The coming qualitative renaissance with Deborah Mendez

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Summary

Deborah (Deb) Mendez, a consumer insights leader whose CPG career spans Kenvue, Mars, Kraft Heinz, and Pharmavite, on using quantitative research to surface the consumer unconscious, why the say-do gap is an opportunity to disrupt a category rather than a data-quality problem, why shallow budgets force deeper critical thinking, why problem framing and judgment are the two skills that must stay human in an AI-shaped research world, and why a coming 'qualitative renaissance' will feed the very models that quant once dominated.

Guest

Deborah Mendez — Consumer Insights Leader, Pharmavite · Brand-side

From this episode — top claims

• The two critical thinking skills that must stay with the human in an AI-shaped research world are problem framing (the inputs — asking AI the right questions, delegating the right tasks, designing the right agents and workflows) and judgment (the outputs — pressure-testing whether the AI's reasoning is logically sound). — Ep. 60, Deborah Mendez
• The great storytelling unlock is shifting from telling the audience everything you know to telling them what they need to do to make their business decision — narrowing a million data points down to the five that make the decision clear, with everything else relegated to the appendix. — Ep. 60, Deborah Mendez
• The mismatch between what consumers articulate and what they do is an opportunity to disrupt a category: the first brand that lets consumers stop having to choose between competing values wins their preference, and over time that resolved tension becomes the new point of parity while fresh mismatches open up. — Ep. 60, Deborah Mendez

aytm's take on this conversation

Stop treating the say-do gap as dirty data. When stated values and shelf behavior diverge consistently, that recurring contradiction marks an unmet conflict no product resolves — chase it as your opening to disrupt the category, not an error to discard.

aytm's perspective, voiced by Stephanie.

Full transcript

Auto-generated captions — speaker labels aren't always available and wording may be approximate.

0:00 A decade ago, we were seeing consumers articulating things like I want to be environmentally conscious and I prefer packaging that it's cleaner or ingredients that are cleaner. But then, you might conduct a conjoint analysis and realize that they are making decisions based on price or that they prefer private label despite everything they tell you about how they prefer to protect the environment or their families with better ingredients. Then, this doesn't necessarily mean the consumers are lying, but it might be that they are in conflict because back then, there might have not been that many options to do both, to be budget-conscious and convenient and environmentally friendly.

0:52 But that tells you that there is an opportunity to disrupt a category there, but giving them what they tell you they want, but they are not empowered to choose. Hello, fellow insight seekers. I'm your host, Molly, and welcome to the Curiosity Current. We're so glad to have you here. And I'm your host, Stephanie. We're here to dive into the fast-moving waters of market research, where curiosity isn't just encouraged, it's essential.

1:21 Each episode will explore what's shaping the world of consumer behavior, from fresh trends and new tech to the stories behind the data. From bold innovations to the human quirks that move markets, we'll explore how curiosity fuels smarter research and sharper insights. So, whether you're deep into the data or just here for the fun of discovery, grab your life vest and join us as we ride the Curiosity Current. Today on the Curiosity Current, we are joined by Deb Mendez, a consumer insights leader whose career spans some some biggest names in CPG, including Kenview, Mars, Kraft Heinz, and Pharmavite.

2:02 Deb works at the intersection of agile insights, brand growth, and strategic influence, helping teams turn consumer understanding into sharper creative, stronger business decisions, and more effective brand plans. Her work also spans brand strategy, communications, and insights generation with a strong focus on using quant data not just to measure what consumers say, but to get closer to what's actually motivating them beneath the surface.

2:30 So, today we're exploring how to use quantitative research to uncover perhaps the less obvious truths in consumer behavior, how to build deep insights when budgets are under pressure, and what it takes to develop critical thinking talent in a perpetually AI-shaped research world. Deb, welcome to the show. We're so excited to chat with you today. Likewise, I'm really happy to be here. I love Curiosity Cruent to talk. I'm happy to be a guest now.

