#833: Qualtrics' Ali Henriques on accelerating the speed to insights with synthetic research

Summary

The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX

Episode #833: Qualtrics' Ali Henriques on Accelerating the Speed to Insights with Synthetic Research
Date: March 25, 2026
Guest: Ali Enriquez, Executive Director of Market Research, Qualtrics
Host: Greg Kihlström

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Episode Overview

This episode explores the transformation of market research through AI and synthetic data, focusing on how organizations can overcome bottlenecks in traditional research cycles. Greg Kihlström speaks with Ali Enriquez from Qualtrics at the X4 conference in Seattle, discussing the practical applications, advantages, and future of synthetic research, including real-world client stories and implications for marketing and insights teams.

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Key Discussion Points & Insights

Introduction to the Changing Landscape of Market Research

• Traditional market research is slow and often creates a bottleneck for marketing organizations wanting to act quickly.
• Synthetic data and AI offer near-instant insights, allowing brands to simulate customer behavior and reduce decision risk before launching campaigns.
  • Greg (00:48): “What if the biggest risk to your next global campaign isn't the market, but the months you'll spend waiting for research to tell you what the market wants?”

Ali Enriquez’s Background & Role at Qualtrics

• Ali has been with Qualtrics for eight years—starting in client support for research programs, shifting toward market research strategy and product guidance.
• Directly involved in the creation of synthetic-focused solutions to address the "speed to insight" issue.
  • Ali (01:45): “A lot of features and functions are designed to specifically solve the speed to insight challenge that you mentioned at the onset.”

The Role of Qualtrics Edge & Synthetic Research

• Qualtrics Edge was launched to showcase innovation, especially around AI-powered tools for market research.
• Edge now labels and organizes new, AI-driven research solutions like “Synthetic”, “Edge Audiences”, and “Edge Instant Insights.”
  • Ali (02:43): “Edge started with a couple of different solutions to help connect researchers as well as... product and marketing teams to data sources and insights and intelligence that are always on.”

Bottlenecks in Traditional Research

• Main friction comes from research being time-consuming, leading to decisions made by “gut feel” or guesswork due to the slow lead time of traditional research.
• AI-powered solutions don't replace but complement rigorous human research, supporting both “quick turn” and “strategic” projects.
  • Ali (04:31): “The cost of doing nothing... these solutions should be considered more for that than even trying to compete or replace traditional ways of doing things.”

What Is Synthetic Research?

• Traditional research involves weeks of design, data collection, and analysis; Qualtrics’ synthetic approach reduces data collection to minutes.

  • Ali (06:57): “We really solved for this challenge of speed to insight first by reducing that data collection from weeks down to minutes... 15 minutes.”

• Synthetic responses: AI models trained on rich Qualtrics survey data simulate how target audiences might answer, both in quantitative (surveys) and qualitative (natural language) formats.

• Model now trusted for qualitative insights due to significant accuracy improvements:

  • Ali (09:26): “Now that we... have the model commercially available, it is meeting all of our accuracy measures and expectations. I trust the qual.”

How Synthetic Differs from General LLMs (Large Language Models)

• Synthetic research AI is grounded in specific, proprietary survey data vs. generic LLMs that may lack behavioral nuance and variability.
• Internal studies show LLMs cluster responses, lacking the distribution and variability of human or synthetic survey-trained responses.
  • Ali (09:50): “95% of what our model's considering is our survey data... we were intentional with showing the variability of the data because humans are quite irrational... Our model... almost perfectly matched that of the human response.”

Real-World Example: Booking.com Case Study

• Segmentation study using synthetic research, including psychographic segments—something Booking.com had never performed before.

• Enabled deep dive into behaviors and preferences (e.g., hobbies), scale of survey impossible with human respondents.

  • Ali (13:13): “You can't do that with humans. With segmentation, you want to explore so many different territories... Synthetic takes the time to go through those and select all of the hobbies that... that respondent engages in.”

• Impact: Booking.com is piloting YouTube Reels campaigns based on synthetic segmentation insights.

Expanding the Role and Value of Synthetic Research

• Synthetic unlocks ability to test ideas quickly, supplement incomplete human research, and iterate on curiosities that would otherwise remain unexplored.
• Encourages rethink of traditional constraints in survey design and data collection.
  • Ali (16:48): “We have to start to shed ourselves of traditional research constructs... go and run those three to five questions with synthetic and... it’s better than nothing, right?”

