The human side of AI: Empathy, insights, and innovation with Idil Miriam Cakim

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Description

How can we balance the power of AI with the human touch?

In this episode of The Curiosity Current: A Market Research Podcast, hosts Stephanie Vance and Matt Mahan are joined by Idil Miriam Cakim, Founder and CEO of Iris Flex, a pioneering thought leadership research firm. Together, they explore how AI is reshaping the research landscape, discussing how curiosity, empathy, and strategic questioning are becoming essential skills in a world of accelerating automation.

From AI-powered storytelling and visual design to the complexities of hyper-personalization and workplace adoption, Idil shares her insights on how researchers and organizations can stay human-centered while innovating with AI. She also shares findings from her recent “AI Gap Study” and offers thoughtful perspectives on how AI is reshaping not just work—but life.

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Transcript

Idil Mirium Cakim:

I think you have to be the ultimate editor always. Say you're going to AI to smooth a little bit of what you wrote - that's one thing. But if you're expecting copying and pasting, I'm with the skeptics, but here's how to overcome it.

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Stephanie Vance:

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.

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Matt Mahan:

And I'm Matt Mahan. Join us as we explore the ever-shifting landscape of consumer behavior and what IT means for brands like yours.

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Stephanie:

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.

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Matt:

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.

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Stephanie:

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

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Matt:

So with that, let's jump right in.

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Stephanie:

Today, we are thrilled to welcome Idil Cakim, the founder and CEO of Iris Flex, a pioneering thought leadership research company. Idil is a true innovator in the realms of thought leadership research and online word of mouth marketing. Additionally, she's a prominent voice in exploring the implications of AI in the modern workplace. Today, we're going to bounce around a bit from insights to marketing, exploring some fascinating topics from the rise of AI-driven research methodologies to ethical considerations of AI-based research and the future of hyper-personalization. Idil, welcome to the show.

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Idil:

Thank you for having me. This is going to be fun.

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Stephanie:

It will be.

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Matt:

So let's jump right in. We always like to start these out with just learning a little bit about your background, where you came from. Obviously, you know, your career has spanned some pretty transformative periods in the market research industry. You spent some time at Nielsen working on media analytics. You spent some time on the corporate side in the broadcasting space. And now you have launched Iris Flex. Tell us a little bit about Iris Flex and what you do there.

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Idil:

Sure. It's a thought leadership focused research company powered by insights and analytics. We launched in October, 2024. And yeah, I am working with clients across the board, nonprofit, manufacturing, technology, definitely in the AI space. And I also just launched a study called the AI-Gap Study, looking at the space between the business and consumers of AI, trying to uncover the opportunities and also identify gaps. Yeah, in terms of background and how it all started, I think it all stems from a certain level of curiosity and bent for, uncovering facts and digging in. I remember even just playing recorder as a kid, pretending to be a journalist and mixing up family news, presenting them as facts. I was always interested in building stories, recording stories like this one, and reporting. And I grew up in Turkey and I came to the U.S., when as a college student so that journey I think that's one of the first transformations like just being aware of cross-cultural trends, what translates, what doesn't translate, what's different, what's similar. Your brain is constantly going back and forth. Someone who is multilingual, multicultural. I think that played a lot into me choosing to become a researcher.

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Matt:

How so? Like, do you kind of feel as though you have the responsibility to advocate for sensitivity to some degree? Do you think that's fair to say?

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Idil:

You naturally learn how to take the other's perspective. I've been in this country, the longest I've lived somewhere is Brooklyn, New York. So, but are you coming from a multicultural background, you kind of learn how to take the other perspective, which I think is so critical, whether you're looking at an AI-driven data set or manually collected data set or, some piece of research that another professional has written, you want to be able to navigate back and forth and think of the other's perspective and see what might be going on under the hood, why things are different, what's different and why. Those are questions that someone who navigates multiple cultures in a given moment has to address. So I think that just multilingualism, multicultural. Being multicultural, yes, it makes you sensitive and it makes you notice things. And that gets incorporated into your work.

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Matt:

That have to be a true champion of empathy it sounds like

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Idil:

Yeah, empathy is a side effect of it, yeah.

