some folks just don't have a great experience with it, you move to this or this. But the reasons you move to this or this are because of you tend to move to this one if you didn't have a great experience on, say, side effects or tolerability, or you tend to move to this month if folks unfortunately, they aren't responding efficaciously. It's not working for them really well. If you can and then, okay, now we get to say a third line of treatment where all of a sudden, let's just say there's one big thing left over. And, you know, maybe to make this maybe a little bit more concrete, we could kinda talk about maybe, like, a biologic, like, your Humira or your Enbrel or, you know, those drugs which are fantastic for folks. Sometimes those drugs might happen earlier, but let's just pretend those drugs happen after we've exhausted all of our stuff. If you can really concretely kinda rattle off how that'll play out, quant quant can do the trick. Alright. What proportion of people and survey a bunch of people, and we're gonna survey a bunch of people who've tried this, this, this, and what we wanna know is their treatment history and how long they were on those things. Or we can do ask doctors to talk about patients and what might happen. Cool. You could do quant. Flip side is let's talk about Quell or let's talk about a more interactive thing when we don't know that treatment paradigm or it's evolving or it's changing or we're trying to look to the future when stuff might happen. So we're trying to make people guess. So then we're gonna talk to health care providers, and we're gonna talk to folks living with us, both experience and not experience with treatment center. And for the folks living with it, we're gonna just play a bit of a what if game. You know, what if this had for the folks not experienced, we might be asking them to kinda lay out how they might do it. Then we're obviously gonna talk to probably really high up doctors, folks who are gonna lead the opinion, and then folks kind of in the middle ground too who might be on, like, day to day practicing. Say, alright. Two years from now. Yeah. This this this this this. What in the world are you gonna do? That's where we're probably gonna verge a little bit more into qual. Quant, I think, could be done. But to your actual question about patient journey, we can do it primary through primary through. We also have longitudinal data available on what folks are doing, but there's that what that's gonna miss is the why. So, like, we quantify pathways, but I don't know why somebody did this one to this one. I have no clue. Right? And then we're gonna have to talk to people in that qual example to, like, fill in that, well, why? Why did you do this to this? Or why is this changing so quickly? So it it's a little bit messier, but it is something we care about. And then the last thing I'll add, Matt, is just folks though I think everybody wants always to go back to research to fill out. At at some point, there is a bit of just logic or common sense on this that we can fill in. You know? Why do people not wanna do something? People don't wanna do something for pretty solid reasons. There's something that something didn't really work so hot. Some things cost a lot of money. The reasons why some people don't wanna do something are crazy, unique, or different, or novel, and I'm just common sense can fill in a little bit of the gaps as well. ? People don't wanna do something for pretty solid reasons. There's something that something didn't really work so hot. Some things cost a lot of money. The reasons why some people don't wanna do something are crazy, unique, or different, or novel, and I'm just common sense can fill in a little bit of the gaps as well.

Stephanie 34:28 That's interesting. Does it it feels a little bit like you're building almost like decision trees, but, like, through disparate kinds of data. Right? Because you don't always need to do, like, a, like, a regression based decision tree. You could be pulling in these elements of, like, common sense, qualitative feedback, and then also, like you said, that just host of, like, longitudinal data that you maybe have access to?

Unknown 34:50 I think for some of my friends who know me, it might sound like heresy or, like, what am I talking about when I'm the one saying, like, sometimes the numbers can't do everything. But I I do think it's true that at some point what I love for it to be super perfect and super clean and super neat through some crazy number y thing, yeah, I would, but it's not gonna always work that. You know? Like, I will make the most complicated y'all know Sankey charts or flowchart?

Stephanie 35:24 Oh, yeah. We love a good Sankey chart.

Unknown 35:25 I will make the most complicated Sankey ever, but, like, I'm the only one who cares about the most complicated Sankey

Stephanie 35:32 ever. Right. Right. Right. Right.

Unknown 35:33 Super pumped about it, and then I would, like, super into it. They might not tell the whole story as much as I'd love it to.