2:57 It's always fun to have a fan. So, Deb, you've built a role where insights clearly do more than just validate decisions that have already been made. Looking back, I'm curious, is there a point where you realized that quant research could be a really powerful tool for uncovering not just what consumers say, but what they're not able to fully articulate? Yeah. Yeah, I think I think there was definitely a shift in the industry that accelerated that. I think that if you look back 15-20 years ago, a lot of the quantitative studies were large budgets, a slow fielding, with long questionnaires and super large sample sizes that, of course, because of the of the large implications in time and budget, they needed to be signed off with by so many cross-functional members, right? Like a same questionnaire would get revisited so many times and you would get data points that were giving you information about

3:58 that field in about one point in time. But then with agile research and big data, we were able to field multiple iterations sometimes of the even same question getting more data points and then the value wasn't much about about one data point, but it was more about the patterns that multiple data points were giving you. So then once you start identifying patterns, it's it's very easy to understand when something is off pattern and that triggers your curiosity, right? That that it's the tip of the iceberg that tells you there might be something more interesting here and when where you can start peeling that onion by getting different data cuts or by getting longitudinal data points or by asking the same question

4:54 in a different manner or by asking the same question at different points in the questionnaire when the respondent is fresh or when the respondent is exhausted. And identifying those discrepancies is what tells you like, "Okay, I know that consumers are aware of this, but they are contradicting themselves and there there might be a deeper reason here." And that is something that really is always really fascinated me about your your work and the passion that you have for this, which is the consumer unconscious of things that impact consumer behavior that they don't even know, that they don't even realize. So when you say that and you're looking to measure that, what does that look like in practice?

5:39 Yeah, I mean I think um there are a few examples that come to mind, but I think that yeah, maybe a decade ago we were seeing consumers articulating things like I want to be environmentally conscious and I prefer packaging that it's cleaner or ingredients that are cleaner. But then, um you might uh conduct a conjoint analysis and realize that they are making decisions based on price or that they prefer private label despite everything they tell you about how they prefer um to protect the environment or their families with better ingredients.

6:22 And then these these that this doesn't necessarily mean that consumers are lying, but it might be that they are in conflict because back then there might have not been that many options to do both, to be budget conscious and convenient and environmentally friendly. But that tells you that there is an opportunity to disrupt the category there by giving them what they tell you they want, but that they are not empowered to choose. And that's when you see that that mismatch between what they articulate and what they do. And that tells you there is an opportunity to disrupt here because if I am the first brand that allows them not to be in conflict between what they say and what they do, they're going to prefer you, right? And that's Then then now you can see consumers being much more consistent between what they say and what they do because now there are there are brands

7:19 who have dared to give you them the option, right? So then you start seeing how category changes and that and those mismatches start to align, but then new mismatches start to appear, right? And that's where I think there's opportunity for for disruption and growth. I think that that is such a fascinating kind of process and indicators that you're chatting through with us. Um I was curious and I had a question for you that um around the sort of paradoxical nature of using structured survey data to uncover unconscious motivations and I was I was very curious about like the signals that you use and I thought maybe reaction time is some of what you're talking about, but I'd love to hear you you're taking it back. It sounds like you've been doing this before we were doing reaction time based experiments and really just looking for the mismatch between the kinds of things that we see people say and the kinds of choices they

8:16 make in kind of discrete choice exercises which have been around forever. So you're really describing a process that doesn't take the newest tech, right? It's it it's something that you could have you've probably been doing for years and years and that's that's fascinating. Yes. Yes. I I think you're a spot-on. I think um there are you can identify those those um frictions and tensions by um putting consumers not only in front of claimed behavior versus simulated behavior like a like a conjoint or a max diff. That's definitely one way, but also like creating um artificial environments that put them in certain situation, right? Like it can be shelf test or it can be adding artificial constraints like timing as you mentioned. So um seeing how consumers behave in those different environments

9:16 and how consistent or inconsistent can be uh can be very revealing about things that they might not be even aware and that's how you where you start tapping into that unconscious, right? They they don't know that they answered one thing but behaved like the other. They don't know the results of their own conjoint, right? Um and that's that's where that richness comes in. So, it for you is it like when we see those discrepancies, it is a cue to investigate further to really get down to the bottom of like why that might be occurring? Yes, for sure. When you see those inconsistencies, then you can start um yeah, peeling the onion and doing deeper data cuts of the same thing, all right?

