← Insights Unlocked
Insights Unlocked
2026 trends in CX and UX research with Lija Hogan and Amrit Bhachu
00:47:11
Discover how UX leaders are using AI, scaling insights, and shaping voice-first design to drive impact in 2026. Insights from UserTesting experts.
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- [00:02]
A
Welcome back to Insights Unlocked. In this episode, we're turning the mic around as I chat with my co hosts, Leah Hogan and Amrit Bachu, both principles of Experience Research strategy. Here at User Testing, we're diving into what's top of mind for UX and CX leaders heading into 2026, from AI experimentation to the growing pressure on researchers to show impact across the business. Enjoy the show.
- [00:30]
B
Welcome to Insights Unlocked, an original podcast from User Testing where we bring you candid conversations and stories with the thinkers, doers and builders behind some of the most successful digital products and experiences in the world, from concept to execution.
- [00:50]
A
Welcome to the Insights Unlocked podcast. I'm Nathan Isaacs, principal Content Marketing manager at User Testing and our guests today are our sometimes host and that's Leah Hogan and Amrit Batu, both principals for Experience Research strategy at User Testing. Welcome to the show, Leah and Amrit.
- [01:10]
C
Yeah, thanks, Nathan. Lovely to be here. Interesting be on our side of the microphone for once.
- [01:15]
A
No, we're going to put you guys on the other side, but actually we're here with a mission. But before we get started, I'm just wondering, can you just share with us a little bit about what you guys do on a regular basis for User Testing? You're kind of road warriors for the cause of customer insights. So tell us more.
- [01:36]
C
Yeah, we spend a lot of time, or I certainly spend a lot of time with customers, and we're having these conversations with them to help them really show the value of the work that they can be doing with User Testing, but also be able to show the impact of their team internally within their own organizations. And then there's various different ways that we can go about that, depending on what their specific challenges are within their own space. I don't know, Leah, what would you add into that there?
- [02:09]
D
I think there are a few things. One, a lot of what it is that we do collectively is very much informed by our experience as both researchers and designers. So I know that we've both done some dabbling on both sides and I think also I always call it being an innie and an outie. So having been both a consultant and then working internally with organizations, and that gives you a lot of perspective around what's in the heads of leadership, business partners, and also the researchers who are our primary contacts with a lot of these companies.
- [02:48]
C
One of the quotes I often use with customers is a Mark Twain quote, and it says, good decisions come from experience. Experience comes from making bad decisions. And I feel like being an nan who've made so many bad decisions in our career, we've got those hanging with us, and we're able to share that kind of knowledge with our customers and help them not repeat the bad experiences that we've had and learn from our mistakes going forward. Essential. Yeah.
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A
And I think it's not just the mistakes you may have made, but the mistakes that you've learned that other people have made along the way too. Right. That's the value of a consultant. Right. And that's kind of what your role is. You're coming in helping these Fortune 100 companies or whatever it might be, just to speed them up. Right. To help them avoid some of the mistakes that they may go through, or not even mistakes, but just time to see the value in whatever they're trying to do, the research that they're doing. You can bring in and like, oh.
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C
Well.
- [03:53]
A
A healthc care company may do this, or a technology company we've. That we've worked with in the past has done this and maybe approach it in that way. Or there are new. You guys are both experts, not just working with companies, but talking with other. Your peers around the world. And so you're bringing that sort of expertise into the whole thing. And I guess that's why we have you here today. We're just kind of curious what customers are thinking about and talking about and worried about as we head into 2026. So, Leah, why don't you kick us off here?
- [04:27]
D
Yeah. So we have a number of topics, and I know I talk to multiple teams every single day, and most of them have a global remit. And so a lot of them are really focused, I think, on that first piece around AI and having just come from a webinar and hearing a lot of leaders talking about the challenges that they're facing in that space, AI really is here to stay. I think that's one theme going into 2026 is critical for us to define how we create agency and as both leaders and individual contributors, so that we're not on the back foot going forward around artificial intelligence. And then how do we essentially execute on that promise? And when we're in this space where there isn't a lot of experience, actually, because this is actually new, I think one of the biggest challenges I'm finding is there isn't a whole ton of storytelling that is established over years of practice yet, but people are using some of the strategies that they have built over decades of practice and reframing them in this new space. And I think you probably have some thoughts about that too.
