Description
In this episode of The Curiosity Current, hosts Matt and Stephanie speak with Louisea Hudson, Principal Customer Researcher at Amazon, about leveraging diverse thinking styles in research, the power of mixed methodologies, and how neurodivergence can enhance innovation and customer understanding.
Transcript
Louisea - 00:00:01:
Then there's also like this magic of following the tea leaves. Like what is the story that the data is telling? Because the data is always telling a story. And that's the creative part. As customer researchers, we're not just the data keepers. If we can allow for that natural flow divergence and convergence, then it really helps. And not thinking that it's a linear path. Like it's not a linear path. It's really a loop.
Stephanie - 00:00:30:
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 - 00:00:40:
I'm Matt Mahan. Join us as we explore the ever-shifting landscape of consumer behavior and what it means for brands like yours.
Stephanie - 00:00:47:
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 - 00:00:58:
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 - 00:01:05:
So whether you're a seasoned pro or just getting your feet wet, we're excited to have you on board.
Matt - 00:01:11:
So with that, let's jump right in.
Stephanie - 00:01:14:
Today on The Curiosity Current, we are thrilled to welcome Louisea Hudson, Principal Customer Researcher at Amazon and a leading voice in innovation insights.
Matt - 00:01:24:
Louisea's career spans over a decade at powerhouse organizations like Procter & Gamble and Amazon, where she's helped shape how customer-centric thinking drives product development and strategic decision-making.
Stephanie - 00:01:36:
But what makes Louisea truly stand out is how she bridges deep analytical expertise with creative intuition. She's not only a champion of mixed methods research and innovation, but also a published author.
Matt - 00:01:49:
In this episode, we'll explore the power of integrating different research approaches, why neurodivergent perspectives are a superpower in the insight space, and how curiosity drives inclusive, transformative work.
Stephanie - 00:02:01:
Louisea, welcome to The Curiosity Current.
Louisea - 00:02:03:
Thank you. I appreciate it. I'm almost like blushing from the intro.
Stephanie - 00:02:08:
It's the best part.
Matt - 00:02:10:
That's our goal. The flattery is on us. That's what we do best. So we always start out with a question that is on everybody's mind when we're speaking with a guest who has, you know, such an interesting background and history with insights is what originally drew you to this career? What made you interested in becoming a consumer researcher?
Louisea - 00:02:33:
I think it was honestly love at first sight. I did not even know that this type of career existed. However, I knew that I love solving problems. I knew that I love advocating for people who can't advocate for themselves. I love storytelling. And then also, I was a naturally curious person from childhood. I was the kid who was always told, you're super inquisitive. You ask a lot of questions. And then sometimes, do you ever shut up? Questions, because I always needed to understand the why. And so during my path of internship, I was introduced to products research or customer research. And when I saw it, I was like, wow, I would be able to do all of those things. And on top of that, I can almost be an investigator because I did at one point in my life want to be an investigator. However, I'm kind of a scaredy cat. So, did not help me out, but I do love a true crime podcast. So I would say like, as soon as I found out that customer research was a career, I started finding my ways, like, how can I get there? How can I find a job in customer research?
Matt - 00:03:47:
Yeah, I love that. I have not yet heard that analogy made yet from any of our guests. And I think that's a really interesting perspective. It's, yeah, you get to be a private investigator without the immediate danger of harm to your person. I like that.
Stephanie - 00:04:02:
Good stuff. So your work spans both scientific rigor. And I say that like, because I know you as a quant researcher, right? Like at AYTM, that's what I see you doing. But also a lot of creative exploration, given that your focus is on innovation. How have those seemingly different modes of like thinking and doing research informed your approach to insights overall?
Louisea - 00:04:27:
Yeah, I like to say that customer insights or research is really both art and science. I think this is also where we see a lot of individuals who are scientists are very like right brain. And so I truly believe that you have to follow the scientific method. You have to like do your, compare your apples versus your apples. But then there's also like this magic of following the tea leaves. Like what is the story that the data is telling? Because the data is always telling a story and that's the creative part. Or like sometimes like if you notice that you get some data back and you're like something else is going on. There is something in the undercurrent that I cannot put my finger on and it won't let you go. And that is the artistic piece.