Measuring ROI of Synthetic Research

• Speed and cost are key—but the greatest value is in increased throughput and ability to address a wider range of questions, enabling better strategic decision-making.
• Researchers’ roles can shift from data-crunching to strategic advising.
  • Ali (18:24): “It is more throughput, it is more output. Research is constrained... What happens when researchers are more efficient in their day to day? What else can they do and where do they invest that time?”

Future Impact on Marketing and Research Teams

• Moves insights professionals toward strategic, actionable guidance rather than time-consuming data preparation.
• Increase in “bridging the gap between understanding and acting.”
  • Ali (20:11): “I should be really driving strategy and bridging the gap... As a research population... how do we accelerate then the decisioning that needs to happen on the back of this?”

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Notable Quotes & Memorable Moments

• On decision-making bottlenecks

  • “The cost of doing nothing... there’s incredible opportunity there because it’s just not really been cared for because research is a bottleneck.” — Ali (04:31)

• On speed of synthetic research

  • “I ran synthetic for Jeff’s keynote this morning. 15 minutes.” — Ali (07:46)

• On reliability of synthetic models

  • “Now that we... have the model commercially available, it is meeting all of our accuracy measures and expectations. I trust the qual.” — Ali (09:26)

• On segmentation with Booking.com

  • “I found myself in one of the segments... Like, I’m a human connecting with synthetically generated segments. That’s just nuts.” — Ali (13:13)

• On researcher evolution

  • “PhDs sitting here... building charts in PowerPoint. That is kind of ridiculous. They should be driving strategy and product investment decisions, not producing charts.” — Ali (19:14)

• On future direction

  • “I think we’ll become a bit more strategic advisors... stitching together all of these different data sources, building kind of a fabric of intelligence.” — Ali (21:01)

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Important Segment Timestamps

• 00:48 — Framing the episode: Research cycle bottlenecks and the promise of synthetic data.
• 01:45 — Ali’s intro, background, and the origin of Qualtrics’ synthetic research focus.
• 02:43 — Explanation of Qualtrics Edge and AI innovation in research.
• 04:31 — The real cost and pain points of slow research cycles.
• 06:57 — Walkthrough of traditional vs. synthetic research timelines.
• 09:50 — Deep dive into how synthetic models outperform out-of-box LLMs.
• 13:13 — Booking.com case study: Psychographic segmentation with synthetic data.
• 16:48 — How synthetic augments human research and closes gaps in real-time.
• 18:24 — Metrics for ROI: Speed, throughput, and shifting researcher roles.
• 20:11 — The future: Strategic, action-focused insights teams.

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Episode Wrap-up: Reflection & Staying Agile

• Ali cites the value of connecting with peers and learning new perspectives at X4 as a key to staying agile and innovative in her role.
  • Ali (23:05): “More connection with buyers and insights professionals just to learn from them.”
• Closing thoughts center on continuous learning, connection, and embracing new tools to accelerate impact in brand and research roles.

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For those interested in the future of research, marketing, and AI, this episode provides practical insights, forward-thinking strategy, and real-world examples from a leader at the forefront of synthetic data and CX innovation.

Transcript

[Ali Enriquez]

The agile brand.

[Greg Kilstrom]

Welcome to season eight of the Agile Brand podcast. This season we're going all in on Expert Mode, MarTech, AI and Customer Experience, talking with the people and platforms behind the brands you know and love. I'm Greg Kilstrom, your host and I help Fortune 1000 companies make sense of martech, AI and marketing ops. Hit subscribe or follow to make sure you always get the latest episodes and leave us a rating so others can find us as well. And make sure you check out our sponsor, Tech Systems, an industry leader in full stack technology services, talent services and real world applications. For more information, go to teksystems.com now let's dive in.

What if the biggest risk to your next global campaign isn't the market, but the months you'll spend waiting for research to tell you what the market wants? We're here in seattle at qualtrics x4 and today we're going to talk about how to overcome the speed to insights bottleneck, moving away from slow traditional market research cycles towards a world where synthetic data and AI can give us near instantaneous insights, allowing us to simulate customer behavior and de risk major decisions before they even launch. To help me discuss this topic, I'd like to welcome back to the show Ali Enriquez, Executive Director of Market Research at Qualtrics. Ali, welcome back to the show.