- [06:07]
C
Yes, I think the, I've spoken about it on this podcast before, I've spoken about it in blogs, et cetera, before that. Sometimes within the world of UX and design, we struggle to move forward from where what we've done previously. And I think I really gives us now an opportunity, especially as, as the initial boom is settling down. I think it gives us an opportunity to really sort of progress the industry and work more efficiently with the other constituent parts of an organization of what we're doing and how we're doing it. And I'm really quite excited about that as we look into next year and beyond.
- [07:01]
A
As we think about AI, you can kind of think about it in two sort of ways, right? The AI that's going to be public facing that our customers, customers are going to be interacting with, right. And I can think of, you know, apps for if you're an airline or something like that and you're booking and you're using their AI sort of chatbot. But then there's also the AI that researchers and designers and others are using to kind of speed up their processes and workflows as they try and learn more about customers. What are, how, you know, what, what are people thinking about? What are the challenges? What, what are they not thinking about that they should be thinking about? Leah?
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D
Well, I think that's a real challenge because there are folks that for very personal reasons and very, you know, sometimes business reasons are more on the I'm going to be a leader side and then there are those who are, I am going to be a very intentional laggard because reasons and I think that there's good, there's a grounding for both of those perspectives in reality. So for the leaders, what I see is a lot of experimentation and training and figuring out what the use cases are that make sense contextually. Because I don't think, I think a lot of companies leadership teams are saying use AI, but they aren't providing a framing to say like here's how we expect you to do it, here are the guardrails, here's where it shines, here's where it's not a great idea. So they're basically relying on teams and mid level management to essentially define what those use cases are and then how to do it. And there are teams that I think don't see a whole lot of value yet in what it is that they're doing because there are a variety of reasons. One, some of the tools haven't gotten to the point where they are robust enough to be able to deliver on the promise that I think the hype is out there setting expectations around. And then I think the other piece is garbage in, garbage out. And that garbage in can be the actual data, the garbage can be the prompts that you're using and even the logic that you're using around just how to use it. And so you will only learn whether it's garbage when you try it. And not just once, but multiple times. And so I think it's a little bit of a moving target, but I'm curious to hear on with what you have to say about that too.
- [09:59]
C
I'm picking up on that element of garbage in garbage out there. Leah. I think as researchers in particular, we are usually quite quick to dismiss the garbage in, garbage out element of it. If it's not giving us the answer we want really quickly, we fall back on our previous experience and say that's not the way to do it, we should do it this way. And I think there's going to be a need to go through trial and error as we move forward here. As you said at the top, AI is here to stay. So how do we do experimentation on our research process to find ways that uniquely within our organizations, benefit us? It may not be the same for each organization, but you've got to try things. You've got to not be successful. I'm not going to use the word fail. You've got to not be successful. You've got to learn why you aren't successful and try again in a different way moving forward. And I think that's got to be a really underlying foundation of how we think about using AI within research and design and CX over the next few months and again going into the next year as well.
- [11:27]
A
We've had a few episodes on the podcast. I think we talk about AI every episode. But we had Mario Caligaro who was talking about, really about the different prompts and learning from prompts and sort of the science behind prompts with researchers and, and learning about that. We had John Whan on talking about synthetic. And you both interviewed each of those guests also. I, I think, you know, so there's something to be said there and for people to go back and look at, listen to those episodes. We'll provide links to those. And something in my role which is not research. I'm a marketer, but I'm produce videos and, and we're relearning how to do videos. Right. Everyone can create a video, but you can't create a, a long video with AI. Right. You have to, you know, these, you're hearing about these things that sort of blow up. There was a fast food chain that, you know, produced a holiday commercial that had to get pulled quickly because it was insensitive to a lot of people and just didn't look that great. And. But I heard that the, the team that produced it did it with 70,000 prompts. Like, I don't know what, you know, like the effort to do all that for this AI video, probably you could have done it cheaper and faster if you just done it the old fashioned way. So I don't, you know, but it's always having to experiment and learn along the way. But also I'm wondering which, if you think about it from the customer insight and your customer's point of view, what should companies be thinking about as they play around with any of this AI, whether they're using it internally or putting it out externally? Leah, you know, like what, what it's that risk, right, of like blowing up in your face. How do they avoid that?
