Knowing What Your Customers Want, All the Time: Listen Labs' Alfred Wahlforss
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.
我们的目标是让受访群体达到十亿人，然后能够对其进行分层，精确了解每个人在哪些领域是专家。
And it might be, you know, even something like sneakers, you have some people who are influencers and kind of early adopters.
比如运动鞋这类产品，有些人每天都穿一双特定的鞋，而有些人则完全不在乎穿什么牌子。
And if you're able to find that audience and interview them first, the insights are much more valuable.
如果你能先找到那批受众并访谈他们，得到的洞察会精准得多。
And we can learn across all of the interviews that we do.
我们还能从平台上所做的所有访谈中不断学习。
We build profiles of people as we do more interviews in the platform and then we can search and find the right person.
随着平台上的访谈越做越多，我们会为每个人建立画像，从而推荐更合适的受访者。
Okay, today we're sitting down with Alfred Walforce, founder and CEO of Listen Labs.
好的，今天我们请到了 Alfred Wahlforss，他是 Listen Labs 的创始人兼 CEO。
Listen is an AI first customer research platform that can run thousands of voice interviews simultaneously.
Listen 是一个 AI 优先的客户调研平台，能同时进行数千场语音访谈。
You launched about a year ago and you now serve 20% of the Fortune 500 including iconic brands like Microsoft, Anthropic, Sweet Green, NBC and others.
你们大约一年前正式上线，目前已服务于 Fortune 500 中的 20%，包括 Microsoft、Anthropic、Sweet Green、NBC 等知名品牌。
Um, and Constantine are very very excited to sit down with you today and talk about market research and how it's getting uh transformed with AI.
嗯，今天我和 Konstantine 都非常期待与你坐下来聊聊市场调研，以及 AI 正在如何改变这个领域。
Yeah, thank you for having me.
是的，谢谢你的邀请。
Maybe just to get started, so you are building an AI enabled platform that scales market research.
先从最基础的开始，你们正在打造一个 AI 赋能的平台来规模化市场调研。
What does that mean?
这到底意味着什么？
Yeah.
是的。
So, we have this AI agent that can understand your customers better than you can.
我们有一个 AI 智能体，它能比你自己更深入地理解你的客户。
Uh, and the way we do that is by talking to them.
我们的做法很简单，就是直接和客户对话。
So, to give you an example, you can ask a question like how can you improve curses on boarding and then listen will create an interview guide um which is an instructions for the agent to make the interviews and then we have an audience.
举个例子，你可以问一个问题，比如怎样改善 Cursor 的用户体验，然后我们会在 30 分钟内从三千名软件工程师那里收集到具体的洞察。
We have 30 million participants.
我们有 3000 万参与者。
We can find pretty much anyone from an encologist 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.
从肿瘤科医生到软件工程师，几乎任何人我们都能找到，并在几分钟内给出初步的洞察报告。
And now the final step that we're just launching in a couple months is simulation.
我们即将推出的最后一步是模拟，几个月后就会上线。
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?
当你在平台上积累了数万场访谈之后，是否能预测消费者对未来产品或广告的反应？
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.
换个角度来说，随着我们越来越接近 AGI，构建产品会越来越容易，但真正稀缺的是知道该构建什么。
Awesome.
太棒了。
Do you have any favorite customer stories?
你有没有特别喜欢的客户案例？
Um, yeah.
嗯，有的。
So, Chubbies is one of our customers.
Chubbies 是我们的客户之一。
They've been
他们一直
Yeah, they've been like one of our early customers.
是的，他们算是我们最早的客户之一了。
What they use you for?
他们用你们做什么？
They use us for everything.
他们几乎什么都用我们做。
So, a lot of marketing testing for testing shirts to understand um what products perform well and what doesn't.
大量的营销测试，比如测试哪些衬衫产品表现好、哪些不好。
And one of my favorite examples is they discovered that chest hair interface really poorly with one of the materials they have.
