Listen Labs raises $27M Series A led by Sequoia to automate AI-powered customer research at scale

in the studio. Let's bring him in. We got Alfred from Listen Labs announcing and we got his co-founder. Boom. And they got the they got the jackets. They're not they're not quite uh color coordinated. I loveord dressing up for the show. We always appreciate people dress up when they coordinate their outfits.

Uh how you guys doing? Doing well. Yeah, exciting. Uh can you give us a little overview of the the history of the company? Introduce yourselves, tell us about the round, uh give us the basics. For sure. So, uh listen is an AI customer researcher that can find and interview thousands of users and tell you what they want.

And today we're or yesterday we announced our series A 27 million in total raised led by Sequoia, Conviction and Pair. And yeah, we work with some amazing customers like Microsoft, Canva and Chubbies. Mhm. And the core concept of what we do is like every company wants to be customer obsessed.

So imagine you wanted to take that to the most like fullest extent and that's basically you would talk to uh every customer you could and you talk to them try to understand exactly who they are and what they think about and you'd find a way to synthesize all of those conversations into a clear understanding of their behavior.

And you wouldn't do this just once. You do it all the time. Uh, every time you make a change to your product or service or marketing, and that's what we do. Cool. I can imagine a ton of different ways to get customer insights out of all the data.

Just using Microsoft as an example, like there's probably 1 million tickets in some database that's just a bunch of text that hasn't been processed. You, you know, run an LLM over that. That makes sense. It sounds like you're doing something more advanced where you're actually calling people up.

Maybe there's a a screen recording and interaction and some questions back and forth. Is that all AIdriven? Walk me through the case study, I guess, with Microsoft or anyone else you want to you want to chat about. Yeah. Yeah. Yeah, for sure.

So, it's an AI that can have a one-on-one conversation and then you can have hundreds of those conversations in parallel. And what we found is that kind of you have to reach enough people to really understand what people want. So if you speak to 10 10 of them, most of them won't have much interesting things to say.

And the second thing is the the synthesis. If you have like hundreds of conversations, finding the signal over the noise is is you know AI is amazing is amazing at that. Sure. Um and you know AI is also the ultimate listener. It's more empathetic than than myself.

And as a case study like one of my favorite ones is Chubbies.

So um they recently launched a new product based on learnings that they made from listen that is growing really really fast um and it's a little bit weird but it was an AI or it was interviewing kids using uh listen interesting and um they were much more honest to our AI versus like speaking to a stranger um and it enabled them to learn a lot about like how the how to make their shorts more comfortable and basically created a new product with a new liner in the shorts that um just felt much better for the kids and now uh that product is growing really fast.

Do do you believe there's uh sort of a customer love flywheel in a sense where the more uh someone likes a product the more inclined they are to want to talk about how to make it better.

and is part of what you guys are doing helping to like accelerate that where a product could have some customers but uh it's hard to really get scaled feedback because nobody really loves the product enough like I I get emails all the time I'll try a SAS product and I'll churn and then they'll reach out to me being like hey you know I I uh you know can can we get feedback and usually I'm not super um inclined uh to to provide feedback sometimes I do if I feel like it's just off but how do you think about sort of like building that sort of customer feedback flywheel and converting it into customer love.

Yeah. Sorry. Yeah.

I mean I think what is interesting is the you know we we have this at bigger companies right um what you actually you know you have a lot of feedback as you said before but um actually talking to real customers is is actually pretty complex right it's at the bigger companies they typically outsource it to to external agencies because there's a lot of hoops they have to go through and you know they charge like 100k and then they schedule like 20 interviews and and do that.

Um, and you know what we enable them to do is instead of using using these external agencies, they can just use us, right? It's cheaper, it's faster, um, it's better because can reach way more people.

And yeah, I think that's an exciting opportunity also for like in general for, you know, AI founders to, you know, if you can replace one of those like external um, you know, external partners. basically you just just flip a switch and um you know you sell an outcome instead of just a software.

when you talk to um when you talk to maybe bigger older companies are you surprised at how they sometimes don't have a process in place to talk to customers like I feel like it's part of Silicon Valley like ethos lore you know PG is just sort of like banging you know founders over the head saying like talk to your customers talk to your customers and so it's it's a part of the culture here but um I I imagine there's some companies that you're maybe in a sales process with that don't have any infrastructure uh uh or or very little infra infrastructure in place uh rel relative to how sort of like scaled they are.

Yeah. I mean a lot of the products and services out there are kind of uh they they suck. Uh you know and and it's it's it's so often that you use um a product and you think like okay what were they even thinking? Like clearly they're not keeping us in mind here.

And for some of these large companies, um, you know, they do want to invest in talking to users, but it's just so complex because there's a lot of bureaucracy even to reach out, um, to a user.

You kind of have to go through legal and and it becomes too complicated and that's why they have to use these consulting firms to kind of go through it. But we've been able to kind of streamline the process. Can you talk about uh how these interviews happen today and how they'll evolve?

I can imagine chatbot interaction to begin with, but at the like the the voice interfaces are pretty close, virtual avatars are getting pretty close. Uh what's the product like today? Where do you see it going? Yeah.

So we F and I we actually like started a company by virtue of you know trying to solve the problem for ourselves and um we had this AI consumer app that had 20,000 downloads in one day and it really blew up out of nowhere.

