UX Research Must Be Fast and Strategic to Survive

In this episode of UX Leadership by Design, Mark Baldino talks with Ryan Glasgow, CEO and founder of Sprig, about the future of UX research in an AI-first world. Ryan shares how Sprig was built to replace legacy survey tools like Qualtrics and SurveyMonkey by enabling real-time, in-context feedback and powerful AI-driven analysis. The conversation dives deep into how modern research can scale with fewer resources, why AI should be seen as an intern—not a threat—and how researchers can thrive by shifting toward strategic influence within organizations. If you’re in product, design, or research leadership, this one’s for you.

Key Takeaways

• Legacy research tools are broken – They’re disconnected from user behavior and painfully slow—Sprig fixes that by embedding surveys in key workflows.
• AI isn’t here to replace you—it’s your intern – The most successful teams treat AI like an eager junior teammate that accelerates insights and frees up strategic thinking.
• UX research is evolving toward strategic impact – Tactical research is being democratized across teams; researchers who shift toward company-level strategy will thrive.
• Tool bloat is real—consolidation is the future – Many orgs are replacing 3–5 survey tools with Sprig to reduce costs and streamline workflows.
• You can’t scale great product experiences without scaling insights – Research embedded across the product journey is the only way to keep up.
• Designers and Product Managers are sharing research responsibilities – It’s now table stakes for cross-functional teams to gather, analyze, and act on feedback.
• Sprig uses Sprig – The team applies its own product to optimize A/B testing, feature development, and in-product recruiting—truly eating their own dog food.

Chapters

1. From Product to Founder: Why Build Sprig – 01:00
2. What Legacy Survey Tools Get Wrong – 04:00
3. Sprig’s End-to-End Research Workflow – 07:30
4. Using Sprig to Build Sprig (Meta UX) – 09:45
5. AI as Intern: Supercharging Strategic Work – 22:00
6. The New Research Stack: Strategic > Tactical – 29:00
7. The Future of UX Research Teams – 31:00

Resources & Links

• Connect with Ryan on LinkedIn
• Sprig AI-Native Survey App

• Connect with Mark on LinkedIn
• Fuzzy Math - B2B & Enterprise UX Design Consultancy

Transcript

Mark Baldino (00:02.572)
Hi folks, hello and welcome to UX Leadership by Design. I'm Mark Baldino, your host. I'm also a co-founder of FuzzyMath. FuzzyMath is the user experience design consultancy that brings consumer-grade UX to business applications for B2B and enterprise tools. Today, I have the opportunity to speak with Ryan Glasgow, who is the CEO and founder of Sprig. And Sprig is an AI native survey platform

and it's common for legacy tools like Qualtrics and SurveyMonkey. If you're a designer and or a researcher and you've used those tools, you know the market is ready for a replacement. So we spent some time discussing how Ryan and his team at Sprig have developed an end-to-end survey tool, covers in-app, sort of in-context surveys during key workflows, which is very much needed in many platforms, and into longer form surveys, which they literally just released. And then how this AI-driven or AI sort of forward platform helps with synthesis and analysis.

You can see it as basically a partner for your researchers out in the field. And then we touch a little bit on how Ryan's team actually uses Sprig to learn about their customers and to make their own product better, which is just kind of a cool meta thing. And we cover the current state of sort of the research landscape and how the past few years have been tight budgets and tighter timelines. But Ryan paints a picture based on conversations with many UX and product leaders of a bright future for research, one where researchers are more involved in the strategic direction of a product or company. And Ryan ends the conversation with an offer to retail to him to get a demo. And it's something that I'm going to ask a few folks on my team to do for sure. So, okay, please enjoy the episode after you listen. And if you've enjoyed it, please like, share, and rate it. And thank you as always for listening.

Mark Baldino (00:01.596)
Ryan, welcome to the podcast.

Ryan (00:04.024)
Thanks for having me, Mark.

Mark Baldino (00:05.352)
No, it's my pleasure. I would love if you could give the audience a bit about your background. I know you're currently leading the team at SPREG. Would love to get there. But how did you start in the industry? How did you sort of land where you are now?