10:00 Like, does this inconsistency show up with across all of my demographics or all of my behavioral groups or did it show up last year or does it show up um at a different season of the year or through a shopper of a different retailer? And and and that's where you start seeing the story unveiling itself and and getting giving you some depth um um them on and some cues on the potential whys for those discrepancies.

10:34 I wanted to call out something that you had said a bit earlier cuz I don't we talk a lot about the say-do gap on this podcast, but I don't think we've had someone necessarily say that the opportunity lies in actually the middle of that and it's your job as a brand to iterate a product that allows the consumer to not be in conflict with the with each other. I don't think we've we've seen that perspective before, which I just wanted to call out as as really really interesting of instead of we're trying to close this gap, we're trying to better understand what creates this gap, instead it's an opportunity.

11:10 I think to just to piggyback on that, uh Molly, I think it's that we treat the gap as something that is uh it means the say was not accurate, right? It's like what they said doesn't match what they do, so maybe they lied to us. Maybe this is a data quality issue. When in reality, that say-do gap exists in real life for consumers, for humans, right? Yeah. Yes, 100%. Um so, yeah, I think I've seen executives at major corporations say like, "Yeah, let's not for example, let's not invest in sustainability or higher quality ingredients." We see that at the end.

11:47 This is what they're choosing at shelf. And and true that that is true at that time and we can over simplify the consumer and saying they're lying. They want to portray an image of themselves that it's not true. At the end, they're looking for this. And in some cases, that might happen. But I think they're missing the opportunity to move the category in a direction that that it's more aligned with what consumers say they want to be and who and how they are behaving today. Uh and then there's going to be one competitor that's going to do that. They are going to innovate.

12:25 They're they I are going to fight for a of the share of the larger ones. And they're going to deliver the consumer exactly what they want. And they're going to get a share of the consumer. And and that's going to be an opportunity missed, right? So So we've seen we've seen that in categories through history where things that were uh unique 10 years ago, now they are the point of parity and not the point of distinction in the category, right? And that's how you see them evolve. So I think yes, sometimes consumers um might lie to themselves, but there is a reason. If if you understand that reason, then you can capitalize on it and and get growth rather than ignoring it and thinking, "Yeah, consumers consumers are lying or they or this is a bad survey that came out wrong." Yeah, sure. Those do happen. But um if you start seeing the consistency in the

13:23 inconsistency, that tells you there there might be something deeper. Yeah, it's like treating it as signal, not noise. Yeah, I love that reframing. Um I think that's going to be useful to a lot of people. There's a phrase in in research that I I've been kind of noodling around on lately around like deep insights, shallow budgets, because I think it really does like capture this tension that so many researchers, especially brand side researchers, are living with right now.

13:52 I'm curious in your roles, how have you protected depth when I I feel certain that you've experienced that pressure for speed and efficiency? I I love this question and again, this is one of those phrases where we're treating it as a contradiction, but I actually think they they can be very well um aligned because shallow budgets forces you to do deeper research. When you have um shallow budgets and shorter timelines, then your critical thinking needs to be flawless and that's where you can't sacrifice that. So, you can't sacrifice depth of thinking. So, truly taking the time to understand the needs of your cross-functional teams, to understand what's the business decision at hand. What are the business risks?