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Stephanie:

Love that. As a thought leadership researcher, you've obviously researched and written a lot about AI and the role of AI and kind of reshaping work in people's lives more generally. I'm curious, what do you think is kind of the biggest conception out there about AI in the insights industry? But if you want to take that question more broadly, just in enterprise work, feel free to do that. But what do people get wrong when they think about it and they hear the word and they are in that nascent stage of adopting it?

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Idil:

Right. I mean, we're seeing AI here, but there's so many different applications. There's so much going on on the back end. There's so much that could be happening on the front end with human input. So what kind of tool are we talking about? How is the tool being implemented? Is it using data that we came up with in our company or is it using a data set that we're bringing in? All of these things affect outcomes and usage. But if we take a step back and just look at how Americans are viewing AI and how they're viewing AI in their daily lives and in the workplace, we're using it in our daily lives. Every time Netflix offers you a new title, you're using AI. Every time your phone corrects your spelling, you're using AI. Your car is offering you options on the dashboard or is able to take in commands with voice, you're using AI. So there are many, many areas where your shelves in the grocery store are smart enough to know exactly what you need. That's all AI. But if you bring the lens over to the workplace, there's a lot of fear. Most people, six and 10 among employees say, I think it's going to create this new era. It's going to erase more jobs than create new jobs. We just don't know that. But that's the perception. And depending on the group you're part of, like women are much more likely to believe that than men. Men are more likely to believe, interestingly, that it will create more new jobs. They're a little bit leaning in harder to AI currently. But overall, there's this fear from, because you're also thinking about it from the perspective of a bunch of different roles, a bunch of different levels of people, different levels of familiarity with technology and just the art of asking questions. And I just think as researchers, we have a bit of an edge here because we know how to ask questions. You're trained. Your mind is trained in crafting questions that are not skewed, that aim to present things as facts, that aim to probe further and dig deeper and deeper. So a lot of people I find in everyday conversations or at presentations where we share these findings say, I just don't know how to start. How do I prompt? And I think maybe we change the word prompt or something and say, if you said, don't you know how to ask a question? A lot of people would feel a lot more comfortable. Depends on how you approach technology and what your threshold is for failure also. That's the murky sort of thing that's playing out here. But when thinking about AI, I think we're at a point where we're past the early adopters. Our research in AI-Gap shows that the bulk of people who want to embrace AI are actually middle managers. They're millennials who are looking to get ahead and who are aware that their job is changing, not maybe going away, but changing fundamentally. So I think we're also at the cusp of something really exciting.

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Stephanie:

I love that. And I think that's a really nice reframing, both in terms of like this. It doesn't have to be you. Like you said, you know how to do this thing. This is a superpower you already have. This is an area where you can leverage that superpower of asking questions. Gives people confidence. And I think a lot of times that's what it is, right? Is that lack of tech confidence that kind of holds you back. So, yeah. To switch gears a little bit, I was thinking about how, you know, 10, 15 years ago, it was all like, oh, self-serve, self-serve platforms, reshaping the industry. And I mean, they absolutely have, you know, Matt and I work at a self-serve platform, AYTM. And, you know, the ideas were allowing companies to bypass that traditional research cycle and get insights faster and faster. But I think we've all been doing this long enough to know that speed sometimes comes with a tradeoff of quality. Like it has to be executed well, right? There are lots of ways to prevent that from happening. But I'm finding that the cycle is rising again, right? Now with the rise of AI. And that, again, we're getting faster and faster even than we were before with just, you know, other kinds of automation. And that quality question comes to the top again. How do we ensure that self-serve AI tools are empowering businesses with reliable, high-quality insights rather than just kind of quick outputs and quick data points?

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Idil:

Right. I think it depends on where the data is coming from. So what's exciting about self-serve now is, like I said, we're beyond early stage. The early stage was, oh, look how fast it can help me crunch data. Or look how fast it can bring me data. Now, I think the question is, look how beautifully it harnesses multiple data sets, and some of which is data that's bespoke to the company. I think that's what's so exciting. And that's where it becomes super relevant and applicable to the end user. So imagine being in a company. Case scenario one, you're doing a survey and that survey data is present in the self-serve tool and you're able to ask everyday questions and just bring up insights. That's fantastic already. Let's take it a step further. You're able to bring in your past studies that may or may not be with the same methodology. That may or may not be even about the same topic, but you as a researcher think there might be some alignment or some crisscross between the findings. And the self-serve tool, if it's smart enough, will harness all of it together and will bring you some insights that will make you so much smarter about going to the next step. So there's always that human element, too. So I think it's empowering for whoever is guiding the research process. But self-serve doesn't mean a robot made it up and I took it. It always makes me think of, I'm at the counter and I have this cup that someone, some robot hand just delivered to me. It's not so, but it allows me to use the power of AI all the while using all the data sets and maybe even more data sets that were available to me. That's what's exciting.