[Ali Enriquez]

Thank you. Great to see you.

[Greg Kilstrom]

Yeah, yeah. Was just saying I think this is episode number three for you, so welcome back.

[Ali Enriquez]

Yeah, I was in Mexico City for one. Yeah. We've had a whole host of global interactions.

[Unidentified Participant]

Yeah, yeah.

[Greg Kilstrom]

Love it, love it. So before we dive in though, why don't you give a little background on yourself and your role at Qualtrics?

[Ali Enriquez]

Yeah, happy to. This is year number eight for me at Qualtrics, which is fantastic. I've been a Qualtrics user my whole life, so always in the market research capacity and rewinding eight years. I started in a capacity to support clients in designing and executing on their research programs pretty traditionally. Right. And fast forward for the past two to three years. Right. I think as long as we've been, we've been chatting, I've taken on strategy for our market research kind of division. So very, very close partnership with product taking kind of the market signal and turning that into recommendation on where we should make product investments. And so this is how synthetic was born. And a lot of features and functions are designed to specifically solve the speed to insight challenge that you mentioned at the onset.

[Unidentified Participant]

Yeah, yeah.

[Greg Kilstrom]

Well, and maybe a little bit more. I know you and I have talked about Edge, you know, Qualtrics Edge a little before in the show. But for those less familiar, why don't you just give a high level?

[Ali Enriquez]

Yeah, absolutely. And so we actually announced edge at x4 three years ago and it was really meant to be disruptive and really signal to the attendees and of course the market that we are taking AI very seriously. And this is how we will incubate and really label our innovation for market research in particular, because each of our different product divisions have different approaches to their own incubation. And so Edge started with a couple of different solutions to help connect researchers as well as the kind of product and marketing teams to data sources and insights and intelligence that are always on. That's really the ambition and the idea because speed to insight is critical. And so Edge right now now describes, it's used a skew, right, that we effectively reference for certain AI powered solutions. So synthetic is Edge audiences and we have Edge instant insights and we'll keep going with that as a way to again, label and kind of delineate what are some of our newer, more innovative solutions for the market. So, happy to speak more about synthetic if that'd be helpful, but I'm sure we'll get into it in different ways.

[Greg Kilstrom]

Yeah, yeah, definitely. We'll dive in here and why don't we start with looking at research from that strategic level and starting with diagnosis. So traditional research, as many people listening to this and we've been involved with for years, great in so many ways. And yet it often presents a bottleneck to taking action. Where do you see this friction causing the most damage in a large marketing organization? Needs to move quick and all the pressure and everything like that. Where are some of those friction points?

[Ali Enriquez]

You know what immediately popped to mind, Greg, is actually the cost of doing nothing. Right. And I really do think that these solutions should be considered more for that than even trying to compete or replace traditional ways of doing things. Think about all the decisions that are made based on gut or, you know, that meeting with that team and we just feel like this is the right thing to do. To me, there's incredible opportunity there because it's just not really been cared for because research is a bottleneck, right? The teams are small and nimble and doing their best to service their stakeholders. Now the scenario we all want to avoid as researchers is, well, we had to make a decision. So we chose to put this feature and we chose to put this on the box and it's already in production. Even though you've just gotten back to Me six weeks later with the research. It's just, it's a horrible, it's a horrible position for us all to be in. Right. The researcher, the marketing team, the product team. But it happens because research is, has been conducted the same way for decades and unfortunately, each of the steps of the workflow and life cycle take time. And we have to accept that these AI powered solutions are not at all degrading the rigor and. Right. And the science of, of how we've done things. And so we've really started to separate the types of projects and the times that the researcher gets involved in marketing product ops type of research into strategic and quick turn. Right. And starting to convince our stakeholders that it's okay for X, Y and Z use cases to turn this way and be very AI LED and AI powered. And there are still times when the, maybe it's the weight of the decision warrants a more involved timeline. Human powered research. Right. Multi phase, multimodal type of investment.

[Unidentified Participant]

Yeah.

[Greg Kilstrom]

Well, and maybe for those a little less familiar with synthetic data, synthetic research in general, can you break it down in practical terms? How are we moving from those traditional research methods that most are probably familiar with to generating AI results? Like what does that look like in practice?