14:51 What's the cost of those business risks? How do you mitigate the risks? Then you can be very surgical about what are the data points that you don't have, that you don't have historically, that you haven't tested in the past and that you truly do need to get. But you can be so surgical, so specific, so intentional that then you can go deep into those data points, right? So, you can concentrate resources on being deep in what truly matters and isolate what you don't what you don't truly need. Isolate those nice-to-haves so that you might complement with what you already know or with safe assumptions that you might not be certain, but they don't put the business at risk. So, that way um yeah, those those those budgets and timing constraints can actually be very

15:48 helpful to shape the research in the right direction. It sort of forces you to crystallize what's most important, right? Exactly. Yeah, and I was even sort of thinking this push of the cheaper research versus the smarter research and what that looks like. It can be It can be very difficult to see a tightened shoestrings tightened budget around you and think, "I'm going to do everything that I need to, but I'm going to do it in this really cheap way." versus, like you're saying, really distilling down exactly what you're trying to get at and investing in that in a very smart way.

16:25 Yeah, I when I think about cheap research, I can imagine something that it's very broad and very shallow. So, yeah, you might not be able to do much with that. But, when you think about intentionally doing research with budgets and and tight constraints then I can imagine something very narrow and very deep um that actually helps you become smarter and that becomes part of your arsenal of historic research and it puts you in a better situation for a future business problem because now you you you have more relevant data points. And when we talk about doing research to empower business decisions, it's important to have that research as part of the process from the beginning. I know there's a lot of times where we see that research comes in at the middle of the process or the end of the process even to just kind of say that we did it, and it's not informing anything. It's potentially boxing it into just

17:24 validating it what could be a a pretty bad idea. So, what does that actually look like for you in practice when research gets to be at the beginning of the process and helps lay the fundamentals versus just something to cross the Ts and dot the Is at the very end? Yes, definitely. Yeah, I think I think that the function of the insights can move from validator to creator when they they are we are involved at the beginning of the process, right? So, when we were thinking about protecting depth by focusing resources on that critical thinking, when insights is at the table from the very beginning, it can definitely help reframe the business question, identify the problem, identify those risks, prioritize those risks, establish the business

18:21 um the the success criteria, identify the action genders, right? And then, when you finally do all that and you validate, then it should be an easier story to tell because insights have been carrying out that critical thinking framework and tools with a cross-functional team throughout the process. If you're involved later on, when you're already validating a concept or copy that you weren't a part of it, then yeah, maybe results aren't going to be that good, and then insights can also not be accountable.

18:58 There's a benefit for the marketer, too. If the marketer brings you in through all all the way and then you test copy and you test for, then okay, what what what was missing from an insights point of view throughout the process, right? But then if you're using you're leveraging your insights partner only to validate our results come forward, then you don't have that accountability partner there. So, yeah, definitely bring them along the way. It it's critical for the higher risks and the higher growth projects.

19:36 I I like that. I don't I don't think that I've heard somebody talk about the accountability aspect of that and how it it aligns everybody's accountability in a way that's important for that development process to have all of the inputs and all of the the you know, the right stakeholders involved to say, I did my part here. And you're right that that's very difficult to expect if you're only being brought in at the end. But if you're involved from the beginning, you are just as accountable as everyone else in that process. And that can only be good for the final product. Instead of having a moment of like blame the research or blame the researcher, why didn't you tell us this sooner? Well, it all happened in a vacuum. I'm only here at the last second. And it's And I know we we talk about a lot that it's really important to have that seat at the table and to push your way in almost as like an advocate for the consumer and to help that to make sure that that aligns of course with what could potentially be a

20:34 risky new investment for a business. Yeah, I think about the example in communications, right? And then you might be I testing a piece of copy against a normative database and it did a rank as great as you expected or it didn't meet corporate action standards. And then insights wasn't even part of the brief. It wasn't part of the briefing process with the agency. It didn't onboard the new agency on the consumer. And so the results are going to be clear once you include insights versus when you when you don't. I've seen both examples in and I don't think I've seen an example where you included insights from the beginning that has been inferior than when it doesn't. So, yeah, that's a challenge because sometimes we also multiple products and the insights team might not be as large

