Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss

0:00Our goal is to get to a billion people in our audience and then to be able to stratify and know what exactly is this person an expert on. And it might be, you know, even something like sneakers. You have some people who are influencers and kind of early adopters. And if you're able to find that audience and interview them first, the insights are much more valuable. And we can learn across all of the interviews that we do. We build profiles of people as we do more interviews in the platform, and then we can search and find the right person.

0:51Alfred Wahlforss:Okay, today we're sitting down with Alfred Walfors, founder and CEO of Listen Labs. Listen is an AI-first customer research platform that can run thousands of voice interviews simultaneously. You launched about a year ago, and you now serve 20 % of the Fortune 500, including iconic brands like Microsoft, Anthropic, Sweetgreen, NBC, and others. And Constantine, I'm very excited to sit down with you today and talk about market research and how it's getting transformed with AI. Yeah, thank you for having me. Maybe just to get started. So you are building an AI-enabled platform that scales market research.

1:24Alfred Wahlforss:What does that mean? Yeah, so we have this AI agent that can understand your customers better than you can. And the way we do that is by talking to them. So to give you an example, you can ask a question like, how can you improve cursive onboarding? And then Lyssen will create an interview guide, which is an instructions for the agent to make the interviews. And then we have an audience. We have 30 million participants. We can find pretty much anyone from an oncologist to a software engineer. and we'll go and actually talk to them and have hundreds of those interviews and then analyze the data, give you recommendations.

2:00And now the final step that we're just launching in a couple of months is simulation. So after you've done tens of thousands of interviews in the platform, can you predict how your customers will answer questions in the future? Put it another way, as we get closer to AGI, it will be easier to build things, but the hard part will know what to build. And that's what we're building at Listen.

2:22Alfred Wahlforss:Awesome. Do you have any favorite customer stories? Yeah. So Chubbies is one of our customers. Like the shorts brand? Yeah. They've been like one of our early customers. What do they use you for? They use us for everything. So a lot of marketing testing, for testing shirts to understand what products perform well and what doesn't. And one of my favorite examples is they discovered that chest hair interface really poorly with one of the materials they have. So it's like really uncomfortable to wear one of their shirts and they changed the shirt and it became like radically more comfortable. So we saw, you know, the small things to the big things, Manscaped changed their Super Bowl ad with insights from Lesanne.

3:06Alfred Wahlforss:So never heard of that, but I'm not going to ask. That's huge. So you got the men's hair covered. Yes, that's our niche. From shaping to clothing. That's right. Wow. We do other things. Skims is one of our customers' work. You heard it on Trader. Don't know what you're talking about, but I know context clues. So that's awesome. I'd love to understand, as you framed it, as we get closer to this AGI future, one of the questions I have is, you know, traditionally I've always been very skeptical, actually, of surveys because people get paid to take surveys. So you already got a selection bias issue.

3:44Alfred Wahlforss:the things that people say they would do or the way that they describe how they would behave is different from how they actually behave in practice. And so I guess I come from the school of thought where like actual just telemetry in the real world matters so much more than asking people about what they would do. And so I'm curious what you think of that and how you think AI or Listen Labs can help bridge that gap. Yeah. And so we've done a lot of research on this. One of the things we've done with surveys, for example, is we went back to the same person and asked them a multiple choice survey again, and they were radically inconsistent.

4:20So even if you go back to the same person and ask them a survey question in a multiple choice fashion, they're much more inconsistent. But we did the same thing with listen when you actually have to think and you have to really reason through your answer. and then you're much more consistent with at least how you answer the same question. And then we're constantly tracking, for example, with Chubbies, when we test their different charts, we, a couple of months later, look back and see how did that perform with the actual sales data. And I think it depends on different use cases. I agree that A-B test is kind of the holy grail, but in practice, it becomes really difficult to get right because you need a very large volume of users.

5:08And it's really useful to have some kind of input than no input at all. Does Listen do voice to text as in the actual customer who's answering the survey can speak their answer and then you guys transcribe it? Does it also do text to voice? It's a two-way conversation. What does Listen start with and And what does it finish with for the user experience? Yeah, so it's essentially a Zoom call that you have with the agent. So you're on video, and you can also detect their emotions. So that's another way to bridge the gap between what they say and how they actually think and feel. So it looks at your eyes the way you say it.