- [13:24]
D
Well, I think the biggest, Well, I, I personally have my own rubric actually for when I choose to use AI or not. And it's really informed by experience that I had where I was I working on this very normal, very practical task of creating screeners for like seven different segments, right. Working alongside one of our customers. And I thought, oh well, this would be a great potential use case for bringing in the assistance of AI to just like polish and provide some additional, you know, best practice guidance and all that. And I did a search using, I will use to say an unnamed LLM and found myself being quoted back at myself. And that was like truly a moment where I was thinking, gee, the moment when you know that you would totally write it faster and the AI is just going to quote yourself back to you that tells you you should just like spend the time writing it yourself because you are going to be better, faster and spend less time just trying to fix a slop. And so I think there's one, like the rubric in my head is, is it the sustainable option is this is what I'm about to do worth the energy and the water required to produce whatever it is? Yeah, yeah, yeah. And then I kind of go from there, like, am I, is how much work is it going to take me to do it versus get it to be just the right thing in the back and forth that you need to have with a collaborator? And then I think, you know, the other piece is, you know, practically at what level of like dialed in granularity Precision. Do I need to have this answer? If it's just a, oh, it's a draft. But if it needs to be like perfect and polished, you know how like I will invest the time and the attention.
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A
Go ahead. I was, I was going to. And yeah, if you can also just address like what companies should be thinking about as far as testing whatever AI they're going to roll out.
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C
I was going to say, Nathan, that said fast food chain that you referenced should have used or tested that video before it went out. And I think that's a really good example there of no matter if it's AI or anything else, the time that you've got is going to dictate how you go about that task. But if you're taking a new approach to something, then you've got to have a way of validating it. If you do not have a time to validate your approach, then that's probably not the right approach to use at that point in time. Whether that's through user testing, whether that's through analytics, whether that's through anything else, you've got to make sure that you're, you're validating and you've got the elements in the right place and you've got to understand how you're validating it as well. And I think that's a really key point that we sometimes miss, that we do something and then we find a reason for accepting it or denying it and it's all about good research process or what is a hypothesis, what are we aiming for here? What's the metrics that under pin it? How do we talk about those metrics? How do we measure against those metrics? And I think this is where Leah's example of the unnamed LLM spitting back her own language at her. Leah at least had the time to go and do that. At that point in time she was able to validate it and then come to her own conclusion off the back of it. Whereas we hear about it in every single industry just now where people are taking what is told to them by the LLM as verbatim. And if you're not, if you're not at that point of double checking, we know fine and well that that that same system, the same question asked in the same way may get a different answer at this point in time. So we need to keep going back over and we need to make sure that we are in the right place for, with it, but in a timely manner.
- [18:18]
A
Right, right. And I think, you know, going back to that fast food chain as they're producing that in. These things take time. They take, you know, several weeks, several months to kind of make that along the way. You got the first 20 seconds of that commercial done. Test it. Are we going on the right path? Oh, no. Nobody loves this idea. Let's do something else, right? You, you can test along the way. It doesn't have to be at the end after you've all this time and effort. There was a, a newspaper that just rolled out like a personalized podcast that's AI generated and it's, you know, nothing. It's not accurate, it's not very, you know, warm and fuzzy. It's had nothing but negative results. But they invested so much time and effort, they just rolled it out anyway and said, oh, we'll just fix it along the way. But, you know, that. What does that do to your brand, I guess. Right? All right, so we've, we've. I've, I've taken up way too much time on this AI topic. I think it's definitely something that's going to be on 2026. But, Leah, what's, what's another. You, you mentioned you had five. So what's number two?