Matt 35:40 Sadly, Sankey is not the answer for everything. He's not.

Unknown 35:44 I would love that, man.

Matt 35:45 I need a Sankey chart for life. Yes.

Stephanie 35:47 You have mentioned in the past interview that I love this, by the way. The hardest part of research is knowing when to stop, knowing when you've truly exhausted all of the secondary and internal data before moving into primary research, which is, I think, a good transition because we were just kinda talking about when you need to supplement versus not. But can you walk us through that decision making process? Uh, how do you know when it's time to to shift approaches?

Unknown 36:13 I think that depends on if we had unlimited resource, I think research will expand to fit the resources it's given. So from that client side, if if we have unlimited resources, we would do unlimited research just because it helps us and helps people feel more confident in their decisions. So first, I just think operating in a little bit of a more constrained universe in general is pretty solid. You know? Okay. There's some new question that's being asked.

Shawn 36:50 First, have we have we touched on that

Unknown 36:53 in the past? Mhmm.

Shawn 36:55 I I

Unknown 36:55 feel like that's super overlooked.

Stephanie 36:58 Totally is. Yeah. ? People don't wanna do something for pretty solid reasons. There's something that something didn't really work so hot. Some things cost a lot of money. The reasons why some people don't wanna do something are crazy, unique, or different, or novel, and I'm just common sense can fill in a little bit of the gaps as well. That's interesting. Does it it feels a little bit like you're building almost like decision trees, but, like, through disparate kinds of data. Right? Because you don't always need to do, like, a, like, a regression based decision tree. You could be pulling in these elements of, like, common sense, qualitative feedback, and then also, like you said, that just host of, like, longitudinal data that you maybe have access to?

Stephanie 35:24 Oh, yeah. We love a good Sankey chart.

Unknown 35:25 I will make the most complicated Sankey ever, but, like, I'm the only one who cares about the most complicated Sankey

Stephanie 35:32 ever. Right. Right. Right. Right.

Unknown 35:33 Super pumped about it, and then I would, like, super into it. They might not tell the whole story as much as I'd love it to.

Matt 35:40 Sadly, Sankey is not the answer for everything. He's not.

Unknown 35:44 I would love that, man.

Matt 35:45 I need a Sankey chart for life. Yes.

Shawn 36:50 First, have we have we touched on that

Unknown 36:53 in the past? Mhmm.

Shawn 36:55 I I

Unknown 36:55 feel like that's super overlooked.

Stephanie 36:58 Totally is. Yeah. ned universe in general is pretty solid. You know? Okay. There's some new question that's being asked.

Shawn 36:50 First, have we have we touched on that

Unknown 36:53 in the past? Mhmm.

Shawn 36:55 I I

Unknown 36:55 feel like that's super overlooked.

Stephanie 36:58 Totally is. Yeah.

Unknown 36:59 I have a pretty funny anecdote from a buddy of mine who did the commissioned research synthesis for things that I did, who's now one of and my buddy has my suppliers now, but he inherited all of my primary work when we were competitors. So, like, first and then he was basically saying, I think somebody's already answered some of these things. So first, I think just having a thorough understanding of the historical information available and what might have been done. Just because it was done two or three years ago or just because it was done by somebody else doesn't mean it's bad, doesn't mean it's wrong, doesn't mean it's inaccurate. You might disagree or you might think you have a sense of superiority over somebody else who did something differently, but that's your own feelings about superiority, and they might be valid. But chances are there's something in something that was done before that has already touched on this. So first of all, I think just understanding the historical information available. Second is then you move into why. Why does your different team need to know this answer? Why? What's the reasoning, and what's the rationale? Is it nice to know, or is it super critical for a real and valid decision that you guys absolutely must make? Because if it's a nice to know of or if it's confidence builder, if it's a actual real unknown that we can't answer, first of all, through historical stuff, Second of all, we just can't common sense think about it. Alright. Cool. Now we've moved into the next level. Third is, well, then do we have any stuff that we've already purchased that can help point us in the right direction or not? That's usually clear and simple. Yes or no. If it has to do with Stephanie, you mentioned concepts. If it has to do with, like, new concepts or new imagery, chances are, like, numbers that you buy internally or have, that's not gonna help you because now you're looking at something, a, visual and b, that's new and, c, for the future. Alright. Cool. We've never done it before. We really need to know it, and there is nothing internal that we already have available to us on a number of day to day basis that can actually do this because it has to do with the future, and it's critical to the business. Alright. Cool. Now we need