Stephanie - 00:05:17:
I love that. Sort of along the same vein, because, I think some of that plays into mixed methods. But let's dig into that a little bit because I'm curious like if you could talk about what you think is or are the biggest opportunities when it comes to combining qualitative and quantitative approaches. Like is there a particular kind of project or stage of innovation where the sum of those things is just greater than its parts? Like by doing them together you are exponentially kind of building the insights that you get.
Louisea - 00:05:48:
I'm going to answer this question in two parts.
Louisea - 00:05:51:
Okay.
Louisea - 00:05:51:
For me I really believe that the first thing you have to do is get your objective right. Like the crisper you can get your objective then you should marry it with the methodology that works best for that to answer the problem. And so if we can treat that like product development and it's like you're more in love with the problem or the objective then the methods will follow you there. And whether it's a combination of qualitative or quantitative you should be able to marry it with the objective. The second piece of that is I've noticed that mixed methods. It's really, really good when you are building new to world products or products that require a mental model shift from the customer, whether it be behavior or they're thinking about something or there's a new form. But anything that's new to the world, I really believe that mixed methods just elevates the learning and the product development cycle.
Stephanie - 00:06:53:
That makes a ton of sense. Do you ever have, like within the organizations that you've worked at, do you ever have like stakeholders who are more wedded to one versus the other and you've really had to like make the case that like, no, qualitative is you're going to miss all kinds of things if you don't do a qualitative piece of research here?
Louisea - 00:07:10:
I think we will, as researchers, we will always have those stakeholders that love to lean on either conjoints or they only want to lean on focus groups. And it is our jobs. It's like the advocates for the customers to allow them to see the beauty in both and what it can bring you. And so I also, I feel like as customer researchers, we're not just the data keepers. We are the voice of the customers. We are the people that brings their stories to life, their pain points to life. And you can only do that if you truly understand your data, whether it's quantitative or qualitative, and building that trust so they can know like, hey, if they are the voice of the customer, then what I should tell them is like, this is what we want to learn. These are my objectives. And then trust them to go and do it. But you have to build that trust and bring that open communication to the table and constantly fight for your customer, even when it feels like it's an uphill battle.
Stephanie - 00:08:10:
Absolutely. You know, that is a common theme that I feel like we hear. And that is a good takeaway for me because I think a lot of our guests tell us that, right, that it's not just your job to be the gatekeeper, right? Your job is to have a point of view and to really share it and advocate for that customer point of view.
Louisea - 00:08:27:
I will tell you one thing that has truly helped me, and I got this piece of feedback early on in my career, is the ability to bring those anecdotes to life so that people can feel like your customers are real people versus like 50% top box.
Stephanie - 00:08:43:
Right. Weighted top box.
Louisea - 00:08:45:
Weighted top box.
Matt - 00:08:48:
That is also another theme that we've heard a lot over the last few months is the reemergence of the importance of the researcher to be able to tell a real story and make the data and the findings real so that they stick and they're meaningful. And yeah, I think that that's just such a valued skill set that a really good researcher, particularly one, you know, like you who's got, you know, a lot of seasoning and in mixed methods who can look at things from multiple perspectives is able to do. When you focus in on one particular methodology, it is very easy to get focused on or really focused on the numbers and the quantification if you're a quant researcher or the verbatims if you're a qual researcher. But like if you want to paint that portrait, you've got to have all angles. That's such a great point.
Louisea - 00:09:36:
I will also add on to their point is don't discount immersing your stakeholders in your process.
Stephanie - 00:09:44:
Oh, amen.
Louisea - 00:09:44:
Like with quantitative and qualitative, like they have like every time I'm running research, I'm always inviting my team. I'm always inviting my stakeholders. Like I encourage them to like join, hear this for yourself. And they always come back with a stronger point of view when they join themselves, even versus like watching a highlight reel. So they'll actually be listening in on the interviews, being in the observation rooms, joining the in-homes. It brings so much power to the customer's voice. And then I've even done things from a quantitative standpoint where we will be in a meeting and someone will ask me a question about data breakout. I'm like, let me share my screen and let's look at it together.
