Venture Europe: Entrepreneurship | Technology | Venture Capital | Eu/Acc
00:00:00 · Casper Henningsen, Calin Fabri
[00:00:00] S0: The world knows so much, but understands so little. [00:00:03] S1: What does that mean? [00:00:04] S0: I think one of the unlocks of not only knowing, but understanding is where you go a step in it and to the roots and starts to connect with a true understanding of why is the world as it is. [00:00:17] S1: How do you guys help, let's say, big brands like Coca Cola, Heineken to start with why and not with what? [00:00:23] S0: I think you need both. The problem the enterprise has is that if you go for a deep contextual kind extraction of knowledge, that takes a lot of time and is a costly process. On the other hand, surveys and quantitative methods is very fast, but it only gives you a very shallow and surface level view on the world. Market research, a forty year old category, is built for an industry and an enterprise that no longer exists. So what we did were we looked at the possibilities of AI to essentially orchestra the entire research workflow, interviewing real people, real human beings, writing code would interact with customer needs autonomously. And when we run simulations in pilots now in our models, you can easily run thousands of loops over durations of hours. Where do you
00:01:14 · Calin Fabri, Casper Henningsen
[00:01:14] S1: see the synthetic data? Synthetic is a huge opportunity. I'm still very cautious. This episode is brought to you by Light. Light is the AI native accounting and finance platform built for fast growing multi entity businesses and used by the likes of Legora, Lovable, and Fuse Energy among others. Scaling across multiple entities usually means an ERP nobody loves, plus a Frankenstack of fine end tools duct taped together. Light replaces all of it. Retire the Frankenstack. Find Light at the link in the description or visit them at l I g dot That is l I g h h dot inc. Absolutely. And and as you mentioned, you're now the CEO of GetWhy. You've been at it for almost ten years. You're now like the global leader in AI moderated research. You've raised in total around 60,000,000. I also think like we have there like a small sign, you know, that kinda shows a little bit and part of the pictures that that you guys did. And also you have clients like Coca Cola, Heineken, and Unilever. Do you want to tell us what GetWhy does? And maybe within the consumer research, like, what has it changed in the age of AI? [00:02:48] S0: Yeah, starting at the the biggest kind of like avenue of my curiosity, there is just the fact that the world knows so much, but understands so little. [00:03:02] S1: What does that mean?
00:03:02 · Calin Fabri, Casper Henningsen
[00:03:02] S1: What does that mean? [00:03:03] S0: There's so much going on. There's so much getting exposed to us with a lot of what's. This is happening. This is happening. To an agree and in a tempo I have never been exposed to before. And yet, we keep repeating. We keep accelerating. Going in directions that are necessarily a good force. And I think one of the unlocks of not only knowing, but understanding is where you go a step in it, and to the roots, and starts to connect with the true understanding of why is the world as it is. The quillient for me ten years ago starting GetWhy, it was like funny realization that companies can know so much, but understand so little. And why is it that all the young companies that doesn't have all that data, beats the established on very few truthful insights? I became obsessed with the idea of closing the distance to the why. [00:04:19] S1: I mean, mean, it's in the name. GetWhy. Yeah. Like, the insights. You know, I think also it's like Simon Simon Simon Sinek. Also, he has, like, a very famous and popular TED talk, start with why and the importance of why. So how how do you guys help, let's say, big brands like Coca Cola, Heineken to start with why and not with what? [00:04:44] S0: I think you need both. But the idea were that if understanding, true understanding of the why is a scarred resource within the enterprise, what would happen if you could scale empathy and true understanding and the democratized a democratized leader. [00:05:07] S1: What does that mean?
00:05:08 · Casper Henningsen
[00:05:08] S0: That that essentially means like if you go to an enterprise today, it's built in structures and hierarchies and only very few people knows certain stuff about their markets and customers. True stuff. There's a lot of data and there's a lot of what's coming from analytics, transactional brand, category performance, whatnot. But the minute you start to say and turn the conversations from what's happening to how are we going to deal with it and grow the business. You realize the moment you say how the only way to execute with position is to understand the why. So every good what needs a why in its composition to basically fuel the people and the teams building for growth at the enterprise. Then you then look at the people that actually understands the why at across applications and categories and ambitions. You'll find that it's a very, very few people that knows that within an enterprise. And back in the days, I've even seen cases where that is an asset you protect because knowledge is power. But it it makes it hard for the enterprise to keep itself relevant over the ability of a lifetime. And that lifetime of an enterprise are just derailing. I think starting two decades ago, was very long time. You could stay as a leader. Ten years ago, it was like fourteen years. You weren't a Fortune Top 1,000. It's seven years now. What it tells me are that the velocity of how you become a leader, but also stay relevant is getting shorter through the level of innovation. And I think one of the root causes for it is the enterprise has a hard time keeping itself relevant to dynamics of their consumers. And every good company starts at something and keep itself relevant to become something else. And over time they stay close to the ones that means the most and keep innovating and reimagining themselves to stay relevant. I think the secret sauce to it is the why. And that I wanted to put in a full layer of accessibility for every builder and creator within the enterprise to go build great stuff for their markets and start seeing growth patterns that is unfair compared to competition and makes them stay relevant for longer durability of time.
00:07:40 · Calin Fabri
[00:07:40] S1: So if we would have to compare, let's say, the old world versus the new world, my understanding that, let's say, old world in consumer research, it's a report you get once a quarter, it takes, multiple months, and then the new world in which you operate and you're a category leader is more to embed yourself within the infrastructure of decision making of big brands, so then they can get this type of insight and why, you know, in twenty four hours, but it's pretty much to be embedded in their decision making process.
00:08:16 · Casper Henningsen, Calin Fabri
[00:08:16] S0: We can step... Go one step back and say today there's two established methods for extracting understanding from your consumers or customers, or even employees. You can go at a qualitative toolbox, interviews, focus groups, whatnot, and interview people to extract data and information you can translate to actions. That's one method. Another method is quantitative. We all have tried a survey, one to five, one to 10, which one do I like the best, etc. And analytics is very closely connected to that domain as well. The problem the enterprise has is that if you go for a deep contextual kind of like extraction of knowledge that takes a lot of time and is a costly process. On the other hand, surveys and quantitative methods is very fast, but it only gives you a very shallow and surface level view on the world. What's happening? Which of my concepts do they like? Which brands are recognizable and preferred within a category? So that's your two methods and has been so for the last forty years. What has happened at the enterprise level is that the enterprise has accelerated. It went from a very static, predictable environment of how we work and how we take decisions through stage gate and modeling and very predictable linear processes to being completely full paced, chaotic, agile, execution driven modalities. [00:09:57] S1: And