- [19:29]
D
Well, number two is kind of like maybe two things blended together. And so the first part is, and this is kind of like spoken about this a little bit today, because it's also related that AI course of thought around research impact and showing it, and I think also the context of making the case to continue doing research with people at the helm, people as the orchestrator, the primary collaborator, with AI as a support and an assistant and accelerator, and then pairing that with the expectation that now that you've got access to the power of an assistant that is trained across a number of different disciplines, the expectations around your personal skill set as not just a researcher, but the scope and remit of your responsibilities. There's a little bit of creep there. And I say a little bit, actually, I mean, a lot. So there's like, you are now, I think, as a researcher, expected to take on more responsibility for thinking about the impact and business storytelling, for creating deliverables that are not just reports that sit in a repository. But are you curating experiences that get people to actually develop and then leverage empathy in their work? And notice I didn't say design work because it's not just designers anymore, it's everybody who benefits from human insights. And I think that it also manifests in that, you know, we've had this pendulum swing. I've been in the industry long enough, and I know amrit has too to know. We go through these periods where people are unicorns and specialists and we are now in the unicorn end of that pendulum swing.
- [21:46]
C
I think it's an exceptionally exciting time to be within the industry. Whether, as I know you didn't specifically mention design there, whether it's UX or design cx, where we've had over the last couple of years a fear of our industry, a fear of losing jobs, etc. AI coming in and taking over these jobs, we've now got that opportunity, with the benefit of AI, to really own that internal communication, that internal impact communication within an organization. Because now your tech stack is open to you. You can go into your analytics tools, you can go into your survey tools or whatever else it is that you're using for quantitative insights and you can go and pull from it what is of interest to you. You can then mold your insight story around the qual and the quant being combined together a lot easier. You don't need to be an expert, you don't need to be a data analyst expert to get the key points at any one time. You can then use that, leverage that to tell your story. And ultimately not everyone can tell contextual, qualitative story, but everyone is interested in the quantitative story. We can be the key holders there and drive that kind of conversation forward and be an integral part of a team that can't be put to the back or pushed out of the way. And I think that is really, really exciting. As we move forward into this next phase of AI.
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A
How do, how do teams or team leaders make implement that sort of thinking within their organizations? What should they be doing? Yeah, go ahead.
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C
It's not a quick process. I think I've had this conversation with customers so many times over the last eight, nine months and we hope to have a click of the fingers and everything changes like magic. It doesn't exist. That isn't going to exist. But again, with that trial and error and validation approach, how do we understand what the team is interested in? How do we understand what the execs are interested in? What can we learn from what the business is trying to achieve? What are the key metrics for that business? That's all available now with pretty relative ease. External systems, internal systems, and then when we're communicating our work, how do you bring some of that language into our work? Again, if we're putting a presentation together, how do you use your LLM to assess your presentation against what it knows about what you're trying to achieve, what your business is trying to achieve how you mold that language, how you use that to mold your language and move that kind of stuff forward, how do you use that to tie that to the key metrics? And again, you're not going to be successful first time but the more often that you do that, the smaller wins that you get, you're going to inch forward, inch forward, inch forward. For me, I think in a lot of teams you're probably looking at about six to eight month process. Small win, small win, small win, small win. To get to the point of what we really need is to bring this to the front of it rather than doing it reactively after everything and then we get a big win. I think that it's, it's really the door is open for us to go and do that. No one's going to ask us to do it. We've got to take the bill by the horns and do it ourselves at this point in time.
- [25:54]
A
I'm reminded of a conversation we had on the show where someone was using an LLM. They had built out a Persona of their boss in the LLM to review their work. My boss, he's or my customer asks a lot of questions about this. Am I answering their questions? Am I explaining this in the way that my, you know, in the business goals that my, my executive team is looking for? And they're getting those answers there, I guess rather than embarrassing themselves in a board meeting or in a team meeting. Thoughts on that, Leah? You know, like, how can they implement all this? How do they, you know, start, you know, we're saying that the AI, the AI is going to save us all this time so we can be more creative, more impactful. How do we become more creative and more impactful?