5:50And that's kind of much closer to how you actually behave in the real world. And have you seen, per Sonja's point, that actually having the person's face and their emotions and their voice and whatnot yields more engagement, truthfulness? Have we been able to have any studies or at least data to point in that direction? Yeah, specifically with advertising, it's a huge benefit because you might have people say on a like it scale, which is like five questions that you click, are you extremely likely to you know, buy this product versus when you you might have very high scores on a survey question like that.

6:34But when someone also reacts very enthusiastically, it's going to be like perform much higher. And we've seen that those ads then perform better in performance marketing, for example, on Meta and LinkedIn. And can you if you're the customer and you commission this and you get all this response, Can you actually click in and if you ever wanted to watch the interview to get that level of granularity? Yeah. So we built a platform around traceability so that for every data point, you can always click and then look at the video or see the quote. So you know that AI is not just hallucinating kind of where it's coming from.

7:12That's awesome.

7:13Alfred Wahlforss:How did you come up with the idea to build this? So my co-founder and I actually built a consumer app. That did what? that went viral. It was called a BeFake. So you could create an AI avatar of yourself. It was an early version of the ChatGPT images. And you could fine tune stable diffusion and put yourself in that world. And that ended up going super viral. And overnight, we had 20 ,000 users. And we were also kind of experimenting with different ways of using AI. So we built this AI interview for ourselves because we had a bunch of questions of how we had We had a ton of churn, so we wanted to understand why, how they thought about our positioning, different use cases.

7:57And it was really useful for us.

7:58Alfred Wahlforss:Yeah. And that's how we got started. Maybe just walk us through how the industry is changing before and after Listen Labs. Like, historically, let's say you're somebody with an app with 20 ,000 users. You don't understand how users are using the app, what they want next, why they're churning. Historically, how did people go about doing that? Yeah. So what we discovered was that there are these survey tools that are pretty old school, like Qualtrics. But then there's also this very large services industry, because it becomes harder and harder, especially if you want to do market research, where you want to talk to your prospective customers, not your current customers.

8:36It becomes harder and harder to do that as you scale. So that's a multi-billion dollar industry. And what they do is come up with questions to ask, which is an academic subject in and of itself. It's actually really hard to know, like, how do you ask questions to your point that get to how someone actually will behave? You can't just ask, like, how much are you willing to pay for this? There's different methodologies that work better than others. To finding the audience, how do you source the participants? to then analyze hundreds of these calls. And in the traditional industries like CPG, even in Microsoft, they spend tens of millions of dollars on focus groups to bring people in a room and interview them.

9:23And we can help speed that up much faster.

9:26Alfred Wahlforss:Okay, so that's the old world of how this used to be done. Maybe describe the new world. And then it seems to me that there were obvious kind of first-order benefits. It's like it's probably much more scalable, probably much more cost effective, but there's probably also less obvious benefits. Maybe just talk about some of those benefits of, you know, what is it like when you actually do AI first market or customer research? Yeah, so most decisions that gets made are not based on the customer input. Right. And the reason for that is it's just a lot of friction to even talk to customers. so when you can lower the barriers of talking to customers you end up making much smarter decisions so the speed advantage is actually huge for us you can get input within five minutes from real people and it's a really magical experience when you see hundreds of people populate in your like interview and so that's one thing and because it's asynchronous it's also much more affordable So you can pay people much less than if you would have to run like synchronous interviews.

10:36So actually, that's an interesting thing that people often ask us, like, do people even like being interviewed by an AI? And the objective answer is yes, because you can pay them less to talk to an AI than to talk to an actual interviewer. Why is that? I think it's mostly because it's asynchronous and people are very busy. But then also what we found. Yeah, lower pressure. You can kind of go on and off. And we've also found that people are more honest talking to an AI. We've had people really open up. It's a very therapeutic experience because it's a nonjudgmental entity that's really interested in you.

11:18And we can also have sensitive conversations like interviewing kids, how they react to different products. And so I think that's another advantage as well, that people can be brutally honest. talking to the AI. Hmm.

11:32Alfred Wahlforss:Okay. So historically, for example, if I was doing research on the kids' market, very, very hard to access that market. Is that regulatory thing? Is it a scheduling thing? Yeah. You need parental consent. Kids are really busy. They go to school. They have extracurricular activities. How do you find time with them? You need to find the right kind of kids. One of the things we realized is the audience is extremely important. And And that's actually where we spend 80 % of our engineering resources. Every company is driven by a power line customer segmentation. So even a product like Sweetgreen, which you would think is for everyone, the right audience is typically urban, high household income, mostly female.