Shawn 39:23 to talk to some people

Unknown 39:25 and get some actual real input on it. But I think just ruling out historical things you have already bought or things you've already purchased and then really whittling down whether that question is severe enough to warrant something that you need to do, you now got boom, boom, boom. Alright. Now you're in the realm of what do we need to do? Qual and then into quant for, say, concepts. And that's usually how I try to think things. ned universe in general is pretty solid. You know? Okay. There's some new question that's being asked.

Shawn 36:50 First, have we have we touched on that

Unknown 36:53 in the past? Mhmm.

Shawn 36:55 I I

Unknown 36:55 feel like that's super overlooked.

Stephanie 36:58 Totally is. Yeah.

Shawn 39:23 to talk to some people

Unknown 39:25 and get some actual real input on it. But I think just ruling out historical things you have already bought or things you've already purchased and then really whittling down whether that question is severe enough to warrant something that you need to do, you now got boom, boom, boom. Alright. Now you're in the realm of what do we need to do? Qual and then into quant for, say, concepts. And that's usually how I try to think things. ngs you've already purchased and then really whittling down whether that question is severe enough to warrant something that you need to do, you now got boom, boom, boom. Alright. Now you're in the realm of what do we need to do? Qual and then into quant for, say, concepts. And that's usually how I try to think things.

Stephanie 39:48 And is that a flow that just, like, your team internally goes through, or is this sometimes something that you're having to communicate and walk an internal stakeholder through, like, who comes in through a request? And you're like, I hear the request, but I wanna take you through what we already have, what you you know what I mean? That same process. And then because that to me is a little bit more challenging because you're it it's cross functional by nature, and you're having to educate someone and and persuade them that we do have some existing foundational knowledge to start answering this question, which can sometimes be a harder task than figuring it out yourself.

Unknown 40:27 I might first of all, I might be biased. I think at Jazz, I worked with incredible people who did already go through a lot of that process internally. I think our two main brand leads are wonderful people who did a really great job of thinking this through. I think in my current position, I think, yeah, we the marketing team that I work with, I think, also does a really incredible job at sort of running through that themselves before we get to May. So I do think I am in a very fortunate place. However, when I was a supplier, I do think it's a very worthy conversation to have with somebody. Also, another caveat on that original two points is I am a little bit fortunate in that I've been doing this for a little bit. People whether I do or I do not, I don't know. But people at least think I know what I'm somewhat doing. And so so I think that helps. However, back to the supplier side, I think if you get a question and you're just sitting there over there going, are you serious? Like and it, you know, tend to typically might come from, let's let's throw new people new new me under the bus. Let's throw me under the bus because I was reactive, and I wanted to please, and I didn't know that I was available. And then you're sitting there on team supplier, and you're like, hey. If I actually put this out to this person, I'm turning down money.

Stephanie 42:00 Yeah. It's the conflict. Yeah. ngs you've already purchased and then really whittling down whether that question is severe enough to warrant something that you need to do, you now got boom, boom, boom. Alright. Now you're in the realm of what do we need to do? Qual and then into quant for, say, concepts. And that's usually how I try to think things. And is that a flow that just, like, your team internally goes through, or is this sometimes something that you're having to communicate and walk an internal stakeholder through, like, who comes in through a request? And you're like, I hear the request, but I wanna take you through what we already have, what you you know what I mean? That same process. And then because that to me is a little bit more challenging because you're it it's cross functional by nature, and you're having to educate someone and and persuade them that we do have some existing foundational knowledge to start answering this question, which can sometimes be a harder task than figuring it out yourself.