Ryan (00:23.214)
Yeah, my background is in product management and I've helped several companies really scale their EPD organizations and

Before starting Sprig, I was the first product manager at a website builder called Weebly and helped build out the user research team there and really saw firsthand the antiquated legacy survey tools like Qualtrics and SurveyMonkey that research teams and design teams were using. so started Sprig to really modernize and rethink and reimagine the legacy survey that really needs to be, it needs some attention, it needs some rethinking. And so we've been really focused

I'm here at Sprig. And we're now tracking over 1 billion end users per month across our platform. We're working with many of the fastest growing companies and their UX teams, including Figma, Ramp, Notion, DoorDash, just to name a few. And so it's been really exciting working with them on scaling their insights and scaling their impact across their UX programs.

Mark Baldino (01:24.884)
That's really, really cool. want to, before we get to legacy, can you talk about the, this is like a personal question, right? So I started as a designer and then I made the pretty natural transition to being like a consultant, freelancer, and then starting an agency, right? What was the decision to go from like product leader, product management leader to like CEO and founder? First of all, it's very impressive. Congratulations on success in making that transition.

What was that natural? Was it like something you always wanted to do? Like how did that go for you?

Ryan (01:59.47)
So I had joined four different companies pre-launch. And so founding team member very early on, one of them I showed up.

you know, I accept the job and the first week we're figuring out the company name. And so, you know, I've been, definitely wearing some of those early hats as a product manager and help bring some new companies to life and new products to life. And, you know, after those experiences and helping scale Weebly, I wanted to either join as a first PM. I was actually interviewing as the first PM at Figma and Notion, which ironically are now, you know, spring customers.

Mark Baldino (02:33.972)
Clients, yeah, yeah, yeah, great.

Ryan (02:37.408)
And, to decide as in those interview processes and decided actually, I really want to see how this goes with re-imagining surveys and really rethinking the survey experience.

And so I let Dylan and Ivan know like, hey, I'm going to sit tight here. I'm going to focus on this other thing. And I think it was really about a problem that was really core, one that I'd felt throughout my career. And for me as a product person, building great products always started with deeply understanding the customer problem.

and deeply understanding the customer problem is really the heart of user research and customer insights. And so that was really where I spent so much of my time as a PM and partnering with world-class researchers and world-class designers to deeply understand the customer problem. And I knew if I could deeply understand the customer problem, building the right solution was actually fairly straightforward.

far easier than I people make it out to be if you do all that upfront work. And so it's really exciting opportunity to now help other product teams and other product managers scale their own EPD, their own customer insights across their organizations and do it in a more meta way, but it significantly increases my impact as a product person myself.

Mark Baldino (04:02.58)
So what was it with the legacy tool? I mean, it's great. You didn't step into an industry and maybe saw, there's a financial need here. I'm going to go learn as much as I can about this sort of product space and build the SaaS tool to meet that market need. You were living the experience yourself. You're using these legacy tools in your career. How are those tools not...

working, how are they letting you down or teams down or maybe the folks you were working with.

Ryan (04:34.232)
So what I saw with Qualtrics is one of the first things that was really broken was the open text analysis. And I saw very skilled user researchers and designers, product managers even, spending sometimes over a week analyzing open text data. And that was a visceral problem that I observed as a product person. And it was kind of a classic, there has to be a better way.

The second one was how traditional surveys are disconnected from the user experience. And so if you maybe cancel a subscription or you complete onboarding or you sign up for a new product,

you might get a 50 question survey a month later and they're offering you a Red Lobster gift card and they're saying, hey, how was your onboarding experience two months ago? How was your experience using our first product for the first time? Did you try our new feature yet? Yes or no? Do you know we have this feature? Yes or no? It was so disconnected from user behavior. And it's how can we bring survey data and survey questions in context?