21:32 as your marketing or your agency teams you need to prioritize, but for those large growth and high risk projects, insights definitely needs to be part of the of the full process. Well, I'd like to talk a little bit about sort of the way that we communicate insights. I know from working with you a little bit just at AYTM in various roles that you've been in that storytelling is a big part of how you personally, you know, work with data and uh make insights land with decision makers. When you are translating maybe a complex or nuanced finding into something a non-technical audience can act on, what is the first kind of tool that you reach for? I'm curious, is there like a storytelling rule or instinct that you come back to a lot when you're trying to make quant work feel urgent or human to decision makers?

22:28 Yes, that that that's something that I learned earlier on my career. I think like being a junior analyst and you and again, back then there were like these large quantitative studies where you felt as a junior analyst that you wanted to tell the audience everything you knew, everything you had said, report on every data point. And I think a great unlock in my career was shifting from telling what I knew to shifting to tell them what they needed to do to make their business decisions. So, what's the business decision that they need to make?

23:07 What is my point of view on that decision based on the data? What are the data points that make it clear that the solution that that decision that they need to make it's A or B? And then putting that story together and that can definitely narrow down a million of data points from your survey on that 10,000 respondents to truly five data points that you laid out on an executive summary and the decision it's clear. And then everything else goes to the appendix and you're ready to answer and that's when you showcase what you do know.

23:49 Um but keeping the storytelling tight and crisp based on on on that the the business decision. Well, and I think that makes a ton of sense and I think something I'm hearing you say that I I think I I would like to pull the thread on a little bit more is your version of storytelling which is likely a lot of people's but I think it's so important to to say this again and to really like hear it is it doesn't end with you know this is what consumers are saying and this is how they're feeling. It ends in a recommendation.

24:25 And I think that sometimes insights can fall short there or or insights teams may not feel like they have permission to make recommendations but insights doesn't end with and that's the data, right? It ends with uh the implications are and thus the recommendation is. And that's a really different skill set than the rest of research. Yes, definitely. And I also think I mean that there's definitely what you're you're saying, right about feeling empowered to make recommendations and and also owning that the the same way that the job isn't done just when you report what the data's saying, but when you translate into the recommendation, the storytelling doesn't happen through a meeting or through a deck, right? There are also the those

25:22 numbers of tiny meetings of elevator pitch of a small talk where you're starting to influence your stakeholders even before you get the results, right? When you when you start aligning that cross-functional team and when you're starting planting seeds in their heads of what's the recommendation coming, especially if it's going to be controversial, then it lands more softly and and with more acceptance.

25:52 Well, this is the elephant in the room for the vast majority of conversations, but AI is very clearly changing how research is done, but it's also changing what sits with the human and requiring more critical thinking into that distinction, and that determines what successful research teams will will value. So, when you're thinking about the research skill set today and developing research talent right now for the next generation of professionals, what critical thinking skills do you think are the most important to protect?

26:29 What is going to continue to sit with the human being versus what can be optimized or outsourced to an AI system? Yeah, I think that there are there are two critical skills. One, it's going to be problem problem framing. Uh and the second one it's going to be judgement. So, AI is wonderful even for critical thinking. It can be a very powerful critical thinking tool, but just like any other tool, it's going to be just as good as the user of it. So, being able to ask the right questions to AI or delegating the right types of tasks, um, developing the right types of agents, designing the right types of workflows, all of this, uh,

27:27 are going to be, at least for the time being, are human decisions. So, human beings need to be empowered to to identify that and and empower the AI human ecosystem to work at its best. And this And And all of this is important for the inputs, right? You need that the problem framing, identifying the sources, training your agents, all of that is for for the inputs. And just like it's always been in research, garbage in, garbage out. So, you can have the best AI platform, um, but you definitely, uh, need that. But then, you need judgement fro- for the output, right? Like when I get an AI output, um, that's this task, um, a logic test, right? Like it is this sound? Is the rationale of the AI