Stephanie 42:00 Yeah. It's the conflict. Yeah. der the bus. Let's throw me under the bus because I was reactive, and I wanted to please, and I didn't know that I was available. And then you're sitting there on team supplier, and you're like, hey. If I actually put this out to this person, I'm turning down money. Yeah. It's the conflict. Yeah.

Unknown 42:01 Which is a huge catch 22 because you're basically turning it down even though you're sitting there and you're like, oh, come on. We did that two years ago for so and so, and I actually already know this. So then you're stuck. My, again, my altruistic I I like to think it's altruistic, but, really, it's just out of that overwhelming sense of obsessiveness and anxiety. It's just if if I've done something before on the supplier side saying that, I still think it's fantastic. When I first joined the prior company that I worked at, two main groups of people that I'm still very close with at competitor companies basically told me two or three things. I was like, no. You don't need it. Like, this was done before. Uh, we did this, like, a year ago. I was like, thank you.

Stephanie 42:51 I'm just gonna say I've never met a client who wasn't grateful when you do that action. Right? And so I think for client relationship purposes, it's always a win.

Unknown 43:01 It is. It really is. Because I think it's a sign of you know, it's honesty and it's respectfulness. And I think I'm eternally grateful to the two groups of people that told me these things when I first joined them. Already said their company names. And I I just am very grateful toward how they were able to fill in things historically to help to answer your question, whittle down what actually needed to be done because it was a new question. First, it it was just me coming into

Matt 43:31 Yeah. And I think it you know, going back to Stephanie's point on, you know, clients being grateful for that type of interaction, like, that just speaks to how things have changed, continue to change on the supplier side. It's like, we know it's no longer enough to just provide the answers. Like, we in this world where everything's changing so fast or all these new tools, it's like the way you we really show value is by curating that data into, like, knowledge and implications and helping clients whether they're, like, internal or external, really, like, see the forest through the trees including if they've done the if we have had done this work in the past. Like, that's like, that, like, information management is a role that I see people taking more and more of an interest in.

Unknown 44:17 That's really well said. I think that's critical. der the bus. Let's throw me under the bus because I was reactive, and I wanted to please, and I didn't know that I was available. And then you're sitting there on team supplier, and you're like, hey. If I actually put this out to this person, I'm turning down money.

Stephanie 42:00 Yeah. It's the conflict. Yeah.

Stephanie 42:51 I'm just gonna say I've never met a client who wasn't grateful when you do that action. Right? And so I think for client relationship purposes, it's always a win.

Unknown 44:17 That's really well said. I think that's critical. ternal, really, like, see the forest through the trees including if they've done the if we have had done this work in the past. Like, that's like, that, like, information management is a role that I see people taking more and more of an interest in. That's really well said. I think that's critical.

Matt 44:20 So kind of like moving into our closing questions. I have one I have one question we ask all of our guests here, which is to prognosticate over the next five years. So getting back to all of this all of this change, like we talked about AI, we talked about the pace of of evolution and just all of the all of the different challenges that are presented to you, particularly in a in a challenging industry to do the type of work that you do. What is the skill or mindset that is going to be the most valuable to someone looking to do the type of work that you do? You know, if you were looking to hire someone for your team, what is that skill or mindset that you would look for that would show you that they are are setting themselves up for success over the next five years?

Shawn 45:10 Skill wise, I think tech to Stephanie's earlier question, I I am a bit biased towards technically inclined folks or at least folks who demonstrate a willingness to do that. Mindset, I think, is more interesting and more important. I think it's important to have folks who are generally creative and more curious and able to communicate clearly the limitations of what they've done and impact it might have on the business. I think creativity is something that can never really be replaced or really thought through or think differently. Creativity has a downside, right, because I could be creative and I could be unique and I can think about all of these or I like to think I am, where, you know, I can always try to come up with all this stuff, but then I can strike out, like, 15 times. And by striking out, I mean, literally spending hours over there working on something that somebody said which might be important for us, and they don't know that I've spent six or seven hours trying to figure it out. And, oh, by the end of the day, I take the average of two different sets of people, and the numbers are one point six and one point six five. And I'm like, good god. Same thing.