As you complete onboarding, can we ask you one or two survey questions in that moment? As you maybe try a new feature, can we ask you one or two survey questions in that moment? When you do cancel a subscription, can we ask you after you cancel about why you made that decision? And by bringing survey questions into and connecting them to user behavior, it is significantly better experience for the end user. So you make an

action, you immediately get a question about it. It's not 30 days or 60 days later buried in a large survey, but it's significantly better also for the product team and the design team research team who can now get much higher quality data, much higher response rates to their survey questions. And they also get much more user detail because it's in context and connected to the exact moment that they're making decisions.

Ryan (06:37.056)
And so it's really from first principles. How do we solve the challenges of getting in-context survey data and pair that with analysis, AI analysis? This is 2019. This is pre-LLM. So know people were using word clouds at the time. And you say price, I say price. There's a big word that says price. So it's very, very difficult. Yeah, you say the price is too high.

Mark Baldino (06:59.508)
Super awful for making design decisions. Is price bigger than discount? What's the relationship? What's statistical relevance here? Yeah.

Ryan (07:07.854)
You only get one word and that's it. We could be saying different things, right? You say the price is too high. I say the price is completely affordable for everybody, but some segment. But it says price. And so it's how do we really approach these two problems that have been solved before and do it in a vertically integrated way to meet the needs of modern product works.

Mark Baldino (07:33.758)
How come you didn't just do the second half and do like an analysis tool? Why build a survey tool when, I mean, there are in-app like sentiment analysis tools that you can embed. Obviously Qualtrics, I thankfully haven't had to use it in many, many years, but SurveyMonkey is still pretty popular out there. Why did you all make the decision not to just, okay, we're going to export data out of these tools and we'll put it into our tool and we'll just handle analysis. Why did you go end to end in that sort of research space?

Ryan (08:04.458)
What I noticed is that the AI really struggles even to this day when you have a very wide array of data that it's analyzing and very unstructured data sets. So you have some questions, I have some questions, we're taking the responses together and grouping it and having AI try to understand different types of data.

We've also seen when the data and the questions lead to just different types of responses that go in different directions. It doesn't always set the AI up for success.

And there was no solution at the time that was really built for companies at scale to collect survey data in context. Google and Meta had built internal tools. And if you use Google Suite, if you use Facebook's products, you will see in product surveys. But there was no vendor at the time. And even to this day, for companies that are already at scale with millions and so many of our customers have hundreds of millions of users.

Sprig is the only solution that can support large-scale and context serving.

Mark Baldino (09:18.004)
Okay. I mean, that's fantastic. bit off a you know, it's a bit pretty bite of the apple, is great. But, you know, the cool thing about tools like Figma and Notion is Figma uses Figma to design Figma and Notion uses Notion as they're designing Notion. You know, the old phrase was, you eating your own dog food when you're developing

Ryan (09:25.346)
Yes. Yeah.

Mark Baldino (09:47.912)
software applications. How do you all use Sprig in the design of Sprig? And what have you learned by using your own research tool that's kind of adjusted how you've gone to market or the feature set?

Ryan (10:01.512)
One of the exciting things about working with so many world-class organizations is we actually learned from them how to use our product. And they're always teaching us new things. Remember, Robin was one of our very first customers. And they taught us how to integrate, for example, in product serving into A-B tests. And so that's something that we now do, where you can get experience data alongside and embedded in each of the feature flags or design changes or A-B tests that you're running.

Mark Baldino (10:08.798)
right on.

Ryan (10:30.752)
Another example is working with, you know, companies like Figma.

doing experience measurement across different critical user journeys. And so if you use Figma today, there are specific flows that they are measuring at a very small sample size with SPRIG to quantify the experience of the user with short in-product surveys. And so we've taken some of those principles and learnings and applied it to our own product.

where we now run continuous and product surveys at a small sample across our product lines.

We also run in product surveys and our own A-View tests and feature flags and product rollouts and make sure that Spring is integrated into our A-View testing system launched darkly. And so it's been exciting to just see how, what we're learning from and applying it to our own use case. Another really common use case for us as well that we learned from our customers is around in product recruiting. And so everyone loves it. Seamless, targeted, qualitative,

recruiting. And so with Sprig, can target a specific group people that try a new feature that you just launched.