Stephanie - 00:10:25:
That's cool.
Louisea - 00:10:25:
Yes. Let's do the breakouts together so you can see like I'm just not bringing you a percentage. Let's let me show you, hey, these are all of the target customers versus Gen Pop. And what are your questions? Let's put it on the side. Let's do a categorical analysis together.
Matt - 00:10:40:
That's such a great point. It adds to the credibility too, right? As a researcher, like they see, not that they don't inherently understand there's a lot of work that happens behind the scenes, but when they actually see it or when they're actually a part of it, they have skin in the game, it elevates that relationship with your stakeholders to a whole new level.
Stephanie - 00:10:57:
So one thing that we're really excited to talk to you about today is neurodivergence and the idea of different cognitive styles and ways of thinking as an asset in insights. And I'm curious how your experience has shaped your perspective on the value of diverse thinking and research.
Louisea - 00:11:14:
Of course, I love this question and I love this topic. So I just have to let everyone know. So like a couple of years ago, I was actually diagnosed with ADHD. And I feel like it really put the world into perspective for me. Things that I felt like did not make sense or made me feel different was actually the symptoms of like ADHD. And so sometimes I would get feedback from stakeholders or like either my manager. You're really good at surfacing unobvious connections. And to me, like that piece of feedback really did not make sense to me because all of the connections that I was making in my research or my findings was obvious to me. It was like looking at a picture, seeing that the sky is blue.
Stephanie - 00:12:03:
Yeah.
Louisea - 00:12:04:
And sometimes other people didn't see it. And so I realized that, oh, that is a part of me having ADHD. As well as the part of like, why do I like brainstorming so much? How can I see all of these possibilities? Why is it easy for me to connect with customers so much and like using their body language as a sign of like as a data point? Like even like when I'm interviewing people and I see that, oh, their body language changed. They're saying one thing, but the way that they're looking is totally different. And I will call them that in an interview in a very polite and respectful way. And I'm like, you're saying yes, but you're frowning. Can you tell me a little bit more about that? And so I would also hear stakeholders say like, oh, you're really good at interviewing and making people feel comfortable. But that's part of like the ADHD, like the being sensitive and the input and being super empathetic. And it just married really well with research. And not only did it marry well with research, but it married well with this like new to the world product development, which I really like. I have to be honest that when things are gray and very ambiguous, I am in love. It's like I'm in a playground. However, once the product is defined, once it's like all I'm doing is conjoint and max diff and maybe only just focus groups. And now we're just getting to claims and demos. I'm like, you know what? This is not for me. Let's go solve a problem. I get very bored quickly or like I'm always probably going to have a typo somewhere because once it gets down to the details, I've checked out.
Stephanie - 00:13:46:
For sure. I think it's such an amazing point to bring up in the context of neurodivergence and just, you know, genetics and epigenetics and things and like viewing those things as superpowers. And I mean, I think we've even like science shows the more that you can think like that, the more that that type of thing becomes true for you. But it's also so interesting to me to think about extending it to all the ways we're different, right? Like cultural backgrounds and experiences, even different educational backgrounds, like how we got to market research, because I feel like every one of us took a different path. And it's I feel like that diversity in market research is the superpower because it's like we all the way that I kind of think about it is we all have a perspective or a lens. I like a little viewfinder, right, that we're looking through that's very narrow. And I, through my own viewfinder, I'm not going to see everything. But if I'm standing next to you and Matt in a circle and we all have a viewfinder, then we're going to have a pretty good view of this problem or this issue that we're trying to solve, you know?
Louisea - 00:14:46:
Yes. And I find like throughout my career, the most diverse teams. And I mean that by like backgrounds. I'm talking about neurodivergence and neurotypicals together on teams, even like experience levels. Those are some of the most high functioning teams that I have ever been on where everyone's strengths complements each other. Like where I'm weak, someone else is strong and we're there to be curious. We're not there to let my idea win. It's like, no, how can we let the customers win?
Stephanie - 00:15:20:
For sure. I love that.