Stephanie 46:32 Really. Waste out more time. Yeah.

Shawn 46:34 So that's the downside of the creativity is spending an inordinate amount of time and being very obsessive about things. However, on the flip side, when the creativity or the, you know, that sort of mindset or curiosity works. It's very impactful, and it's very helpful. So I do feel technical stuff is always a little bit important, and I am biased toward it. But I do just think not more importantly, I do just think curiosity and creativity will win well over a lot of other things. I do.

Matt 47:11 That's it's a great answer. Yeah. I mean, I think it's it's really on the theme with, uh, a lot of the conversations we've had. It seems to be kind of a universal truth in that, which I really like. I look for I like a simple universal truth. ternal, really, like, see the forest through the trees including if they've done the if we have had done this work in the past. Like, that's like, that, like, information management is a role that I see people taking more and more of an interest in.

Unknown 44:17 That's really well said. I think that's critical.

Stephanie 46:32 Really. Waste out more time. Yeah.

Matt 47:11 That's it's a great answer. Yeah. I mean, I think it's it's really on the theme with, uh, a lot of the conversations we've had. It seems to be kind of a universal truth in that, which I really like. I look for I like a simple universal truth. creativity will win well over a lot of other things. I do. That's it's a great answer. Yeah. I mean, I think it's it's really on the theme with, uh, a lot of the conversations we've had. It seems to be kind of a universal truth in that, which I really like. I look for I like a simple universal truth.

Shawn 47:23 I'm glad I could help you out with that one because otherwise I'm

Matt 47:26 getting into a philosophical space now.

Shawn 47:28 That's right.

Matt 47:29 Well, speaking of philosophical questions, Stephanie has a a a deep philosophical question for you that we need to that we can't let you go without addressing.

Stephanie 47:37 It's true. And we don't usually end on a totally out of pocket question. But, Sean, you showed up on my doorstep today, and I have a burning question that takes up way too much mental space for me. Where do pharmaceuticals come from, and how are they tested embedded?

Shawn 47:57 So in interestingly, first, I think I've only been a part of, like, a very small amount of these or even tangentially involved in them. Most of my stuff has to do more with, like, you know, commercial and opportunity afterward. However so I'm not a complete top out to the what I well, to the best of my knowledge, first, giant list of things brainstormed. How those things are initially created, I think it's just we can't be too duplicative of something else. We can't be outright or explicitly promotional in the name, those sorts of things. And then as more and more of these names have promulgated over time, it becomes more and more difficult to be unique different than so I think a lot of these names a lot of these sets of letters gets generated. A lot of questions on folks' associations with those things? Positive, negative? What do you think about? Then a lot of diligence to make sure we're not unduly violating anybody else's names, or we never want to promise a benefit that has not been clinically demonstrated. So we can't have those associations either. So I I think it's less it's less interesting than it is just ensuring we abide by the act rules and regulations regarding how things proceed here. Admittedly, though, I've not it's not really something I've been super involved in except once or twice, but that's how I've seen it play out. At the end of the day, we just want to ensure that anything we put out there, any end product, one you know, once marketing has its way with it, it's fair and balanced and genuinely demonstrates what we believe to be true about the clinical value of the, you know, product, and we don't never want to confuse or dissuade or or change anybody's or or trick people or promise something that isn't true. So, you know, that's the fundamental nature of what we do. So it's more on the diligence aspect of things that

Stephanie 50:07 Yeah.

Shawn 50:08 The fun.

Stephanie 50:08 I think we don't see that as much in consumer naming and of other, right, of other types.

Matt 50:13 I think

Shawn 50:13 you guys can call anything.

Stephanie 50:15 Anything anything as long as there's an is a fringe on another name. Yeah. creativity will win well over a lot of other things. I do.

Shawn 47:23 I'm glad I could help you out with that one because otherwise I'm

Matt 47:26 getting into a philosophical space now.