[Ali Enriquez]

Yeah, we chose to build, solve for that first, which is think about a research typical workflow and life cycle. Roughly two weeks we spend in the design and survey instrumentation phase could be two to four weeks in the data collection phase and another two to three to four weeks, you know, in the analysis and reporting phase. So as you know, the team that I started on eight years ago was called Research Services. And we existed to connect our clients to third party panel audiences that they don't have in their database. And so it was a natural place for us to really solve for this challenge of speed to insight first by reducing that data collection from weeks down to minutes. I ran synthetic for Jeff's keynote this morning. 15 minutes.

[Unidentified Participant]

Oh wow.

[Ali Enriquez]

Study design. I wrote the questions randomized. These are facts. I can show receipts. We have timestamps on the responses. And so it's just, it's wild. Right. So very intentional about using synthetics. Synthetic can mean so many things. And you know, we've talked about this for years now. I will speak only to synthetic responses. And so our model is trained and tuned to survey data. And as what we have, and that is what, you know, we've built our existence as kind of this Mr. Pillar of Qualtrics helping clients with. And so this, the survey data has something to do with advancing A product or a service, it's pricing its features, it's introducing a new concept, something to do with competition. Why are my customers going to that brand for, you know, that purpose? And how do I get them back? Or an audience? Right. Can we learn more about millennial moms and their shopping behaviors? And that's what's feeding our model. And those are the types of consumer attitudes and behaviors that we can very well represent with it right now it's quantitative, right? Because that's the nature of our product. But what we're demoing downstairs is qualitative. It's that same model, but now I'm interacting with it in natural language. I'm having a conversation with that Millennial mom instead of forcing my questions into a survey instrument to get record level data back. And so as the researcher, I wouldn't have trusted qual from any model last year, two years ago. Right. But now that we, we've got the model commercially available, it is meeting all of our accuracy measures and expectations. I trust the qual. I know that I'm having a conversation with something that's really rooted in robust, objective, quantitative data behind it.

[Unidentified Participant]

Yeah.

[Greg Kilstrom]

And maybe just for context there, because technically not to pick on any of the LLMs, but I mean, you could ask ChatGPT a question, right? Pretend you're this, that, whatever. How does this, you know, how should people think of this differently?

[Ali Enriquez]

Such a great question. It's true. Right. And I use Claude, I've got ChatGPT, we've got Gem, we're a Gemini shop. Right. We're not using it for this purpose though. Right. And I've heard clients this week, you know, they took all of our like past 10 research projects, threw it into Copilot, and that's what the product team is using. I'm like, oh my God, that's great. Nothing could go wrong here. So what? Rewind. The models have gotten very good though. They're out of the box. Right. And we, there is a very thin layer of our Edge audience's product that is publicly available. LLM, Llama, Claude, OpenAI. But 95% of what our model's considering is our survey data. And so we actually ran an experiment. I'm sure you've seen this with. There was actually a Google study that was published, academic research. Right. We replicated it in partnership with them. It was about Google search. There were eight KPIs we tested out of the box. Gemini, ChatGPT against, we ran new human responses and of course Edge audience. And the distributions are fascinating. So what you see with the out of the box models, it answered, right? It gave us, I can't remember, 500 responses each. What you see is we were intentional with showing the variability of the data because humans are quite irrational, right. And have very different perspectives and opinions. And you see that in the human data. So you've got a score, mean score, top two box score. But then you see the spread, right, the distribution of the data. We don't see that with out of the box models. Those models tend to be a lot more similar in pattern and behavior. And so you don't see the variability. You see them cluster around. I either love it, hate it, I'm agreeing. And they moved together. So ChatGPT and Gemini tended to move across these eight different KPIs together, whereas our model, again, that's been studying kind of human behavior and how we react and respond to survey questions almost perfectly matched that of the human response.

[Unidentified Participant]

Yeah, yeah.

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[Greg Kilstrom]

So you did a presentation with booking.com. i'd love to talk a little bit about that and maybe walk us through that before and after and that example because I think it's a great way to kind of put this stuff in context.