Matt - 00:15:22:
So I'm sold. I think it's such an interesting point. And it has me sort of remembering this experience from my past, which is not particularly interesting, but it pertinent. And it's we talk a lot in research about this, like, divergent thinking and then convergent thinking and like this process of, you know, like you think, like, okay, you brainstorm, you have all these ideas and then you validate the ones that that you think are going to have legs in market. And, you know, I think there's this tendency for researchers to think of divergent methods as being largely qualitative and then convergent methods being largely quantitative. And a lot of the like the people that I went to school with and started out in research with, you know, I was a classically trained market researcher doing a lot of quantitative research. Like we were we were drawn to that type of work because of the quantitative precision. And then, you know, fast forward a couple of years, there was this whole like Agile Design Thinking transformation. Everybody was doing workshops on on what divergent and convergent thinking looks like. What does the build, measure, learn loop look like from an agile perspective? And we really struggled with the divergent thinking process and thinking, dedicating time, you know, basically permitting us to sit in that sort of nebulous gray area.
Matt - 00:16:42:
The gray area.
Matt - 00:16:43:
Just thinking about was the hardest thing because we wanted out of there so quickly just because that's not where our minds operated. Like it's like, oh, no, this is not this isn't research. This isn't right. We've got to get out of here. We've got to start getting those numbers down and narrowing in on a direction. We didn't even like give ourselves the permission to sit with the discomfort of not having an immediate answer. And I think that's one of the coolest things that I took away from that experience, which was, you know, you can you can actually build a process around that. Like it is okay, to sit in this less defined gray areas as long as there are parameters. But you really got to embrace it. And I think looking at folks with different perspectives, different backgrounds, I think are better at that than than others. And it just speaks to the importance of, of having this this diverse approach to your research team. So given all that, how do we do that? Can you share any reflections on how teams or organizations can better structure the research process to include that type of talent?
Louisea - 00:17:49:
Yeah, I think, too, one, allowing for a fluid process. Like when I said in the beginning, like being very bullish on what your objectives are versus how you get there. I like to think about it like qual is helping you figure out what type of map you're going to build. And then quant is telling you, did you build the map correctly? And I think like they're in the nebulous place. You have to first figure out like, hold on, let me start over because I'm going somewhere else. You started talking about directions and maps and then I'm like, you're like, I don't like this map analogy anymore. Okay, yeah, but it was a good. Let me go back to the original question. So you were asking about how can we make the process? Okay, if we can make the process more fluid, what I've noticed, particularly about myself, because I can only speak about myself in this process, is that I cannot follow the set standards when it comes to a methodology. There is always something that I'm going to tweak so that it can better answer my objective. I'm never just going to do just a conjoint. Stephanie, you probably know this better than anything. I'm always asking you, can we add this on top of it? Because I also need to know this. I think allowing the process to be fluid and allowing the process to be to diverge and allowing understanding and listening, because a lot of people who may be neurodivergent, we ask questions to learn. It's not our challenge. It's like allowing for individuals to ask questions, to gather the evidence that they need because they're building their boards, like they're building their investigative boards to get the answer. And if we can allow for that natural flow divergence and convergence, then it really helps. And not thinking that it's a linear path, like it's not a linear path. It's really a loop. I think about it like a snowball. Like the snowball gets bigger as you build and iterate and learn and build and iterate and learn and build and iterate and learn. And so if we allow for the process to be fluid, the individuals can show up and not be perfect. Like we don't always have to validate something. We don't always have to have the right answer. Sometimes it's just a signal because at the end of the day, honestly, with research, we're providing signals. Nothing is actual until it's out on the market and somebody is spending their money. Until then, we're just providing signals. And how can we provide high fidelity signals by allowing the process to be fluid?
Stephanie - 00:20:34:
Yeah, I like that. So turning our attention just a little bit, because we've talked a lot about kind of inclusivity in the research team, which I love because I don't think we've ever covered that in our sessions that we do. But there's also a growing conversation around designing research that's more inclusive for customers and consumers and respondents. And, you know, not just in terms of demographics, which is a big part of it, but also in how questions are asked and how data is collected so that, you know, it's accessible for people. If you were going to score us, like, where do you think we are in that process just as an industry? Do you think we're making headway in a way that, you know, in a way that we should? And if not, how do we get there?