Mark Baldino (11:41.076)
Yeah.

Ryan (11:47.182)
run a short screener right in context, link to your Calendly and have the scheduled time right on your calendar. And by the end of the week, get 10, 30 minute calls scheduled with people that just tried your product or your feature that you just launched. And so it's almost like in context, qualitative interviews as well. And so that's been a really popular one for our product team before they go into a project or after they just launched something truly do to have a

qualitative conversations and do all that recruiting right within SPRAG.

Mark Baldino (12:21.31)
Yeah, I was going to ask that. So obviously surveys are giving you quant data. Is the in-app stuff like key workflow, is it sentiment analysis? How do you rate this experience of one to five or something like that? I imagine it could be anything. It was the best practice that you're just kind of like picking a point and asking them to provide it. And then you're getting a longitudinal view in terms of research over time, but also like throughout the course of key workflows, like what the satisfaction level is.

There's some pain points, like getting that.

Ryan (12:54.286)
Yeah, so we've generally seen a closed question to start. We'll get the highest response rate. Our general recommendations are 1 to 5 scale, which works really well in context. And then the follow-up question using open-ended. We're actually rolling out dynamic open-ended AI-generated questions right now, which is really exciting.

Mark Baldino (13:13.352)
What does mean?

Ryan (13:14.35)
And so it could be a couple of questions pop up. You can have AI actually ask a follow-up open-ended question. And so it could be, hey, Mark, why did you choose? You mentioned that our new feature that we just launched was missing some features. What features would you like us to build that you're referring to? And so it's actually taking and referring to

what you previously said in the survey and having custom questions for you, which is really exciting. But generally, start with one closed question, one to five, and then a follow-up. It's historically been a static question that the researcher or designer will write, but now you can have AI actually ask dynamic questions for you based on the user's response. And so that's generally how we recommend the end product surveys to get the highest response rate.

Mark Baldino (14:11.87)
Right on. And then for my qualitative interviews, is that done through your platform as well or is the data I would take out of an interview just notes? Does that get put into the tool for analysis?

Ryan (14:26.136)
We focused on unmoderated for qualitative. And so we do have video and voice questions, prototype testing, things like that. And that's been a really exciting use case that we've seen emerge.

embedding that you can embed a Figma prototype and have some video questions and tasks and screen recording about your Figma prototype. We don't do the moderated. There's some really great tools out there. A lot of folks just even use Zoom or Google Meet and maybe Granola transcripts. so we've generally recommended partners for that, knowing that it's a very solved problem.

Mark Baldino (15:04.564)
Okay, yeah, fair enough. Constraints are good, Ryan. You have enough on your plate. So it makes sense to focus on slightly more structured data. And then what about the analysis? How are you helping folks in UXR, design and product teams, once they have all of that data? What does that side of the coin look like?

Ryan (15:25.166)
That's where we've done some pioneering work since starting the company in 2019 is we spent a good year really analyzing how expert user researchers analyze open text data. We looked at the exact patterns and how they move through large data sets of open text responses. And at the time, we're using open source models from Google called BERT.

And we built significant modeling and data labeling in-house with OpenAI and newer models. We're now using the latest LLMs like 4.0 and 5.0.

but we've essentially taken the motions of expert researchers. We broke them down into a very advanced series of prompt chains and it's going through very specific tasks to recreate the open text analysis that a researcher will typically go through. We've compressed that time of two or three weeks of analysis down to real time open text analysis. It'll have custom written themes, custom written summarization.

all the clustering automatically, even if there's no overlapping words or phrases. You don't have to say price and I don't say price anymore. You could say cost and I can say price and look at the intent of what we're saying and summarizing it in one theme. But we've really been pushing much further. We now do beyond question level analysis like OpenText. do.

Mark Baldino (16:41.64)
Right. Understands the difference. Yeah.