[Ali Enriquez]

Yeah, I love partnering with Alina. She's been such a. A curious researcher on this journey. We presented together one year ago. Both times it's been on her birthday, so what a delightful human to spend her birthday with us. I did bring a cake on stage and made everybody sing to her today. Sorry, on Tuesday. So our journey's been interesting. We approached our experiment last year. The model was not at peak performance. Right. And so it was one of these eyes wide open. We are going to take some questions from your brand tracker. You've already collected human responses to them. Let's collect some synthetic responses. And for some of those, she even had operational data. So it was a very different type of experiment. But, you know, it was crazy. Her learnings maintained. Right. That human. I know this sounds obvious, but. Right. The human is still very, very important in terms of structuring the questions and making sure that we are applying our expertise to even how we interpret the data. And so she had a lot of this things. She actually used the same slide as kind of, you know, the three things that she learned from this. But we chose this time to do a psychographic segmentation, which is wild. I had never done one of those. And so I was super excited and I found myself in one of the segments. Right. Which is just wild. Like I'm a human connecting with synthetically generated segments. That's just nuts. Like, that one was so me and that one was so her. So it was really fun to show the also again, like the variability of the data. And this one was an always on trendsetter and that one was an independent traditionalist. And you see all the same behaviors that you'd expect and you see from human based segmentation. But we threw a probably 30 minute questionnaire at these synthetic respondents. You can't do that with humans. With segmentation, you want to explore so many different territories. Her hobbies and interests question would have been rejected by Ali if we were running this with human responses. It was like 75 things on this list. But synthetic takes the time to go through those and select all of the hobbies that, you know, that that respondent engages in. And so it was, it was fascinating and it was kind of a reveal to the crowd. She walked through the segmentation as just kind of a readout and then said, and all of this was synthetically generated. Right. And I talked a bit about the model and how it worked. And so it was. It was really, really cool. They'd never run psychographic segmentation at booking before. They've got more, I think, more behaviorally rooted segmentation. So this was a great way for them to experiment internally with something they've never tried before. And they're now taking some of the learnings here about social media behaviors and they're gonna pilot some YouTube reels based on what we found with our synthetic segmentation. So very cool.

[Greg Kilstrom]

Yeah, that's amazing. Well, and I think it goes back a little bit to what you were saying earlier. You know, there are a lot of like gut decision ideas out there. Some of them we hear about the good ones, right? The ones that like, turned into something that's successful. But to me, part of this, the value of the human and us being creative and thinking strategically and all that, is that again, not all our ideas are going to be great in execution. But what if there was a way to quickly test those, run them and see and validate the ones that are? Because then you get less of these gut decisions that go nowhere, right?

[Ali Enriquez]

Yeah, yeah, that's exactly. And the other thing that I've been trying to encourage all of us qualtrics and clients this week to think about is we have to start to shut ourselves of traditional research constructs. And what I mean is this, you know, survey design and it being perfectly crafted. Because what happens with Agentic is we, we let go of all of that and we sure, it sits behind the scenes, right? Some type of structured way of organizing our questions and curiosities into research, but it becomes less important. And so I love synthetic too, for exactly what you said, but just to build on it, maybe we've just commissioned some really expensive human powered research and there were three to five things we wish we would have asked, right? Where we're in the room and our stakeholders are asking us and we're caught a little flat footed and like, well, I didn't ask about that, but. Well, I could go on.

[Greg Kilstrom]

I might have done that before.

[Ali Enriquez]

Yo, totally. Yeah, we've all been there, right? And it's like, you know, you're confined. You cannot possibly ask everything that you want. And so go and run those three to five questions with synthetic and you know, it's, you're not, you're probably not rooting the decision on them and that's okay, but God, it's better than nothing, right? And you can't go back to the humans that you just ran that research with.

[Unidentified Participant]

Yeah, yeah.

[Greg Kilstrom]

So how do we look at. I mean, I think we've touched on several things, but how do you look at the ROI of this is it. It's certainly speed, you know, and we've talked about that. But what, what are maybe some of the other metrics here?