12:17And by the way, they need to know what seed oils are, which only like 1 % of the population does. And then you find that some people go to Sweetgreen every single day, and that's 80 % of their revenue. So if you can find that segment, the research is so much more actionable. Yeah, there's probably a network effect to it as well, where when you get a certain scale and people use it, you can access the same kind of person that otherwise might be really difficult to access. Or maybe it's a scale economy. Something along the lines of accessing those really, really specific people that are really valuable for the type of product that you're trying to introduce.

12:56Yeah. Yeah, it's really about, you know, our goal is to get to a billion people in our audience and then to be able to stratify and know what exactly is this person an expert on. And it might be, you know, even something like sneakers. You have some people who are influencers and kind of early adopters. And if you're able to find that audience and interview them first, the insights are much more valuable. and we can learn across all of the interviews that we do. So we build profiles of people as we do more interviews in the platform and then we can search and find the right person. So someone might say in a totally unrelated interview, I'm a total sneakerhead and you can keep that in the database on that person and then when Nike or what have you is launching the new product line, you can offer that person up.

13:47That's right. That's amazing. And that was not possible to do before because it was usually like separate entities and it would be a very manual process where you would have an email list and you would just spam email. I've been on the receiving end of those. Yeah, they're terrible. And one of the problems with that is that you then need to have an extensive screening process. So you have something called an incidence rate, which can be 10%, which means only one in 10 people gets qualified to even take the interview. And that causes significant churn on these databases because it's really annoying to be screened out 10 times to even get paid the first time.

14:29Alfred Wahlforss:Why do these brands even need you for access? Let's take Sweetgreen. Sweetgreen knows who that 80 % is. Can't they just reach them? Don't they have direct relationships with them already? Yeah, so they can. And we do that as well. We connect to their CRM and they can send that out. But then the really interesting part is how do you talk to prospective customers, people who also may not be kind of current power law users? And how do you compare those two? And then also what we found is that the CRM is typically really unorganized. And sometimes there's also like regulatory issues. If you're at Google, you can't just send emails to people who use Gmail.

15:14And it gets much easier to use an external third party. And you run the risk of spam, which can get you totally blocked. I have seen that at some of our companies over the years where you do outbound and then eventually you're in the Google filter. And next thing you know, you're in Microsoft purgatory. I guess going through you guys, you don't have to deal with that. Yeah, exactly. It's cool.

15:35Alfred Wahlforss:What does this mean for the McKinsey's or whoever's of the world that are, you know, the people that are building the hundred slide decks that, you know, reach 3 ,000 people to reach some set of insights? Didn't you do that, Sonia? Wasn't that a former life? You know, no, Constantine, but I'm glad that that's what you think of me. Isn't that what banking does? We hired consultants. I used to hire these people. Got it, got it. So I was a layer on top of the layer on top of the layer. Okay, got it, good. I was even more redundant. But what does it mean for all these people? Do they still have a role to play in this new future?

16:11Yeah, I think AI is changing all roles very quickly. and we work a lot with Bain, for example. So they use us to speed up their traditional processes. And I think they still have a role to play. I think traditional services and being able to then implement these changes is still extremely valuable. But a lot of margins are going to drop and you have to make sure you kind of unbundle a lot of your services to maybe allow for AI agents to help solve some of the problems that you would go to traditional consulting firms before? Maybe I'm an optimist here, but why wouldn't it be more? Why wouldn't I, if I'm running a business, say, oh, great, I want to find five new areas to expand to now that I have AI and these tools, and I will pay you, Bain, or what have you, the same dollars you use, listen, and just explore those new areas and tell me where to where to build.

17:13Is that overly optimistic? No, I think it's one of those areas where the ceiling is very high. You can kind of learn more about your customers and you can build more things. And so I think you're right. I was thinking the chest hair shirt thing. I'm glad that 20 minutes in you're still thinking about chest hair. There's so many little things that I'd love to tell the companies that I am a consumer of, Like the smallest little thing. Even the way they lace these shoes, I'd love to give that feedback.