Ryan (16:57.386)
survey summarization. So we'll give you the key takeaways from an entire survey and we also do synthesis analysis as well. So it'll come back with the key takeaways from the entire survey. We've broken those down into different types of takeaways. It could be a correlation or a trend or an opportunity or a strength.

And that's been really helping UX teams run a survey or get in context survey data and in real time, not only understand what the survey data is saying, but also have AI draw conclusions and next steps and takeaways of what to do with the survey data. And so we're moving it beyond just analysis to actually synthesis. And that's where it's been really exciting to think about

You run a survey, you know what's happening, but more importantly, what are you doing with that data? And that's where we're really doing a lot of work with AI to make sure that teams understand exactly what to do next.

Mark Baldino (17:58.918)
Is it more of like a co-pilot experience? are we, is the designer a partner, sorry, the researcher a partner in this process and modifying it and like refining it along the way or is it more of a kind of set it up, it takes the data and then it's going to give you opportunity analysis, insight, that sort of thing? What's the...

Ryan (18:23.234)
Yeah, so it's all done automatically. You run the survey, all the data streaming in in real time. It's doing everything for you. We do allow you to enter a survey goal. And that survey goal really helps with the prompt instruction for AI to know exactly what you're focused on. It would regenerate the results with your survey goal if you wanted to add that. But there's no input or effort required from the UX teams.

Mark Baldino (18:47.348)
Okay. Okay. So, mean, the flip of this is like, were two, there were challenges with the legacy tools. You saw those firsthand. You're building a tool now to support, you know, robust, as you said, like products at scale and the teams that are designing and developing them. As you're out there, like, talking to folks who, maybe practitioners at the researcher level or...

managers, leaders, people who leading product teams, design team, businesses. I'm kind of curious about some of the challenges that you're hearing from these folks. doesn't, from my perspective, and I think for the audience, it doesn't always have to tie back into research in general. But I feel like you're probably out there as a business leader having a bunch of really interesting conversations about the state of design and product management. And I'm just kind of curious,

what you're hearing and if you can, if you want to tie it back to spring, like that's awesome, but you don't necessarily necessarily need to. I just think when I talk to people, they're always like, what are you, what are you hearing? What are people telling you about the state of the market? Or I'm going to run a consultancy. They're like, what's the consulting, what's your consulting business like these days? But so like when you're talking to leaders out there, what are you hearing in the market?

Ryan (20:07.726)
A key focus has been efficiency. I think particularly for design leaders out there, UX leaders, you see the stock market, things look great, but it's nearly all driven by seven to eight companies. And so what we're hearing in the front lines is that if you're not one of the 78 companies, you have a tall task ahead of increasing the team's productivity.

and reducing cost. And cost is both tooling as well as headcount. And so we're seeing folks lean towards more junior roles, less headcount, less tools, but also how do they increase their impact? How do they increase their velocity? How do they increase their scale at the same time? And it's two counterintuitive goals because doing both the same time does create strain.

And so we're seeing, you know, a lot of teams rethink, you know, the stacks, rethink the roles, responsibilities. We're seeing designers start to take on tactical research and evaluative research.

We're also seeing researchers using tools like Claude to create prototypes during research sessions to fully understand and prototype ideas based on what customers are asking for in moderated user research sessions. We're seeing product managers create prototypes of designs to showcase to design and to engineering of the customers. We're seeing designers start to code. And so I think there's a

learning of roles responsibilities in a positive way but I think ultimately it's how can we accelerate our work as a team as an organization but do it with significantly less resources but you know

Ryan (22:04.14)
people, human capital, as well as the tools that we use every day. And that's where we're seeing lot of innovation and exploration and testing and hypotheses that have been really exciting about how EPD orgs are evolving for an AI First world.