[Ali Enriquez]

Yeah, you know, it's such a great question. We've been, you're very familiar with the Mr. Trends data. We've started to explore the value of market research traditionally. Right. So that we've got a baseline for what new value does this create. I think in simplest form it is more throughput, it is more output. Research is constrained, is time constrained, it is resource constrained. And I mean that in the human and the dollar sense. And so what does this allow us to do? Certainly a lot more endless curiosities, you know, addressed and answered. And so there's something to this kind of throughput that we haven't quite put our finger on. But you know, it's the, what we started this with, right? The all of the curiosities that, that go untested and decisions that are made, you know, based on gut. And so yes, there's obviously, you know, a cost savings too. The constrained traditional research costs more money, takes more time. We're able to do that at half to if not more than half, you know, the cost with synthetic. And what happens when our researchers more efficient in their day to day, right. What else can they do and where do they invest that time? And I think it's truly. I'll reference a client conversation I had just yesterday. PhDs sitting here, right, building charts in PowerPoint. That is kind of ridiculous. They should be driving strategy and product investment decisions, not producing charts and PowerPoint.

[Unidentified Participant]

Yeah, yeah.

[Greg Kilstrom]

So, you know, looking out a few years, what is this, you know, how does this change the structure of a marketing or, you know, or an insights

[Ali Enriquez]

team and oh, that is such a great question. I don't know what three years out looks like, but I've had this conversation. We're actually running some qual right now with research and insights professionals to understand how they are preparing their current like ICs and teams for these technological advances and what skills are they trying to develop. And so I think the closest I can answer when pressed by even my team, right, I was hired to do research. We're doing a little bit less of that. Right. Each year. What'll I be doing in three years? I think it's kind of what we were just talking about. I should be really driving strategy and bridging the gap. What Jason spoke about in the first keynote, bridging the gap between understanding and acting. And that's where research can often falls flat. Right. It's a lot of information about something. It's rarely telling you exactly what you need to go and do next, what button to click and what investment to make. And so, and we wish we had more time to spend on that. So I think we'll become a bit more strategic advisors that way. As a research population and as folks who have been around data and insights for, you know, forever, how do we accelerate then the decisioning that needs to happen on the back of this? And four, it's so tough, right? Each industry's so different. I could see so many different variants of that. I think that's the simplest way to think about elevating the role. And I mean that right away from the menial cross tabs and charts and all of that to all the things that we wish we had more time to spend on and the recommendations and also stitching together all of these different data sources. So building kind of a fabric, if you will, of intelligence that again, we're so constrained today and unable to do.

[Unidentified Participant]

Yeah, love it.

[Greg Kilstrom]

Well, Ali, always great to talk with you. A couple, couple last questions as we wrap up here. What's been a highlight of X4 for you so far?

[Ali Enriquez]

You know, I was not thrilled about it being in Seattle. I live a mile from the convention center in Saltway. But this venue has been really cool. It is beautiful. We've flipped a couple of different things to the agenda, but I think it's working really, really well. So I'm just, I'm loving the venue and I'm loving the kind of even, I'm sure it's attracted, you know, new attendees that just hadn't, hadn't come in the past. Right. And so it's just, yeah, it's been quite fun meeting new people in a new place.

[Unidentified Participant]

Yeah, love it.

[Greg Kilstrom]

And last question, I know I've asked you this before, but I'll ask it again. What do you do to stay agile in your role and how do you find a way to do it consistently?

[Ali Enriquez]

Yeah, okay. Gotta think of something new now, Greg. You know, I'll say. I'm not sure if I've said this before, but I really think I should have answered. I'm answering both questions in one. But I think part of what I love about X4 period is the opportunity to talk and connect with so many people who are, you know, somehow rooted in insights and intelligence. Right. And hearing all of their perspectives and, you know, what challenges they're facing and what cool new things they're experimenting with. Right. Helps me to kind of expand. I don't get to interact with clients too regularly. And so having it all kind of consolidated here in one place, I'd say more connection with buyers and insights professionals just to learn from them.

[Unidentified Participant]

Yeah, love it.

[Greg Kilstrom]

Well, again, I'd like to thank Ellie Enriquez, Executive Director of Market Research at Qualtrics for joining the show. You can learn more about ALI and Qualtrics by following the links in the show notes.

This episode is brought to you by Tech Systems. They're leaders in full stack, tech services, talent solutions and helping companies put it all in action. You can learn [e-mail] and thanks again for listening to the Agile Brand podcast. If you like the episode hit subscribe and drop a rating so others can find the show too. And if you're interested in consulting, advisory work or if you need a speaker for your next event, feel free to reach out. Just visit GregKillstrom.com that's G-R E G K-I H L S T R O M.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.

[Ali Enriquez]

The Agile Brand.