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Matt:

That is exciting. I think we hear that from a lot of different perspectives. There's just a lot of hope that, you know, at least now, finally now is the time that we might have a tool that solves this like, ever-present sort of holy grail problem that the research industry has faced forever of bringing it all together. Like these disparate data sources, you know, past work that's been sitting on an electronic shelf for ages, gathering electronic dust. Like how do we, you know, make use of all of this investment that we've made and pull it into one cohesive source? That's something that I think really resonates. I wanted to go back to something you said. You mentioned your passion around storytelling. We often hear concerns from AI skeptics about, you know, is it really going to provide the depth? Is it going to give us the ability to pull meaningful insights? Are we going to be able to use these tools to actually make a compelling story? I'm curious how you see, you know, that changing. Do you have concerns as a passionate storyteller yourself? Do you have concerns that, you know, this storytelling element might be lost? Or is that the role that the human then still plays in this new future where AI is, you know, an ever-present partner in your research journey?

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Idil:

I think you have to be the ultimate editor always. Say you're going to AI to smooth a little bit of what you wrote. That's one thing. But if you're expecting a, you know, copying and pasting. So I'm with the skeptics. I'm with the concerned people. But here's how to overcome it. So imagine a scenario where you are completely relying on AI to draft the whole story, copying and pasting and walking into a meeting. Well, you must have been like, you had to do it, maybe. Maybe you were given just one minute, but then you have to overlay your expertise, your experience. Otherwise, it will show. I still think it will show. Because even the layperson's eyes are now catching what AI has produced versus what a real person has written. There are certain words, like the word just today, I was reading about it from an editor. Ecosystem. This is AI jargon. It's appearing over and over in press releases, he was saying. And I agree. I keep seeing it too. And when he said it, I was like, oh, is that where it's coming from? Everybody's talking about some ecosystem. So you want to avoid that. But I think more importantly, as researchers, we're at the table as experts. Subject matter experts in a given topic. I was in the media, in audience analysis. You're a subject matter expert in methodology. So you want to be able to hold on to that and overlay your expertise to what was quickly delivered to you. You're given a tremendous advantage. You're reducing time to delivery by maybe 80, 90 percent on huge volumes of insights. But at the end of the day, I think the voice has to be human if you're presenting it.

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Stephanie:

For sure. I'm curious if you've had as much experience on the, just going back to storytelling for a second. This is something I've been thinking about quite a bit in the past, honestly, just a few days but. Anytime that I am doing storytelling, I find that a lot of my personal time is spent telling that story visually. And that's not particularly, as a researcher, a skill set of mine, but it's a critical part for the audience a lot to bring that story to life through visualization. And there are so many tools right now. Like I'm thinking of things like Canva just did their big announcement with Magic Studio, where it's just like, we'll build the visual part of the story for you. And how much that frees you up to move to really focusing on the actual narrative and what the insights are. And then to be able to trust that that visual part of that story can be reflected and built for you by the AI, as long as you know what the story is.

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Idil:

As long as you know what the story is, I would definitely want to underscore that. You have to hold the voice of the story. Where it gets interesting is the generative aspect, right? Maybe it will deliver you something you hadn't thought about. And that's amazing. That's enhanced. But at the end, it's up to you to accept that and to weave that into your story or not. So ultimately, you have to be the subject matter expert.

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Stephanie:

For sure.

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Idil:

I don't want to come across as someone who's saying, well, the human voice is so important and then it will always have to be part of AI. We don't know the future iterative stages of it. Maybe we will take the offered voice. But what I want to underscore is that there's so much knowledge you have in your field when sitting in front of an AI tool that that has to speak and be an integral part of whatever it is that you present because that's how you're going to catch any inaccuracy. That's how you're going to pressure test anything that may not resonate with field experts. I think that's why you always need the human eye as an overlay on top of what AI brings you.