28:26 logically sound? Um, of course, we fall talked about hallucination, and I think that's probably the most basic issue with with logic in an AI, just fabricating evidence that doesn't exist, right? But then, there are others that are subtle and and harder to identify, right? Like an AI can be giving you an answer that relies upon an an assumption that you haven't tested. And if you're not Chris at identifying AIs relying in this on this assumption, you won't be able to test it. Or AI might give you an answer using a password that means something for the AI that means different a different thing for your organization, right? And and in that mismatch of concepts some there there is a baggage of assumptions that care that come with that answer that you need to to identify to pressure test. They might not be

29:25 wrong, but you as a human need to have the judgment to to see them, call them out, and be transparent or or pressure test them. I feel like there's something to, like if I'm going back to an earlier part of the conversation and kind of marrying it up with this part. When I think of how a lot of um in in this on the supplier side of the industry, how we're using AI on behalf of our clients is really to uh you know, allow them to you know, input their their business issues, right? Or their research questions to your point, the point that has to be human owned, and then running the experiment, churning out the analysis, and then putting it in front of the human for them to use their discernment to say, "Does this, you know, I I reviewed everything it did. Do I Am I discerning that this is the decision that I would make or that this is an important insight?"

30:20 Um but it also takes me back to earlier when you were describing like what you do to really understand the say do gap. There's a lot of analysis in that, right? There's a lot of analysis that you're doing and I think in a lot of our kind of automated experiments, we're not necessarily doing that kind of analysis, right? We are doing a very straightforward like, you know, we're looking at appeal and like in a concept test, we're looking at whatever the the key performance indicators are against a benchmark and then we're making a judgment about that. But it it it strikes me that something could easily be lost in this process, but it is also something that AI is remarkably good at, which is pattern detection. Back to your earlier point. So it really it's just reminding me or or or making me realize that there's this open area where I really think AI, at

31:18 least on the supplier side, could be doing more because I have a feeling that on the customer side where you have access to all of this data, you're probably doing it more than we are. Definitely, definitely. And I honestly like I think we're just we're just testing the waters right now with AI because the power it can have on critical thinking, on creativity, on calling out human biases, I think it it it can be truly infinite or untapped, nothing that we've done up until now.

31:53 And AI, it's a it's a great critical thinking, it's a great problem reframer, it's great with judgment, right? So it can be a great companion, a great colleague with whom you collaborate to to get to a solid answer, not only faster and cheaper, but but probably even a deeper type of answer. So yes, AI can be a great tool, as you said, in pattern identification, in in pattern breaking, right? What are those outliers that are breaking, what are those contradictions that are breaking the pattern. So it can it can really accelerate your capacity of thinking, um but that doesn't mean that you can delegate them to them. Yes, AI can be great at them, but you still need to own them because again, uh the power of AI it's going to be just as as

32:51 great as a human can empower it to be. And and you really need to think about the power of that human AI ecosystem as a whole. You will empower the AI, the AI empower the the AI will empower you, and it can become a virtuous or vicious cycle, depending on on how you use it. Totally. Absolutely. And kind of pulling on that thread a little bit, you know, I universities, workplaces, we're we're all trying to figure out how to use AI without letting it flatten judgment, to your point, right? The week that cannot be the outcome.

33:29 In your view, do you have a sort of a a way that you think about younger researchers, so people entering the field, um about using AI without outsourcing the part of the job that actually helps them grow their ability to to be discerning and to make judgments that are accurate. Yeah, I mean, I think that a great part of education from now on it's truly going to be um critical thinking. And by that, I mean just just philosophy, logic, the Socratic method, right? Because students are going to have the world of knowledge at their fingertips. They just need to discern, right? And identify when a piece of information is valuable versus not. So, going back to those Socratic basics, I think it's going to be very very important on one hand, and then on the other hand, once that they they come

34:30 into organizations, I think we middle management, upper management needs to own that development, right? And that happens when you put people on the spot, right? Like you guys could send me a discussion guide for today and I could have replied with the with AI, but it's when you ask the follow up. So when you ask for an example and when we get into the conversation that you can see, okay, this is true human on the spot thinking.