我最喜欢的一个案例是，他们发现胸毛和某款面料严重不相容。
So, it's like really uncomfortable to wear one of their shirts and they changed the shirt and it became like radically more comfort comfortable.
穿上那件衬衫真的很不舒服，他们改良了面料之后，舒适度大幅提升。
Um so, we saw you know the small things to the big things.
嗯，从细节问题到大方向问题，我们都见过。
Manscaped um changed their Super Bowl ad with insights from from Leen.
Manscaped 就凭借来自 Listen 的洞察，修改了他们的超级碗广告。
So
所以
never heard of that, but I'm not going to ask.
从没听说过，但我不打算深究。
Uh
嗯
that's huge.
厉害了。
So you you got the men's hair market covered.
好吧，男士毛发市场你们全包了。
Yes, that's our niche.
是的，这是我们的细分市场。
From shaping to clothing.
从体毛护理到服装。
That's right.
没错。
Wow.
哇。
We do other things.
我们还做别的。
Skims is one of our customers.
Skims 也是我们的客户。
You on trading.
你在培训上。
Don't know what you're talking about, but I can I know context clues.
不知道你在说什么，但我能从上下文推断出来。
So that's awesome.
太厉害了。
Um I'd love to understand as and as you framed it as we get closer to this AGI future.
嗯，我很想了解一下，按你刚才说的，随着我们越来越接近 AGI 的未来，
Um one of the questions I have is you know traditionally I've always been very skeptical actually of surveys because um people get paid to take surveys so you already got a selection bias issue.
嗯，我有个问题，你知道传统上我一直对调查问卷这类工具很持怀疑态度。
Um the things that people say they would do uh or the the way that they describe how they would behave is different from how they actually behave in practice.
嗯，人们说会怎么做，或者他们描述自己行为的方式，往往和他们实际的行为不一致。
And so I guess I come from the school of thoughts where like actual just telemetry in the real world matters so much more than asking people about what they would do.
所以我更倾向于认为真实的遥测数据要比问人们他们会怎么做有用得多。
And so I'm I'm curious what you think of that and how you think um AI or listen labs can help bridge that gap.
我很好奇，你怎么看待这个问题，以及 AI 或 Listen Labs 能如何弥合这个差距？
Yeah.
是的。
And so we've done a lot of research on this.
我们在这方面做了大量研究。
Um 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 like radically inconsistent.
比如我们在调查问卷上做过一个实验，对同一批人在相邻几个月里反复问同样的问题。
So 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.
结果发现，即使是同一个人，在不同时间回答同一道问卷题，答案也会不一样。
Um but we did the same thing with listen when you have actually have to think and you have to really reason through your answer and then you're much more consistent um with at least how you answer the same question.
但我们用 Listen 做了同样的测试，当你真正需要思考并口头回答一个问题时，一致性会高得多。
And then we're constantly tracking.
而且我们会持续追踪。
For example, with with chubbies when we test their different charts, we couple of months later look back and see how did that perform with the actual sales data.
比如和 Chubbies 合作时，我们测试了他们不同的短裤，过了几个月再回头看，实际销售数据和预测的匹配度如何。
And I think it depends on different use cases.
这取决于具体场景。
I agree that AB test is kind of the the holy grail, but in practice it becomes really difficult to get right because you need a very large volume of users.
AB 测试确实是最理想的方法，但实操起来很难做对，因为你需要非常大的用户量。
Um, and it's it's really useful to have some kind of input than no input at all.
嗯，而且有某种输入总比完全没有输入要好得多。
Does listen do uh voice to text as in the actual customer who's answering the survey can speak their answer and then you guys transcribe it?
Listen 会做语音转文字，也就是让真实客户语音作答吗？
Does it also do text to voice as in it's a two-way conversation?
它也做文字转语音，也就是说这是双向对话吗？
What does listen start with and what does it finish with for the user experience?