Mark Baldino (22:22.224)
I think what you're pointing out is people, where they've been limited... I'll use design as the bottleneck here because I'm a designer, so I'm fine with it. Product feeling like there's a bottleneck in getting rapid design and prototypes out the door, right? And so they're out there putting prototypes together to visualize their ideas when they used to...

rely solely on design. Or as you said, designers being limited by maybe the tools and timeframe from an interaction perspective, so actually starting to code these things. And so there's these kind of gaps in journeys of product design and development, and people are filling them with this outgrowth of AI tools that allow things really, really sort of rapidly to generate content, UI.

and code. I like to think, and I'm curious your take on this, that it's freeing people up to do more complicated things and where I very simplistically like to say, let machines do things that machines are better at and this allows humans to focus on things that humans are better at. So in this world of research, it'd be maybe some of the insight and opportunity

Maybe not the initial analysis, but that second stage and then connecting it to the roadmap and prioritization and those human discussions of promoting a feature. But do you kind of agree? Because there's a healthy, in certain groups, organizations and roles, of fear of this sense that, yeah, we need to be more efficient, so let's put tools in here. But the flip of it is sometimes you're freeing those same resources up, those human resources up to do...

things that they're better suited for and that are actually more impactful for the organization.

Ryan (24:22.766)
I've been talking to UX leaders, both design and research. And one of the key takeaways is starting to emerge for those that are AI forward, the ones that are really innovating and learning and testing with AI, is that they don't see AI as a job replacement. It's not there to threaten them or to take away their job. It's not another coworker that trying to push them out.

But the AI is an intern on their team. And they are a manager who has AI as an eager intern that's looking to take on tasks off of their plate. And so think you're exactly spot on.

The folks that are ahead of the curve and the innovators with AI tooling see AI as their eager intern and they ask it themselves, how can I accelerate my impact and my work by having this eager intern called AI help me do my work?

I think the ones that we're seeing fall behind with AI are the ones that are hesitant to really fully embrace AI tooling and see AI as a peer that is competing for their role. And there's kind of maybe a denial or maybe a fear around AI. so I think for, based on where you're on the spectrum, I would really encourage those of you out there.

that are seeing AI as maybe more of a peer or maybe there's some fear around AI to think about how you can really see yourself as a manager of AI and to your point, accelerate your own work so that you can increase your impact and the more that you can increase your impact at your organization.

Ryan (26:13.646)
That's really what, you know, that will allow you to take on much more strategic projects and initiatives and think at a much higher level, which is really the role of a manager. The manager can then think at a higher level, help think about a longer term view, help see around the corner, help think more strategically. And, you know, if you're an IC, AI is there to really support you so that you can elevate your own level of thinking on your own journey to achieve that.

Mark Baldino (26:44.132)
Fantastic. Thank you for that. I think I agree. And I think the point about the folks that are behind, first of all, you're not that far behind. You've got time to catch up, right? But you have to start using these tools and you have to see it as a complement to your work because the people in management and above you in the organization are now expecting you to use these tools. For a while, I think people were guilty and felt guilty, right? Oh, I'm using chat to...

do A, B, and C, and you kind of keep it in the side. And now they actually are feeling the opposite pressure, which is like, if you're not using this to increase your productivity or efficiency, or you're sending really lengthy emails and you're not running it through JAN AI to get it down to its core, which is something I do all the time, you're kind of missing the boat. It's a necessary skill set. I would be remiss, and people on my team would be bothered if I didn't ask you this question, which is, you know,

for a while and in down markets or when budgets are being cut, research is generally one of those things that goes to the wayside. Unless you're in an organization that like yours, where you've come from this space or you're a business leader and you just understand the value of doing continuous monitoring of the pulse of your customers and folding that in an innovative fashion. So for me, if I'm pitching work,

I talk about research, early phase, later phase, discovery, validation. A lot of times people see that as too much budget, too many weeks, too slow. And it can be something that gets, we already know our customers. We know what they need. so my ask to you or my question to you is like, obviously you can position Sprig in a bunch of ways so that it is cost sensitive and it speeds, but like,

What is the answer there, either about the value of research and keeping that on point or what you're hearing in the market? It just feels like, and just had coffee with somebody in the UXR field and they were facing this pinch of like, there's not as many jobs out there for UXR. seems like budgets in general are lower and for UXR, they're even a little bit lower. So what's your response to that, that research is the first thing that can go and how do you position it so it's something people

Mark Baldino (29:10.27)
starting in their process with.