35:02 So I think that that preparing junior talent to present to the boards, to to answer questions of the spot, they will be able to continue sharpening their critical thinking needed for what they are managing the AI backstage, right? So so I think yeah, it's it's up to the upper and middle managements to to develop that junior talent. So so they don't lose those human skills that will continue to be needed when managing AI. And AI is changing so much, not necessarily about the relationships that people have to research, which it of course is and the way that researchers conduct research, but also the research process as a whole and how research interacts with different parts of the different stakeholders of a business. And so we're seeing now that instead of insights

36:01 teams, there's insights ops that are surveying more of a function that is creating a cyclical insights process that's happening at increasing speeds. How have you seen AI changing the process, creating this new way of thinking in practice? Yes, definitely I think that even when I I again, if I go back to the beginning of my career in insights, research used to be very linear, right? There is this project with this question, this budget, this timeline. We go from design, development, execution, storytelling, and we hand it off, right? And what we're seeing with big data, with agile research, and now with AI is becoming less linear and more cyclical, more iterative, right? And having more data, more patterns. So, those

37:01 those processes are going to be continuous, right? And and data will be democratized even further. And I think the the role of insights is going to be, well, twofold. One, to to feed that cycle continuously with new, updated data. And two, to be the interpreter of that and to provide cross-functional teams with the frameworks for for the organizations to make decisions. So, um that that's how I see I see their thing they're changing.

37:38 And I think that's how they're going to change in the next few years. We see organizations shifting budgets, developing their own AI capabilities, and so forth. We'll be seeing more of that, but I think that maybe in the next three to five years, we are Who knows? Maybe even sooner, but I I do think uh we're going to see a wave of qualitative renaissance because AI's going to be so ubiquitous.

38:11 Everybody is going to be leveraging so much data that it's public at such a great speed that only the brands that decide to invest in deep qualitative data that feeds those models are going to be the ones that distinguish themselves. So, I think it's going to be interesting because all the topic of of these conversation was leveraging quantitative to reveal um consumers unconscious in a way that they cannot even articulate, but I think the opposite will happen too. We're going to be leveraging deep high-quality qual to feed the quantitative, the LLMs, the agents with data that is super relevant.

38:57 I feel like we need that on t-shirts, the qualitative renaissance, because I think that that's so important not just in the AI conversation, but in general, too, because you can have massive amounts of quantitative data, and to your point, it's going to become all very public, all very usable very quickly. It's not going to be as big of a differentiator technically as it is now, but it's going to take that contextualization not as just a deeper option, but mandatory in the process. Yes, 100%.

39:31 Right. And I think, you know, 5 years ago, we would have been like, but how? We don't have that much quant- qualitative data. There's not, you know, qualitative is very expensive. But with Qualtrics Scale, it really just changed the game of what you can do. And especially with AI to be able to process all of that data. Yes, 100%. I think that that's going to be if we do go through that renaissance, that's going to be part of of the process. How do we decrease decrease the friction? Because up until now, we've always thought qual means a slow, expensive, ad hoc, and custom, right? And maybe new qualitative methodologies will come in where they unlock that friction, they lower that that friction. Uh maybe there going to be off-the-shelf reports um for the industry where brands can customize with a follow-up, right? Uh or as you said, uh leveraging AI to have hundreds or

40:32 thousands of deep conversation and and and reach data um reach speech, you know, then you can leverage AI to quantify and to process, right? So, so yeah, I think I think there's been uh a lot of innovation later on in that area. Well, thank you so much, Dev. It's been a super enlightening and interesting conversation so far. There's a lot of topics that I feel like we've talked about on the show, but a completely new lens for a lot of them. So, thank you again so much for for taking the time uh to chat with us today. I want to switch gears a little bit and take us into our reoccurring segment that we have here on the show called Current 101, where we will ask all of our guests the same question, which is in the insights industry, what is something that you would like to see stop, and what is something that you would like to see more of?