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Idil:

Makes a lot of sense.

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Matt:

That was interesting. I had never thought about that aspect of using GenAI for the visual element, kind of freeing you up to actually focus on the data. That's really cool stuff. This is all stuff that we'll just cut out in post. This will not make it into the episode. All right, cool. I'll jump into the next part then. So one of the other sort of evolutions that we see taking place in the industry right now is a lot of passion around deploying AI to enable more accessible, qualitative research approaches. So something, for example, that we're really passionate and working on here at AYTM is Conversational AI engines that allow research to be deployed with open-ended questions that the AI then takes a role of moderator in and is able to elicit an open-ended response, but then probe the respondent for follow-ups and really engage with them in a conversational way. That's something that, you know, we see as having the ability to solve one of the problems that some of the AI solutions have, which is just sort of that lack of depth. What other ways do you see the application of AI really needing to evolve to meet the very specific needs that researchers have when it comes to looking at their data and fielding their studies?

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Idil:

I think if it's, are you talking about conversation?

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Matt:

If that's something you see as impactful, certainly. But if there's anything else.

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Idil:

I think the conversational element, I just want to double click on that for a second, because I think that has a tremendous advantage for the researcher who knows how to guide the data, but can also bring it to the views of people who are not necessarily versed in research. Right now, I don't know if you're reading the same things, but the insights industry is self-assessing itself a little bit, saying, are we doing enough? Are we being impactful enough? Are we at the table and making the decisions that we need to be contributing to making, et cetera? And I think if you have a Conversational AI tool that harnesses the data that the researcher can guide and pull insights from, et cetera, but a strategist, a sales leader, an administrator can equally access the same breadth of insights. I think it's a huge win across the organization, strengthening the position of the researcher as well. I always thought that that was a phenomenal way of translating research across the organization, having the insights cascade. At that point, the researcher becomes the nerve center. And organizationally, you're stronger. So I think going from the fascination from the tool to an organizational application is also really interesting.

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Matt:

Yeah, it sounds like what you're alluding to is really sort of like this rise of the AI agent as a partner in your tool where, you know, eventually we're working towards a future where someone can simply query a platform, a simple question and be presented with an answer that draws upon, you know, all of the resources that are in that platform from secondary research to primary studies that may have been conducted. Or it may be able to suggest, you know, the creation of a new study and even develop that study all with, you know, the simple input of a question.

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Idil:

Right. You don't have to be a research expert, but to go back to the example that you were sharing, in qualitative research, to be able to dig in more and more and more and to rely on the AI agent to take the lead on that, that's also exciting. Maybe you're going to uncover things that you have never thought of or when probed twice without being tired, people are going to deliver more insights. That's also an exciting application.

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Stephanie:

I want to ask you about a third application, yet another application of Conversational AI, moving a little bit into the marketing side of things. You've written about the importance of identifying those key consumer moments, times when people are most receptive to brand messaging. And I'm curious, can Conversational AI effectively be leveraged in that context to kind of identify these moments and deliver a positive brand interaction when it matters most?

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Idil:

Okay, privacy concerns my mind immediately. But the question has always been, how can a brand authentically blend itself to a moment? And it's so difficult to do that. How can you be authentic all the while coming into that setting where maybe the consumer is having me time or maybe the consumer is? Enjoying a movie or spending family time? How can you authentically blend yourself into that moment? Knowing when to bring a brand into that moment, knowing how to serve an ad, will take some craft and knowledge. Conversational AI, I'm not sure how the application would go, but I can see and I can share this with you from data. People who are willing to have personalized experiences are more likely to give up some personal information and give you a little bit more insight as to when those personal key moments are. So that's a path that marketers working with AI can follow. Those people are a little bit more likely than average to be open to AI-based messaging, to be open to AI-based products, and find AI more reliable, more innovative, creative than average. So if you first find your hub among people who are a little bit more likely to seek personalization, then I think you have a pathway. And then imagine a world, I mean, I'm sure there are products like this where you're able to give feedback to the advertising and get what you need and maybe probe a little bit more on. Where you can find the product and where you can find your fit. And that is fascinating because right now, the majority of ads are talking at us, but imagine a world where you're interacting with the ad. Smart speakers offer that a little bit, right? With voice-driven ads where you can come back and say, okay, I want to buy it or tell me more about it. But I imagine a world where that is just hyper-personalized and a lot more common on different platforms. I think that's exciting.