Listen 从哪里开始、到哪里结束，对用户来说体验是什么样的？
Yeah, so it's essentially a Zoom call that you have with the agent.
是的，本质上就是你和 AI 智能体进行的一场 Zoom 通话。
So you're on video and you can also detect their emotions.
你们是视频连线的，也能识别受访者的情绪。
So that's another way to bridge the gap between what they say and how they actually think and feel.
这是另一种弥合他们说的和他们实际感受之间差距的方式。
So it looks at your eyes the way you say it.
它会观察你的眼神，以及你说话的方式。
Um, and that's
嗯，而且这
kind of much closer to how you actually behave in the real world.
更接近你在现实世界中的真实行为。
And have you seen persona's point that actually having the person's face and their emotions and their voice and whatnot yields more uh engagement, truthfulness?
你有没有见过有观点认为，当你能看到对方的脸和肢体语言时，人们实际上更愿意开口？
have have we been able to have any studies or or at least data to point in that direction?
我们有没有研究或数据支撑这个方向？
Yeah, specifically with advertising um it's a huge benefit because you might have people say on a like it scale which is like a you know
是的，在广告领域尤其如此，优势非常明显，因为你可能会问受访者
five questions that you click are you extremely likely to you know buy this product.
五道问题，比如你极有可能购买这个产品吗，诸如此类的。
um versus when you you might have very high scores on a survey question like that, but when someone also reacts very enthusiastically, it's going to be like perform
嗯，但当你在调查问卷里看到这类问题时，可能评分都很高，然而广告的实际表现却差得多。
much higher.
高得多。
Uh and we've seen that those ads then perform better in performance marketing, for example, on on Meta and and LinkedIn.
我们发现，那些经过 Listen 测试的广告，在效果营销上的表现更好。
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?
如果你是客户，委托了这个调研并拿到所有数据，你有没有办法追溯哪条洞察来自哪个具体访谈？
Yeah.
是的。
So we built the platform around traceability so that for every data point you can always click and then look at the video or see the quote.
我们把整个平台建立在可追溯性上，每一个数据点你都可以一路追溯到原始的访谈。
Um so you know that AI is not just hallucinating kind of where it's coming from.
嗯，这样你就知道 AI 并不是在凭空捏造，能看到这个结论是从哪里来的。
That's awesome.
太棒了。
Makes sense.
有道理。
How did you come up with the idea to build this?
你是怎么想到要做这件事的？
Um, so my co-founder and I actually built a consumer app u and that went viral.
嗯，我和联合创始人其实之前做了一个 To C 的应用，结果火了。
It was called um a be fake.
它叫做 Be Fake。
So you could create an AI avatar of yourself.
你可以用它生成一个自己的 AI 虚拟形象。
It was an early version of uh the chatbt images and you could fine-tune stable diffusion and put yourself in that world.
那是早期版本的 ChatGPT 图像功能，你可以微调 Stable Diffusion，把自己放进各种场景里。
And that ended up going super viral and overnight we had 20,000 users.
结果一夜爆火，用户一下子涨到了两万人。
And we were also kind of experimenting with different ways of using AI.
我们当时也在尝试用 AI 做各种各样的事情。
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.
于是我们给自己做了一个 AI 访谈工具，因为有大量问题想了解。
So we wanted to understand why um how they thought about our positioning different use cases and it was really useful for
我们想搞清楚，这两万用户是怎么看待我们产品定位的，他们和我们的想法有什么不同。
Yeah.
是的。
And that's how we got started.
就是这样起步的。
Maybe just walk us through how the how the industry is changing before and after listen labs.
能给我们介绍一下这个行业在此之前和之后的变化吗？
Like historically let's say you're somebody with an app with 20,000 users.
比如过去，假设你有个 App，有两万用户。
You don't understand how users are using the app, what they want next, why they're turnurning.
你不了解用户怎么用这个 App，他们接下来想要什么，为什么他们会流失。
historically, how how did people go about doing that?
以前人们是怎么解决这个问题的？