Louisea - 00:21:16:
That is a very hard question to ask. And I'm going to try not to make people upset when I say this, but I do think that we have, I think we have a long way to go because right now I think that we think about inclusivity and accessibility in very like black and white terms. You know, like everyone is so different and so unique and everyone can bring a different perspective to the table. Like I also think about how I use different customers for different pieces of research. If I'm going to be doing co-creation research or if I'm doing some research where I really need people who are super right brain, they may be tastemakers in their area, then I need to recruit for those types of people. Also, if I'm trying to do like some emotional benefit mapping, then I'm looking for people who are like super expressive. But sometimes when we're screening for people who are verbally expressive, we're forgetting about the people who are really good at journaling or people who are really good at writing things down. Or they may be better in a diary mission versus in a qualitative interview. And sometimes we may count them out. So it's like also figuring out like if I'm doing a certain piece of research, what type of people do I need? And how do I make sure that I am accounting for everyone's voice who I'm building this product for? And not just my own experiences. I think another thing that we have to do better as researchers is when we're talking to our counterparts. Kind of expressing to them that they're not always the customer. And how do we build empathy for our customers so that the customer does not always look like us, think like us, sound like us, be in the same tax bracket as tax bracket as us or discounting audiences who are underserved? Like the way to disrupt any market is when you are making technologies or products more accessible.
Stephanie - 00:23:20:
Right. I mean, that's the whole point of technology, right, is to create accessibility. So, yeah, it is. It's sort of unfortunate to look and see. You know, I mean, I definitely think we've made progress. But to your point, not enough, you know.
Louisea - 00:23:32:
And that's why I say, like, we are the advocates of the customer. Like, every day I get to come to work. And give a voice to someone who does not have a voice in a large company.
Matt - 00:23:45:
It sort of leads into one of my last questions, which, you know, given the growing use of AI, primarily large language models in research and insights, and given, you know, there's been a lot of research, a lot of work, a lot of discussion around LLMs and whether or not they are capable of capturing the, you know, the output or sentiment or whether or not they are fully representative of groups, individuals that have largely been marginalized. Is there a place in your mind, in your opinion, is there a place for AI right now in this mixed method research approach? Is there an area where AI is helping to elevate that? Are there things that you would urge researchers to be cautious of in their deployment of AI? What's your stance on that?
Louisea - 00:24:47:
I want to play back your question to make sure that I'm understanding it correctly. You want to understand, like, what are my thoughts on the role of AI as a researcher when we think about accessibility?
Matt - 00:25:00:
Yeah. So, you know, how do you see AI and automation enhancing mixed methodology research? Or do you even see it enhancing? Do you see it as, you know, a tool we should be wary of or should we be kind of leaning into it? You know, are you excited about the way AI is being used in research? Are you excited about what it can do for us as researchers?
Louisea - 00:25:26:
I am excited. I'm excited for tools that AI unlocks. I'm also excited for the way that AI can help us scale as researchers. Also excited about the way that AI can almost become a translator between me and my stakeholders, especially when they are not as close to the data. Like I've used it so many times where I would put like my very like scientific research into AI. I'm like, okay, now help me translate this insight so that my product manager can understand it or someone who has never done research can understand it and what it means to them and what it means for the product impact or the business impact. And so that has saved me so much time because how much time do we spend on writing documents and trying to make sure that our audiences understand the message that we're trying to provide?
Stephanie - 00:26:24:
And that is something that AI tends to be, especially LLM's good at, is finding that voice, right? That's not your inherent voice, but is the voice that your stakeholders need. So it's a great use case.
Matt - 00:26:35:
Totally agree. It's so great at doing exactly what it does, which is generate language. I mean, it's fantastic for tweaking content in that way. I think that's a brilliant use of it.
Louisea - 00:26:48:
I like that too.
Louisea - 00:26:49:
I'm also a solo researcher on my team. So one thing that I've used it for is to poke holes in my methodologies. Like I would put my study plans into the AI tool and like, tell me what am I missing? These are my objectives. This is how I'm planning the study. I want you to poke holes in it. And I also want you to tell me what the strengths are because I'm also one of those people like I need sugar first before you get me salt.