Shawn 47:28 That's right.

Matt 47:29 Well, speaking of philosophical questions, Stephanie has a a a deep philosophical question for you that we need to that we can't let you go without addressing.

Stephanie 50:07 Yeah.

Shawn 50:08 The fun.

Stephanie 50:08 I think we don't see that as much in consumer naming and of other, right, of other types.

Matt 50:13 I think

Shawn 50:13 you guys can call anything.

Stephanie 50:15 Anything anything as long as there's an is a fringe on another name. Yeah. : Yeah.

Shawn 50:08 The fun.

Stephanie 50:08 I think we don't see that as much in consumer naming and of other, right, of other types.

Matt 50:13 I think

Shawn 50:13 you guys can call anything.

Stephanie 50:15 Anything anything as long as there's an is a fringe on another name. Yeah.

Matt 50:19 It's almost completely different. Like, it sounds like, you know, like, Shelley, like, you're abiding by regulations that say it has to be unique. It can't be descriptive really of, like, what's in the drug or what it's meant to do. Like, there there has to it can't be overly, like, generalized. It's gotta be your and then to your point, it's really interesting to hear that it's gotten so to the point where there are so many unique names out there. It's hard to make a name unique because they all just sorta start to sound like the same level of randomly generated syllables.

Stephanie 50:51 I feel like we've really leaned into the letter x as the letter start drug names with. So

Shawn 50:56 There's only so many letters left. I mean, we

Matt 50:59 need to

Shawn 50:59 go out, but so you gotta get there. Yeah. And and if it's changed, the last sort of naming ish thing that I was sort of kind of included on to some extent was about three ish years ago. So if it's changed or anybody's listening is like, this person doesn't know what he's talking about. It's question two and a half years. This is my caveat that if it's changed, that's great. But I, again, I think the main emphasis that I wanna make sure folks know about is, like, the diligence on making sure we're abiding by rules and regulations that have been passed down to us is is very critical for everybody who takes these jobs here.

Unknown 51:35 So Right.

Matt 51:36 Safety. Well, Sean, thank you so much for your time today. This is a great conversation. Really appreciated having you on the show and, uh, really appreciated all of the insights you shared. Just just had a great time talking to you.

Stephanie 51:48 Absolutely.

Shawn 51:50 Thank you, guys. Thank you, Matt. Thanks, Stephanie. Appreciate it.

Matt 51:52 Thank you.

Stephanie 51:54 The curiosity current is brought to you by A YTM.

Matt 51:58 To find out how A YTM helps brands connect with consumers and bring insights to life, visit aytm.com.

Shawn 52:04 And to

Stephanie 52:05 make sure you never miss an episode, subscribe to the Curiosity Current in Apple, Spotify, or wherever you get your podcasts.

Matt 52:13 Thanks for joining us, and we'll see you next time. : Yeah.

Shawn 50:08 The fun.

Stephanie 50:08 I think we don't see that as much in consumer naming and of other, right, of other types.

Matt 50:13 I think

Shawn 50:13 you guys can call anything.

Stephanie 50:15 Anything anything as long as there's an is a fringe on another name. Yeah.

Stephanie 50:51 I feel like we've really leaned into the letter x as the letter start drug names with. So

Shawn 50:56 There's only so many letters left. I mean, we

Matt 50:59 need to

Unknown 51:35 So Right.

Stephanie 51:48 Absolutely.

Shawn 51:50 Thank you, guys. Thank you, Matt. Thanks, Stephanie. Appreciate it.

Matt 51:52 Thank you.

Stephanie 51:54 The curiosity current is brought to you by A YTM.

Matt 51:58 To find out how A YTM helps brands connect with consumers and bring insights to life, visit aytm.com.

Shawn 52:04 And to

Stephanie 52:05 make sure you never miss an episode, subscribe to the Curiosity Current in Apple, Spotify, or wherever you get your podcasts.

Matt 52:13 Thanks for joining us, and we'll see you next time.

Show full transcript Hide transcript