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Stephanie:

I love where you started with that and where you ended with that answer because, I had a follow-up for you just to ask you, is this kind of hyper-personalization? Does it feel intrusive? And you started there and said, for a lot of people, there's going to be some privacy concerns, but it's about finding that cohort, right? Like you said, who is willing to make the trade-off because the personalization is more important to them.

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Idil:

And building on that personalization may be more important to you on different topics. On healthcare, maybe it's hyper important. You don't want to get a general diagnosis. I think you want to get pretty specific to what you're going through, what you're experiencing exactly. Perhaps ditto with education, what you're looking for. Maybe similar with your interest areas, but maybe if you're just buying a commodity product, you're not so willing to give up so much information. I think it's a back and forth imbalance thing in that people we see sometimes push back on this. But yeah, you have to find the people who are desiring that product, that service, so much that they will, and they see personalization as more of a benefit outweighing the risks.

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Matt:

I wanted to go back to something that we all already talked in and we already beat this dead horse. AI is not coming for your job. I think most of us who have been in the space for a while were of the mindset that, you know, it's going to work best when we work alongside it as a partner, when we learn to leverage it as the effective tool that it is. But that does mean that over the next one, two, five years, the skill set of the standard researcher persona is going to shift. It's going to have to shift. Curious to get your thoughts on what it's going to look like. Who is that ideal researcher? What is their personal tech stack and what skill sets are they going to bring to the table five years from now?

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Idil:

Some fundamentals won't change. You have to be curious. You have to bring some sort of knowledge. Don't nix away the importance of reading books and knowing frameworks and knowing some of the theory so you can identify what's important in the data set. I have a question mark on whether we do need to know how to code. At some point, we thought we did. So I don't know if that's going to be as necessary or if that's going to be on the back end and on the front end we're going to be able to peruse through. But I think your curiosity and your need to know multiple, perhaps AI-driven tools will be at the forefront. And I would say a willingness to learn, a willingness to cross-pollinate will always be fundamental. I mean if I were interviewing someone five years from today for my company and saying maybe someone coming out of college or mid-level, that's exactly what I would look for. I would look for that spark because you're always, you can't be so tool dependent on the technology either. But you have to find the people who are going to, who know a little bit but then are willing to take on what's next. So in that sense I don't think the human qualities of success are too different. We all have a need for upskilling right now and the speed is fast and that's why it feels a little scary.

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Matt:

Hate to frame it this way, but it's kind of like a survival skill set, right? It's like agility, open-mindedness, and resourcefulness. That kind of, you know, sums up what you were just hitting on. Like you really just need to be willing to take on the task of learning and changing and growing, which is not a small ask.

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Idil:

It's not a small ass. Not everybody feels very comfortable with it, but everybody eventually gets into it. I remember when, I'm giving away my age a little bit, when washing machines became automatic. And I remember my grandmother's fear as my mother was showing her how to navigate that, how we push the different buttons, etc. So that's not too different from someone who's maybe in an administrative role or, you know, guest-facing role in the hospitality sector who's wondering if they're going to be replaced or if they are going to have to learn a new gadget to survive. That's not too different. It's human.

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Stephanie:

It's human, yeah. To take you sort of, we're taking you up and down the organizational scale here, but I have a question, sort of given your background in both, you know, AI-driven research and then strategic leadership as well. You've really had a lot of leadership positions. And I'm curious, do you think companies are investing in the right areas, like from the top down when it comes to AI adoption? Where are they getting it right and where do you see them struggling to kind of integrate AI in a way that's effective?