Stephanie - 00:27:17:
Even from your AI, you need a little bit of like positive feedback to take the negative. That's right.
Louisea - 00:27:24:
And then it really helps to be like that brainstorming partner. I think it's a great companion and we shouldn't shy away from it. But I also think that we should be aware of like there are human nuances that we will forget. And AI should not know your customer data better than you should know your customer data. Like if you're using it for like transcripts and stuff, like I've seen mislips where I'm like, no, Brittany F did not say that. That was Jessica M, you know. And so, yeah. So like you who know your data better than AI, you just cannot solely use it like, oh, summarize this for me. No, it's a companion. It is a friend. You can treat it like, are you all familiar with Amelia Bedelia? So I really treat it like Amelia Bedelia, like very specific on how I'm asking AI to help me in my everyday job.
Matt - 00:28:16:
A great call out. Yeah, it still slips up. Yeah, there was a transcript of a meeting we had internally not long ago, and it assigned all of the follow ups to me. I was like, whoa, whoa, whoa, whoa, this was not what was agreed upon in the call at all. And hey, I was like, this is all on you, Matt. You got it. Good luck.
Stephanie - 00:28:32:
Oh, that's great. Well, Louisea, this has been just a really fun interview, I think, for Matt and I. Really refreshing. Really great to hear about kind of the way that you approach things, which is probably a little bit differently from Matt and I. And what I've learned about that is that is a good thing. And so to close this out, if I could ask, we often like to ask our guests if there's a piece of advice that you would give to a younger person in the industry, maybe who's just starting out, who thinks a little bit differently or doesn't quite fit the traditional mold of a market researcher, what would you tell them?
Louisea - 00:29:06:
I would tell them to lean into their differences. I would tell them that it is their superpower no matter how painful it feels in the beginning. I would also tell them that it is okay to be different because being different is what makes them unique and allows them to see the data in a way that no one else will see the data. My second piece of advice is always look for the story that the data is telling. I strongly encourage people to not just present. What they find in the data, but tell the story. That was one of the best pieces of feedback that I've ever received from a mentor.
Louisea - 00:29:49:
Yeah.
Louisea - 00:29:50:
Oh, I do have one more piece of feedback.
Stephanie - 00:29:52:
Yes, let's do it.
Louisea - 00:29:53:
There are many different ways to do research. You do not have to stick to just one way of doing it. I would try out as many different roles as possible until you find something that fits. Even in finding something that fits is you combining multiple different roles or fields and even try different fields. Just because you start in CPG does not mean you have to end in CPG. Just because you start in tech doesn't mean you have to end in tech. Or just because you started in academia doesn't mean you have to end in academia.
Matt - 00:30:26:
It's great advice.
Louisea - 00:30:27:
For sure. Yeah.
Matt - 00:30:28:
My closing question is always to look in your crystal ball and think if you had to predict one major shift that is coming our way in terms of how we conduct research, you know, five years from now, what are we going to be doing differently from today?
Louisea - 00:30:45:
I honestly think that we're going to be leaning more on synthetic user data. And a hope for is I hope that we find ways to make the research process for our customers more engaging and more realistic, as well as like when we think about how can we bring gamification into research. I know that there are some emerging companies who are working on it, but I think that it would be more widely accepted. I also believe the way that we ask questions will change, especially how we think about how we consume social media today. How could they more closely align with that when we're trying to engage customers when it comes to collecting data?
Stephanie - 00:31:28:
It's a lot of change.
Stephanie - 00:31:29:
It's cool stuff. Yeah, I love it. Well, again, thanks so much, Louisea. It's been a pleasure as always. Thanks for joining us today on The Curiosity Current. And we'll see you around the office, as they say. I'll see you on the platform.
Louisea - 00:31:42:
Yes, it's always a pleasure.
Stephanie- 00:31:46:
Curiosity Current is brought to you by AYTM.
Matt - 00:31:50:
To find out how AYTM helps brands connect with consumers and bring insights to life, visit aytm.com.
Stephanie- 00:31:56:
And to make sure you never miss an episode, subscribe to The Curiosity Current in Apple, Spotify, or wherever you get your podcasts.
Matt - 00:32:05:
Thanks for joining us and we'll see you next time.
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