Transcript
Alex Smith: Design Leader Insights is brought to you by Fuego UX. Fuego UX is a leading UX research, strategy, and design consultancy. Hi, Prayag. thanks so much for joining the show today.
Prayag Narula: Hey, Alex. It's a pleasure. Thank you for having me.
Alex Smith: Yeah, for sure. And so you're one of the co-founders at Hey Marvin. I think to get started, I'd love to learn a little bit about the journey and starting Hey Marvin and what's, what kind of inspired you to do that and what, what is Hey Marvin to for the people that might not know?
Prayag Narula: Let's start with what is Hey Marvin and then I think it will be better for the context. So here is a research repository. One of the things that we take pride in is it is, we have invested a lot of effort and work and time into building an AI native research repository and we will talk about it. That's a topic of discussion always. So it's an AI native research repository. And the other thing that we do Is we have built specific workflows for our customers to make it easy to bring data in you have a really powerful data analysis tool and then we try we try to make it really easy for people within the product, but also beyond product teams to be able to leverage and discover all of the amazing research that product teams or strategy teams or market teams or, or design teams do.
Alex Smith: Okay, sweet. Yeah, makes sense. And tell, tell us a little bit about how it all got started and then we'll dive into this big AI question that I have.
Prayag Narula: Yeah. Yeah. So My background is research. So in fact, I'm calling in from as you know from the bay area. I’m in fact in the bay area to study research, at the University of California Berkeley, I taught research there well design research there. I even before that I used to work in In Helsinki, I think we talked about it. I used to live in Helsinki, Finland for many years and doing research there. So my background is for years and years. I did both academic and industry design research and um, and then when I left grad school. I'd started a company that was in HCI human computer interaction space. Ultimately pivoted into something completely different and I ran that company for many years, became, you know, very successful tens of millions of dollars raised 100 employees and one of the things that I used to struggle with was our own research, right? It was just a painful process.
Alex Smith: Yeah.
Prayag Narula: And that's kind of where the seeds of Marvin germinated. And so after running that company for, gosh, eight years, I decided to take a break and I wanted to start again, but this time I wanted to start something in a field that I was passionate about, and research design. That's my jam, man. You, you folks, listeners. You know, they are my tribe and so I actually went and recruited my brother, who was actually a designer. He went to one of the top design schools in India. I recruited him to be my co-founder and then we both started Marvin about, I think it started at the end of 2020, beginning of 2021. We got Sam Oldman who is now very famous back then he wasn't as famous he then funded the initial company. I think we talked about that too and then we're off to the races man, and we launched two years ago. And things have been going very well. So that's my kind of story of how Hey Marvin came into existence.
Alex Smith: Tell me about the use case and users for Hey Marvin, who should use this? And then, you know, I got to ask, we hear AI in every product now. Everything ever. So like, how does the AI actually work and what is it doing for these, these ideal users?
Prayag Narula: Great question. So our primary users are product teams, product marketing teams and strategy and pricing teams. So in that order, right? So what we help you do Is essentially centralize all of the product feedback you're receiving across everything, right? Across your user interviews, across your focus group, across like third party, you know usability testing software across You know various teams across like nts survey market research survey across your vendors who are doing some research for you. So we help bring all of that together and we build workflows to help you bring all of that together, right? And so our primary users are anybody who is interested in collecting, organizing, analyzing and sharing what your customers are saying about your product. We especially help with the workflow of it. So if you're conducting user interviews, right, we'll help, we'll turn a zoom into like a full on virtual research lab. If you're collecting a lot of nps data, we can help you analyze all of the qualitative feedback, all of the open ends that you're getting. You know, we work with your vendors, bring all of that data in you're collecting you know even support tickets like we'll help you bring that in to Marvin, right? And then help you analyze that and pull out patterns. And in terms of AI, you know, I, once I explained that people are like I get why AI is going to be kind of important to this, right?
Alex Smith: Sounds like a lot of, a lot of different sources and a lot of, yeah.
Prayag Narula: But I think AI changes the game a lot in multiple different things, right? Think of, people think of AI, and if you think of AI like, in the context of customer research, they think about summarization, right? Like, oh, you can take all of this data and summarize it, which is very valuable. And I think that's like a very surface level way of looking at it, right? What we are interested in, what we do for our customers and our users, our AI engine earns your, it almost acts like a research assistant. It acts like think of it across with the research assistant and a library, right? So when you're connecting, when you're conducting research doing these user interviews, right? They'll come with you It will like take notes for you. For example, it'll like help you collect relevant data when you're collecting surveys it'll kind of give you hey, here's my initial thoughts about this survey. And it'll then once you have collected all of this data it'll do like a very thorough analysis where you should not just summarization. But a full on analysis like here are the patterns that are emerging and here are the sub patterns that are emerging and here is how you should look at this versus something right? I'll even give you a recommendation, right? So yeah, so and then when your team is looking for hey what do we know about this and what have our customers said about this? The system will surface that Feedback. Almost like a librarian's work.
Alex Smith: In two areas, like in AI, you hear about the biases within the machine, but I think even more detrimental to research is actually human bias. Like there's so many biases that affect research in a lot of ways that aren't caught. Like do you see the tool eventually catching bias or do you think AI introduces bias? How do you think about biases?
Prayag Narula: I think one of the biggest disservice we as researchers do is, is trying to essentially remove bias or kind of saying like, oh, we should remove bias. Like I don't think it's possible, especially in research, even in quantitative research. I will go as far as to say, like, unless you are like, we're doing very good, most quantitative research, I see more bias in quantitative research than qualitative. I think what we should do as researchers, what's the right thing to do is acknowledge your problem. And it's not why they should they could be biased in the data and that's that goes for qualitative researchers as well as quantitative researchers. It's an important thing that every, like almost everyone, kind of misses to do. And I feel that's really, really important. AI bias is slightly different and there is like a lot of research. That is happening in the bias that comes from AI. So one of the things that you should absolutely do is to check the sources of your AI output, right? And we're pretty big on that. Like we are fundamental, like you should always cite your sources. And when you're citing those sources, you should absolutely be on the lookout for where the data would be biased, right? Most of the time, nine times out of 10, it's not the AI that's going to be biased. It's going to be the data that the AI is working with.
Alex Smith: What advice would you give to a researcher, you know, that is just starting out in their career? How is this going to change 10 years from now? What do they need to think about long term breaking into UX research?
Prayag Narula: I think the foundational stuff is all really important. And one of the things that a lot of researchers that are coming up or design researchers, or even designers that are coming up that miss out on is the narrative. The communication of it. I think primarily as our job, both as designers as well as researchers, is to build narratives and have people buy into those narratives. You are communicating a point of view to your audience, right?
Alex Smith: Yeah.
Prayag Narula: Find your unique point of view or find not even a unique point of view, find a clear point of view, right? And make sure that you're absolutely crisp on right whether you're doing a research presentation. Whether you're doing building a design or or whether you're like communicating a strategy. All of those should have a very stress point of view and you should in your mind be very successful in what that one could use. And that's one thing that I don't think AI can do really well.
Alex Smith: Yeah.
Prayag Narula: AI cannot and should not be compelling humans.
Alex Smith: Very true. Yeah, I love that. Prayag, thanks so much for joining the show today and sharing these insights.
Prayag Narula: This has been, this has been really good. Thank you. Thank you so much for having Alex.
Alex Smith: For sure.