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Idil:

I'm sure not every company is doing it right. I think overall, when you look at even global studies interviewing executives, you are seeing that there is a certain sense of urgency to show results and not everybody's able to find these results. And these are very broad strokes, right? What is the result? Is it that team XYZ got ahead by reducing time to delivery 10%? Is that success and we call it, you know, success and we go home? Or is it that the whole organization now has a new way of working? Three out of the five departments are more efficient. We created 10 new roles and completely changed the way we communicate to each other. Those are all different questions. So it really depends. What I do see where things go wrong is one being tool specific. I don't think personally, I don't think putting ChatGPT on desktops is enough and leaving it to people. I've seen really quantitatively oriented, really analytical people struggle with some of these more complex AI driven tools. So there's a lot to be said about curiosity, about culture, about giving people enough of a runway to absorb, adopt, and adapt, and then turn it around. A lot to be said about having patience with the tools. I think it needs to be now where we are, it needs to be a little bit more culture driven. And like Matt was just alluding to, not everybody's prone to change. Not everybody's open to change. So how do you make sure you don't create so much friction when one team is flying ahead and or a group of people who are already super achievers are embracing these new tools and the middle rankers are struggling. Well, among them, some of them are rising. And these are all very complex HR and culture questions. And I think that also, that is not maybe on the agenda of IT typically or the tech-related departments, but it needs to be on everybody's agenda and certainly on the C-suite agenda. So if you're not just bringing in a tool and leaving it to the tech savvy and hoping the rest will learn, you really have to think about how it's shifting and how it's being absorbed. And you have to think of it as a whole organism and a whole fabric of things.

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Matt:

It's a great call out. It reminds me of something else I wanted to go back to. You already kind of hit on data privacy concerns, you know, when a company's thinking about rolling tools out. Certainly, it's got to be at the top of the list of things to figure out and do well. And you just added some additional call outs, some washouts to this list of blind spots. What else is there, though, that you think the industry just isn't talking about enough? Are there other areas where there's just not enough attention being given to the implications, the ramifications of this transformation that we're living in?

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Idil:

It is a transformation. I think we're going to live and learn a little bit. And privacy, data privacy issues. So first, the barriers, the data privacy. Where is my information going? That's the number one barrier in people's minds. Number two is accuracy. So I'm not going to touch this thing because I don't know what it will do with the information I'm going to give it. Number two is the accuracy. I can't trust what I see. I don't know enough to assess whether what I'm getting is accurate or not. I'm hesitant to pass it along or to go buy it. Or worse, maybe it is inaccurate, maybe it is a hallucination. And I believe it and I proceed and I fail. So there are all these issues, technical issues that are serving us as barriers right now. But on the flip side, this is where I think we're not thinking enough. And I'm curious what you guys think as well. So everybody's focused on time savings as a KPI. It reduced delivery time to delivery by 10%. There's no accuracy check. There's some sources that are being listed, but then there's that. What do you do with the time that you shifted? You gain 10% of your time. Where did you put it? So in the software industry, McKinsey, I think, did an amazing report on this. They're seeing a barrage of work coming in faster and faster. So employees are being overwhelmed already with the new tasks and new projects that are coming on their plate because they just end up finishing faster. That's not a very comfortable position. That's not a growth position. I'm speaking in very general terms. But when you ask consumers, employees, what do you want to do with the time you gain? They'd rather spend it with their friends and family and their hobbies and with other media. Less than 20% want to do an extra gig or catch up on work or do another day of work. I had a question in my study about what would happen if your workday went from five to four days? Thanks to AI. Now we're in a wonderful spot where people can imagine the future actually like that. And people said, I just want to have fun. Like, I wouldn't do more work. I'm simplifying it, but that was the essence of it. So there might be other KPIs that we should be tracking, like employee satisfaction, like a wish to shift your time to something more productive. And what that productive means can be different depending on where you are in terms of your life stage, your age, and your interest areas. But imagine a world where all that time saving is going into more spend or leisure, more spend on media, media time shifting. That is fascinating. I think we have to think of the bright side like that a little bit.

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Matt:

That was the sociological promise of AI that we've all been sold, right? Is that it's going to take care of like the first four or five rungs of Maslow's hierarchy so that we can go right to the self-actualization stuff and do all of the things that we want to do. We can do the art while it keeps us alive. Yeah, that's a great point. Those are implications we should consider. I think that companies run the risk of really putting themselves into a bind if they do just focus on the commoditizable KPIs, like you say, of time savings, of cost savings, when there really is so much more to be had when you're deploying AI solutions that enable people to do research in ways that they've never been able to do it before at scales that they've never been able to achieve before. Perhaps even to reach audiences that they've never been able to reach before. There's a lot of upside there.