41:31 In more of I think it's it's related to to this uh qualitative piece. Um I can imagine that having off-the-shelf deep qual that you can further customize or things like longitudinal qualitative, like seeing new ways of of qualitative that can enrich the insights. Um something that I would like like to that I would like stop seeing, that's a harder one. Um I mean, I think that there is not that much need anymore for for these uh quantitative foundational studies that you just feel every 2 to 3 years. I I rather have the shorter iterative and more continuous studies.

42:26 I like that. Yeah, and I think especially where we are right now, uh yeah, 2 years is far too long between like we're in a stage of rapid innovation. It's changing so many things. So, I think that those are exactly the kinds of studies that get us in trouble when they're too far apart. Yeah, and that uh that was actually my first job in research was actually managing a giant ongoing brand tracker. And I think about contextualizing some of those really niche things that I did back in the day and I like I wouldn't do three quarters of this stuff this these days. And that's only been a you know, a handful of years. Well, I say that's actually been probably closer to 10, so maybe yeah, but that's something that's super interesting about you know, the new and iterative process, which I think you know, the industry is still trying to get the hang of and AI is making it easier.

43:22 100% Yes, now our jobs have changed a lot and I can't wait to see what we will be discussing in 10 years, right? Well, then for somebody who's listening who wants to build a meaningful career in insights and stay relevant in this new world that is shaped by AI speed, constant pressure on resources, is there a piece of advice that you would give them to just kind of stay grounded through this experience in this time?

43:53 Yes, I I would say to continue building the skills that are going to be critical for humans and they're not going to be unique for humans. AI will develop them, too. But, they're definitely going to be needed and and in an even more degree that it's been in the past. And I think those are, as we've been saying, a critical thinking.

44:23 Uh and I would say the second one is empathy, all right? Understanding your audience, understanding how decisions get made in your organization. Um understanding how to sell a controversial point of view. Those are the things that will continue to be to be human. And and AI will develop its empathy, as well. We see how ChatGPT asks you, "Do you want me to now look up for a restaurants in your area?" You're like, "Oh, you already read my mind."

44:56 So, AI will be uh empathetic even more and more as it grows. Um but we're definitely continue needed humans in the organization who can handle both critical thinking and and empathy. So, keep focusing on on growing those and definitely integrate AI management, but don't don't forget about the basics. Yeah, those soft skills are becoming even more important.

45:25 The human aspect is essential versus just the hard skills these days. Well, Deb, thank you again so much for joining us. This conversation will definitely stick with me, whether it's your approach to the say-do gap or the qualitative renaissance. You've had a lot of amazing nuggets to share with our audience through this. Um and uh I I think that this has been a a great conversation. I I agree. I I loved that we sort of talked about this the te- or I raised it as this tension between like depth and speed, and you were like, "Absolutely not. These are not at tension with each other. In fact, here's how Agile actually gets you deeper research. And I'll be thinking about that a lot. I loved that answer. So.

46:13 And it's it's also a reminder that great insights don't just happen. They're very intentionally executed. They come from intentional thinking, asking those really better questions, getting more precise with your questions. You'd said going going very deep instead of very wide at a surface level, and connecting the dots in a way and delivering insights that drive decisions. For sure. Deb, thank you so much for sharing how you approach that balance and for giving us window and a window into what it looks like when insights truly are shaping business outcomes.

46:48 And to everybody listening today, thank you so much for being part of the Curiosity Current. We'll see you next time. The Curiosity Current is brought to you by AYTM. To find out how AYTM helps brands connect with consumers and bring insights to life, visit aytm.com. And to make sure you never miss an episode, subscribe to the Curiosity Current on Apple, Spotify, YouTube, or wherever you get your podcasts. Thanks for joining us and we'll see you next time.

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