Ryan (29:12.782)
So the research role and the research field is very much in that R &D category. so if companies are innovative, they're bullish, they're building your products or launching your products.

Yeah, I think absolutely you're going to see the growing research. I think right now we're seeing an upswing in research hiring. We're seeing a lot of companies adding headcount, growing their teams right now across our customer base. That was not true two to three years ago. You we saw a lot of contraction of teams and reduction in headcount for all teams, but research was one that was certainly impacted.

I think what we're seeing though going forward is a healthy balance and probably the right balance where we are seeing teams where research is shifting more to a strategic research role. And so they're thinking about how can I work directly with the executive leadership team to think about the company strategy and to help the company with larger strategic decisions such as product expansion, international expansion, expanding into new markets around the globe, tailoring the product to maybe entering the European market or the Asian market or South America or Central America. And so when we're seeing a shift of research to be more strategic, and I think that's where we're seeing an increase in headcount, we're seeing a reduction in evaluative or tactical research by researchers themselves, and we're seeing that work shift to

designers and product managers to really democratize research. 
And so it's been interesting to see a lot of researchers embrace democratization and say, hey, let me actually increase my impact to the organization and focus on what the CEO or the CTO or the CPO or the CMO are really focused on right now, which is not this month or this week or this or today, but it's two quarters out or next year. And that's those are the

Ryan (31:18.068)
research teams that are growing. I do think though that the researchers that are holding on to evaluate over tactical research, maybe lightweight prototyping, testing, evaluating a mock-up between mock-up A or mock-up B, that's where we are seeing the reduction, but it's shifting to other people at the organization. And so think it's a good right sizing that's happening. I think the role is really hitting its stride of increasing its impact.

But it does mean that there is a shift of resource unit and a shift of headcount from one part of the product development cycle to another. But others are absorbing that work, and they're often not researchers.

Mark Baldino (31:58.74)
That's awesome. It's funny, I was going to ask you where you think the future of research is and you just answered in that question or the answer to that question which was it is in shifting towards a more strategic impact and it is connecting research at the product level up throughout the hierarchy within an organization and tying it to things that the people who lead the organization care about and what metrics and KPIs they're monitoring and folks that can...

speak that language and can conduct research and tie that research to those metrics and KPIs are the ones that are going to kind of experience growth in their field and have a continued career. So appreciate you getting my final question, which I think is just a great place to wrap up in terms of where you think the research field is heading. So I want to say thank you for your time today. Thanks for talking about

Spreg, it sounds like it's been an amazing journey thus far and you guys have a bright future. If people want to reach out to you, Ryan, where can they find you?

Ryan (33:07.404)
Yeah, so we, I'd love to hear from folks. We'd love to show you the product, Sprig.

And we are rolling out what we call long form surveys right now. So I started the company focused on in context, in the moment, surveys for websites and mobile apps. With this year, we've been very focused on unifying surveys from SurveyMonkey and Qualtrics. Those email surveys, the shareable link surveys that you send out to customers. We now have a very robust survey product to fully replace Qualtrics and SurveyMonkey.

And so if you're using a survey tool today, whether it's in context or whether it's an email or a link that you send out to customers, we'd love to share more about both of our products to really unify surveying and reduce the number of tools. A big focus right now is tool reduction. We're helping a lot of teams do it right now. We've heard five survey tools from companies with SPREG that got down to one.

We've heard three tools got down to one, two tools got down to one. So love to hear from you and help you on that journey of reducing costs and increasing impact.

Mark Baldino (34:15.922)
Awesome. All right, your legacy tools you heard it, SPRIG is coming for you folks. So Ryan, thank you for your time. Thanks for your insights. Great to hear about your journey. Great to hear about SPRIG as an organization and as a tool and definitely your thoughts on the research field in general. So much appreciate your time and energy.

Ryan (34:33.486)
again thanks for having me. It's been a fun talking about the future of research and where things are at right now.

Mark Baldino (34:39.352)
Thanks.