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Idil:

Hey, you just sparked a thought in my mind. How many times as a researcher have you, well, if money was no object, I would have done qual and quant. And I could give up a portion of it because it just didn't fit in the budget. Or how many times did you say, well, I cannot reach those people right now. They're very hard to find. Would be in your field forever to find that hard to reach audience. And it just is not permissible. So you sacrificed insight, reduced scope, and you delivered something that you could do at the moment. So maybe we're going to also push the boundaries of what we know and what we uncover.

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Stephanie:

I think it's those such, I'm so glad you called out this danger of like, I mean, danger in the sense of like, what a shame if like what it allows you to do is just, you know, like free up yourself from some tactical work to move on to some other tactical or reactive work. Because in my mind, the short-term promise is to take care of some of these more tactical, repetitive things to allow me to sit in a strategic place where I can really influence what's happening for my clients, right? And in my industry and for my company to drive it forward. And that is the promise for me right now, not the idea that like, oh, now these tactical things can be done faster. So do 10 times more of those tactical things, right?

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Idil:

And the agencies I worked at before were telling me that their CEO was telling the young staff, look, you don't have to stay in the entry-level position for two, three years anymore. You can move ahead in a year to the next level. If you learn these tools and shift the time to training. So that company is being really smart about that. They're shifting the time from creating lists and maybe doing some of the more rigorous work that you're okay with because, you're young and you're strong and you're just excited about your job and you're okay with that. But it's mind-nobbing, especially the college experience where you're used to digging deep and being intellectual. So imagine getting through that pretty quickly using tools, but spending the time on meaningful training. That's really great.

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Stephanie:

I love that. Yep. So Idil, to kind of close out, I wanted to go to a totally different topic and ask you a question about sort of your history. And you wrote this book and I think, was it 2010? Implementing Online Word of Mouth Marketing. And I was looking at that today and thinking, good Lord, how prescient of her, right? Like, because that was such a nascent period of like, I mean, certainly the internet had been around, but that was when Facebook was really getting really popular and Twitter. I don't know. Now we are in this totally different era of TikTok, right? Like it's just all of that has just fully exploded. And I wonder, is a lot of your thinking then, like, is it just applying more and more as time goes on? Or do those strategies kind of change in this TikTok, Instagram Stories time that we live in today?

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Idil:

You know, what's changed is how much more institutionalized it's become. I was at a conference, Brands and Culture, just recently, and there were a series of panels of folks who were steeped in Influencer Marketing and creator economy, which is a multi-billion dollar economy now. So you're right, it was perhaps the inception of it becoming a business. We were establishing industry associations, et cetera, back then, but now it is an economy unto itself. So what's also changed drastically, you know, TikTok is one example, but there are many other platforms like that, how just individualized media content consumption has become. Journalists are influencers, influencers are journalists, and that's also a tricky space. So it's far more complex, far richer, much more dominant and powerful in terms of impact on, say, sales or opinion shaping or... You know, shifting your entertainment time. So I see that as like, it's just the landscape has become more complex, more interwoven, and just more institutionalized. And I do see these creators as media mavens of their own right. And when that happens, that's great. They're powerful, but they also have a company structure under them. So you can engage them for product and service launches, or you can engage them, you know, to see if they would like to talk about a story you have. So it becomes a lot more structured. I think back then we were figuring out ways to do it seamlessly, to do it authentically. But now there's a lot, even though it's more complex, there are more pathways to do it right.

And I'm going to just double click on that and bring it to, I mean, we haven't talked about it, but podcasts, the growth of the podcast industry is reminding me a lot of that time inception of, you know, blogs becoming media entities unto themselves. So whenever you see a medium with a long tail, lots and lots of titles and lots and lots of participants, it reminds me of that time. So again, something to look forward to as it grows.

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Stephanie:

For sure. That's a great Idil.

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Matt:

Well, Idil, thank you again so much for your time today. I really enjoyed our conversation. Gave us a lot to think about and really great insights. I really appreciate you sharing all of your experiences with us today.

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Idil:

Thank you for having me.

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Stephanie:

Curiosity Current is brought to you by AYTM.

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Matt:

To find out how AYTM helps brands connect with consumers and bring insights to life, visit aytm.com.

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Stephanie:

And to make sure you never miss an episode, subscribe to The Curiosity Current in Apple, Spotify, or wherever you get your podcasts.