- [26:51]
D
Yeah, I think that there are a couple of things I think, you know, a few years ago I did this series of talks about like, talk about the ROI of research and building that story and kind of came up with this framework that has research is happening at points like steps two through four, but the whole process extends over about seven different steps. And one of the greatest values right now of AI is actually helping us to extend beyond those two to four steps that are just like figuring out what your research is going to be, executing it and doing the synthesis required to do that storytelling upfront. The way I think that AI really helps us is to essentially get us out of our own heads around what we think it is that people are interested in, to look for signals in the data across the business, in the news, when we're looking at things Like SEC filings, those are out there for publicly traded companies. But even internally there are signals that we could catch up on too. That might be things that you would capture from internal systems like SharePoint for example, or even JIRA tickets. Right? Like these are the, this is what we're working on right now that tells you, you know, if you can, can have a little interrogate that data with the assist of artificial intelligence to help pick up on some of the patterns that might be so subtle that you' as a human capable of really hearing those. And then at the other end of the spectrum, I think AI really helps us accelerate reframing that story in ways that are accessible to our partners. So you know, to John Whalen's example, like your deliverable might be a synthetic user Persona that is built using the data that you collected in qualitative research and quality and quantitative research. Right? Like, and in that case, if you are really essentially leveraging research insights in this way, that's more useful to people because you're not forcing them to read it. Instead they're saying, well, what do you think about this? Right? And it's based in real data and not in, and not in synthetic data. And I think that is one area actually where I think researchers have been very concerned. Like we're going to replace the voices of people I think we should not be. And this particular use case where you're centering human voices and feedback but then using it in more creative ways is a great area of opportunity for us. And I mean, who hasn't rewritten a presentation like an executive summary for we generate these beautiful rich 70 page reports and it usually takes another like 10 hours or something to create an executive summary. Because editing something takes time, as we all know. Well, AI can summarize that for you in seconds. And why not use that so that you can go on and do other great research with people.
- [30:24]
C
I love how you framed there, Leah, the bringing together of jira tickets and etc. And everything else. I think it brings on the next positive impact from a team's perspective of breaking down and connecting silos within an organization when you've got that kind of access to. We don't always have our eye on what the JIRA tickets are saying as a researcher. But if you're able to quickly have insights and connect those insights to those JIRA tickets, you've got a really quick way of communicating with that team. Same with analytics. And you've got a really quick way of connecting with them, communicating with them. Same with marketing Teams. Same with call center teams, customer support teams, whoever they may be, wherever they may be in your organization. This kind of approach will help you connect those individual silos with greater ease. And that can only be a positive from my perspective.
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D
Yeah, I think that kind of very much dovetails with the next point which was really around. The fact is you can not operate at that high level without an intentional approach to the operation of your research. Right. And so the work that I've seen, you know, Kate Towsey and you know her new book and then Jake Burghardt and his new book really around how do we make sure that all the rich work that we're doing is focused on the things that matter for the organization, is being communicated effectively and is essentially leveraged as that first stop when we need to make decisions, rather than just spinning up more and more research just because we were unaware that we already had this intelligence back there. And so I think the operation and then scaling and how do we make sure that we are creating robust feedback process that enables everyone to be at least minimally competent around collecting feedback in a way that drives high quality business decision making? That is I think one of the top things that people are really, that's keeping them up at night. Actually. I think it's more of a worry than a something that people are necessarily looking at with enthusiasm because there's a lot of risk there. Right.