We were running with expensive GPUs spending like $1,000 per hour and it was our personal credit cards attached and like the users were charging really fast. So we're like, okay, how do we actually solve this problem? And then we we built a very simple prototype.

It was a chatbot that could just talk to all the users and summarize what they wanted. And we found things that were really useful like which template should we add? How should we fix our onboarding? And now we have built out this kind of entire um stack where it's essentially three steps.

It's first interviewing and we do that over video and have like the video understanding both of the emotional like experience and the what they do on the screen and then it's finding the people.

So we actually allow you to kind of find all the customers through a database of millions of users and then of course analysis and the ceiling for analysis is really high. you can hire Mckenzie and they'll charge you a million bucks to give a a PowerPoint deck and uh yeah, we're we're working on that.

That makes a ton of sense. How how many what are some kind of edge cases or or companies that you you would expect to start using listen that that maybe are non-traditional? Like I my immediate thought is that we should throw up like you know basically a number that you can just call and uh give feedback on the show.

uh which you know we we're live uh 15 hours a week so it's hard to find time for phone calls but it would be awesome if people could call in and be like hey like talk more about this or you know uh things like that but um how uh yeah do do you see this type of um user research expanding into industries that maybe just aren't customers of these different consulting firms today just because of the the costs?

Yeah, exactly. I think there's a huge new market you can uh open up for, you know, smaller companies or companies that couldn't traditionally afford or couldn't even honestly wait for 12 weeks until you get like this report back. Um so I think that's very exciting.

But if you take a step back and think about you know what makes a great company um and you can even go back to YC of you know what they think is like we mentioned it before right code and talk to talk to users and you know that's really the core but those two parts you know we have the writing the code which now AI agents are doing more and more you know it's getting easier uh to to build things so what becomes more important is actually building the right thing and kind of having the feed uh feedback talking to the customers so that's what we're building And you know, it's almost scary, but maybe in the future that's that's what we're going to do.

Like that's maybe the last thing that humans will do. Like kind of telling the AI what to do, uh, what to build, being the taste makers which podcasts to listen to. Yeah. Yeah. Just wire it directly into cursor or Devon and then it just takes the feedback and immediately implements the change.

No, it is it is an interesting scenario to think about where where listen is directly integrated into the into the basically developer tools where somebody can say like hey I want a feature to do this and then it just immediately you know gets built. Yeah, pretty awesome.

I'm sure you guys are thinking about on the on the post interview analysis side. I can imagine that AI is very good at aggregating sentiment and tagging things essentially creating like the top 10 requests, the word clouds.

Um, but uh have you found LLMs or any of the modern AI tooling being useful in finding like the diamonds in in the rough? Like uh everyone's telling you to build a faster horse. there's one person who's clocked it and says, "No, you got to go into the automobile for your product.

" Uh, and and you're able to dig that out with uh modern technology. Yeah. Um, so we we have like a module that finds surprising statements and outlier sentiment. And I think there's also interesting things around kind of building a profile of understanding who this user is and if they give good advice or not.

And we actually have like a quality score of each participant. So you shouldn't listen to all feedback. Um, and as we've done more interviews, we've done now more than 300,000, we can kind of build a elite set of taste makers essentially that you can reach out to that tend to give like great advice. That makes sense.

Uh, how how did the round how did the round come together? Obviously got basically the who's who of uh venture in. Uh was anything surprising? Was it uh uh you know did did you guys go go out to run a process or did did people come to you?

Uh well it's a very competitive fundraising market for the for the business today. Um but you know we're we're very uh lucky to work with exceptional partners and Brian Shrier at Sequoia. He also led the first investment in Qualrix which is the biggest outcome in in the customer feedback world.

Um and then it's amazing working with Mike Bernal at Conviction. Yep.

um who's done yeah the best deals uh when uh when listen has as big an outcome as Qualrix what pro sports team are you guys gonna you guys going to buy yeah I actually spoke to I'm from Sweden I spoke to Brian Smith at Qualrix and the first thing he said like oh yeah you you uh I just bought a hockey team you guys you guys love hockey right so maybe I think table tennis that would be table tennis table tennis what is the most popular sport in Sweden right now.

Yeah. Um maybe chess boxing. No, table tennis. I don't know. Table tennis. Table tennis. It is very popular. We we went uh we almost won the Olympics. China. Yeah. Oh, that's rough. It happens. It happens. Um awesome guys. Well, congratulations. Very exciting.

And uh let us know when we when it's ready for for podcasting, you know. Prime time. We'll roll it out. Uh take care. We'll talk soon. Bye. Right. See you. Great. And we got an ad for EightLe. Go to eightsleep. com/tbpn. Get a pod4 Ultra. They got a 30 night free trial, a 5year warranty, free returns, free shipping.

Go check it out. Oh, 100. I close. Uh I don't know how this is possible. I think uh autopilot was doing the work. Uh my little one was uh throwing up last night. Um, it's rough, but uh so credit to uh my wife, real real MVP uh for uh getting uh her taken care of. But um who we got next?

Uh we got Eric from Free Form coming into the studio. Um talking about uh manufacturing going back into hard tech. Thursday tends to be a little bit leaning heavy on the hard tech. Every once in a while we got Delian kicking it off and then we try and bring in some robotics, some manufacturing founders.

Always an interesting discussion to have with those folks. Um we are uh talking to Formeck as well. They're doing robotics and uh I'm sure we'll talk to many more. Although we've hit a ton of the companies at this point. When you do 20 interviews a week,