17:45Alfred Wahlforss:This is why you're a venture capitalist. Details. Details. We hope to live in a world that finally works the way people want. That would be great. Please. Are you seeing any price and compression already hit the industry? Like I would imagine if I am Bain's customer, I'm thinking, well, you're able to do this survey a lot more efficiently now with AI than before AI. Who is getting the benefit of that economic surplus? So because you're able to do it faster, I would argue you should be able to charge more for it. And is that what's actually playing out? We have done some studies where we're able to charge hundreds of thousands of dollars to speak to 20 doctors across eight countries.

18:27So maybe over the long term, like the individual interview will become more affordable. but I think you'll be doing kind of two orders of magnitude more of research. And I think what's really exciting is also simulation which is something we're building now where you're able to unlock the 99 % of use cases where you would never have time to talk to real people. I think that's so awesome in part because there are so many areas where they don't even listen to the customer. Like medicine, there are a million little problems with the medical system. I hear about it all the time. And these are, you know, their doctors are busy, important people, but it feels like the companies haven't even invested the time in figuring out where all those paper cuts are.

19:16And the doctors are really busy. So they're not going to go schedule an appointment and have some long conversation and meet with some group. But if they could do it at any time, like in an app on their phone as part of the normal homepage app and give feedback on their EHR or something in the operating room or something along those lines. That seems like a life-saving use case for a listener over time. Yeah. I think what I'm really excited about as well is taking all those small things and then telling another agent to go and solve that problem. And we're getting pulled in this direction by some of our customers where they will have a churn interview and then they will connect them.

19:59If you find a bug, for example, they'll connect that to another coding agent to go and solve the problem. That's cool.

20:05Alfred Wahlforss:Let's talk about generative agent simulation. It seems like the entire industry has gone from market research 1.0, call 100 people one by one, collate them manually, to market research 2.0, AI native, where AI designs the question track, is able to talk to thousands of people simultaneously, synthesize the answers. It seems like we're maybe moving to market research 3.0 with generative agent simulation. What do you make of that? And, you know, I both see the dream of it. I see how synthetic data has changed, for example, self-driving cars. And then I also am inherently skeptical of it. Like, is a bunch of synthetic data just remixing what's already in the pre-training sets?

20:47Alfred Wahlforss:And Are you actually learning anything useful or with alpha there? And so I'd love to hear your take on it and how you guys are taking on the 3.0. And maybe what is it, too, to start? Yeah. So the way we are building simulation is by interviewing a single person. Say if I interview Konstantin for one hour, I can probably start to predict your preferences to some degree. Fascinating insights about chest hair.

21:15And it turns out that LMs are quite good at this as well. So you can essentially try to feed in as much information as possible on a single individual. And then in some cases, we're able to get 95 % accuracy to predict how they will answer certain questions. Now, the problem becomes things are changing all the time. And chaos theory tells us it's really hard to predict the future. Otherwise, we would be on Wall Street and making a ton of money. So how we think about it is you need to hydrate these audiences. And the way we do that is by all of the interviews that are running through Listen. So we have a very strong network effect.

21:56We've done a million interviews so far, and that's grown exponentially since we reported that number. Wow. And we're able to train audiences on those interviews. So you can imagine a future where you can ask a question and listen, like, how do software engineers think about cloud code? And then listen will say, well, I already talked to a thousand software engineers this week. Let me predict how they're going to answer that question. But the tricky part is knowing what things can you answer and what can't you answer. And how do you do that? Yeah. We try to be very explicit to the model of what is the domain of knowledge they have.

22:36and then see how much can you expand that domain. That's kind of the fundamental idea. And we can backtest how well the simulation works with what's in our training data set. So we remove one of the questions. Yeah. And then see, like, OK, how accurately did you predict that? And then you can add in nonsensical things, like what's the name of their dog or something like that. And then you can say, like, is the model able to understand that you can't predict that. That's really cool.

23:08Alfred Wahlforss:What sorts of things are you finding that you can predict well versus can't? One of the most useful things is message testing. So that's the idea of what's the tagline on the billboard? Or I was actually using it this weekend. So I have created a panel of our customer base and I had to come up with the title for a talk at a conference. And it's like a small thing, but it actually does matter because it will increase conversion if people show up. And I came up with 100 different titles for my talk and inputted that into our simulation. And then the top talk was like twice better than the next one.

23:52And I don't know if it's correct, but it certainly felt correct. And it was really helpful to have guidance in making that decision. And I also think like even if it's wrong, it's just nice to have some help in making a decision. It's also nice to outsource your decisions.