- [33:20]
A
So yeah, the, you know, I know that I do this in my work with the podcast and with content, you know, that I create on for the user testing all of our marketing activities and stuff like that, the blog and so forth is I think about how I can share, hey, we just published this thing I, I think about and I use LLMs to help me rewrite a notice for a different audience. Right. So if I'm sharing that with the sales team and the go to market team, I say one thing and when I do this for the, the you know, my marketing peers as they, you know, I want them to roll it out in our social media or an email newsletters or something like that. I share it in a different way. But the LLMs let me do that in a speed that I would not, you know, I wouldn't have done that in the past. It would have just, I would have taking a lot of time to write that little message and then we kind of repurpose that message over and over again. But now I'm having customized messages pushed out within a few seconds. And so that's, that's one way to kind of get that right, is to think about how can I use this research? You know, so that's my example. But what Leah, you're talking about is you have that 70 page summary of what you just did. And now you need to be able to talk to engineering and you need to talk to the, you know, the business leaders and they, they're thinking about this in two different languages. Right. And you know, engineers think about in sprints and, and your executive team is thinking about, you know, how much are we going to make or how much is this going to cost us. So that's something to think about. I, I also, and I think you alluded to this and I want people to. We had an episode with Udi Lettergore, who's former CMO over at gong, and he's like, you have to budget time to experiment. There's no LIM that would have said, hey, stick peanut butter into chocolate. It's going to be amazing. Somebody had to just kind of experiment to find that out. And I think maybe throw in a bunch of different research that you've done over time. Right. Jake Burkhardt's talking about, stop wasting the research you've done. What can we throw into the LLM and see what comes out of it? You know, hey, we just, we, oh, we found out that there's a common audience or they have a common challenge or whatever it might be. So we've covered I don't know how many topics. 1, 2, 3, 4.
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D
We've got one more.
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A
All right.
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D
Do you want to introduce it or do you want me to do it?
- [35:54]
C
Well, fire on, Leah. I think we're going to speak about the voice, the new voice paradigm. And when we were speaking earlier on, you had some interesting takes on that. So I think the guys would be interested in hearing that.
- [36:09]
D
Well, so I think this, this. I had a conversation about two years ago with someone who was leading efforts around conversational research at one of the major companies, software companies out there. I'll just say that. And they said, the screen is dead. We only care about conversational design from now on. And at the time I just chalked it up to, of course, because this person, that's their job, like, and that's what they're really interested in. But actually I am starting to see like conversational experiences and especially conversational AI is something that is popping up in everyday people's experiences much more frequently. And I mean, it's everything from. I was in a meeting with folks a couple weeks back and someone like muted themselves during a meeting to Have a conversation with their ChatGPT, like custom project to spit out something that they then share with us in the meeting. Right. To just, you know, that AI assist was happening real time to, you know, I was taking my kid to an appointment yesterday and texting my brother with voice to text and text to voice in my car through my phone and was like, oh, wouldn't it be great if I could actually set up a, an interactive meeting by just, you know, telling my systems to do that for me. And so I think we're starting to see a lot more cross platform service design that enables us to more seamlessly go from screens to voice to touch to gesture. And that requires us coming up with new design systems and patterns and strategies to help make that transition. Something that is compelling and fun. I think, you know, why not make it fun? And really just thinking beyond screens is going to be something that we're going to have to do in 2026.
- [38:37]
C
Yeah. One of the things that has been mentioned several times in this conversation today is the time element and they need to reduce the size of presentations and it comes back to attention span of people. Nowadays the attention span that we have, and we're all guilty of it as well, is a lot lower than it was 10 years ago, 15 years ago. The patience isn't there anymore. So that ability to seamlessly move from a gesture to a mouse to voice to call, to a physical interaction has to be happening. It has to be at the forefront of designing a journey's thought process. And I don't think we're there yet. I think we end up again, a lot of the conversations I have with customers, we had a great one this week where we're talking about a digital team who's, I'm going to try and avoid saying their names, so I'll do my best here. A digital team who work in a mainly physical product, essentially a place that you would go to, and they were talking about how their app could help the customer so much if they can get the customers into the app. But the team that own the floor space, the marketing team that own the floor space, don't give them access to get the team to the app, to the customers, to the app. And that has a negative impact on the spend of the customers in that space. And that's a really good example of an omnichannel experience that could be so, so better improved to be thought about the different types of interactions that could be happening and what that, that that app could do. And then when we introduce the ability to to use a voice search within the app to find what you're looking for. It's a revenue generator. But I think, yeah, there's a, there's a lot of, again, green space to play with there.
- [41:01]
A
Yeah, I, I'm just thinking it's something that's been said many times. Right. You, you, those teams are, you know, they're going to do the work that they're paid to do. Right. And so, you know, that one team is like, we're, we're being measured by people coming in on the floor space. They, they're not incentivized to get people onto the app. And so that's, that's a challenge of the leadership team really to say, hey, look, your silos aren't going to work here anymore. We need to be thinking about this in a different way. But then there is an episode we had, and I keep mentioning episodes, but we had Greg Noodleman on back last spring who at one point says like, UI is dead. You know, like you have to think about it. There's not going to be a click this button anymore. It's going to be, you know, telling the prompt what you want it to do type of thing. And I don't think we've really even discussed because we don't really know how Agenic is going to play into all this stuff where we just build these sort of tools. Me as a consumer build my own shopping tool or the, the brands that I interact with on a regular basis basis. The grocery stores, the bike shops and stuff like that are building their tools to deal with me.
- [42:15]
D
Right.
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A
So they'll be. And then we'll let those two battle it out and I'll have more time for biking. Any last thoughts as we wrap up here?
- [42:26]
C
I just want to tell a funny story. As Leah was talking about text to messaging and back again, that even the voice recognition isn't quite where we want to be it to be yet. Me and my girlfriend were trying to make a Christmas list of what ingredients we need to make Christmas dinner. And we were standing there in the kitchen looking at the cupboards. What do we have? What don't we have? And we were trying to use Alexa to make this Christmas list. Alexa add this, Alexa add that. And as you know, I've got quite a strong Scottish accent, as does my girlfriend. Alexa did not quite nail that Christmas list. So we've had to resort to pen and paper to make sure that nothing is missed off it. And I think to me that shows that we are trying to take those steps forward. There's a lot of opportunity for us. But don't be afraid of falling back on your tried and trusted ways and when you need to.
- [43:32]
A
Well, and I think that's a great example of something Leah said earlier too was like you gotta if it's not working, it might just be faster to do it the old fashioned way with a pencil and a post it note and write it down. I really appreciate having you both on the podcast. We got to do this again. I really had a great time today. How does anyone learn more about what you guys do and your thought leadership and anything else?
- [43:59]
D
Well, I know this. We both of us do a lot of writing and presenting both through the framing of public facing presentations that we have at User Testing. So if you go to usertesting.com and go to the blog, we're there obviously we host podcasts with you, alongside you, Nathan. And then also I think another great way is reach out if you are a user testing customer to your customer success manager. I know I talk to one on one with with teams multiple times a day about the things that they are working through and cover a lot of ground in that space. And so yeah, that's another way to reach out.
- [44:52]
A
Amra.
- [44:52]
C
Absolutely.
- [44:53]
A
Any last thoughts there?
- [44:54]
C
No, I think just feel free. You've got our LinkedIn profiles, etc. There as well. If you have any kind of question, big or small, feel free to to jump in, ask away. I know Leah does it as well. We will usually share some of the things that we're doing within that kind of environment as well. But yeah, absolutely. If you're user testing customer, speak to whoever you speak to. User testing and feel free to ask for us.
- [45:24]
A
Perfect. I'll just throw this out there too if anyone's listening to this point in time and congratulations and thank you. But if you have a question, shoot us an email@podcastusertesting.com and we'll start putting together a list. And so in a quarter or two quarters we'll have one of these ask me anything sort of episodes where we can talk about questions that may have bubbled up. So until then, thank you both. Have a great day.
- [45:52]
D
Thank you.
- [45:53]
C
Thanks Nathan.
- [45:56]
A
One second.
- [45:57]
B
Want to keep the conversation going? You can find the show notes@usertesting.com podcast if you haven't already, don't forget to follow us on Apple Podcasts, Spotify, Overcast or Google Play so you never miss an episode. And if you enjoyed today's show, please share it with a friend or leave us a rating and review on Apple podcasts. And until next time, this is Insights Unlocked, an original podcast from User Testing.