The next billion starts with the customer – Keynote-Highlights

Transkript

# A!-Day · Keynote-Highlights · Transkript v11

Stand 22.10.2026 · Englisch · geschnittene Fassung 13:28. Zeitmarken beziehen sich auf die Highlight-Fassung. Aus den eingebrannten Produktionsuntertiteln rekonstruiert; automatische Erkennungsfehler können enthalten sein. Kein vollständiger manueller Hörabgleich.

## 00:00:00 · Solve or create

Vector one is you solve for something. And vector two is you create something that wasn't there. Now, honestly, most of us are in vector one, whether we thought about it or not, but we tend to be in vector one. Vector two means creating demand where there wasn't any demand. And that's, of course, tricky. But technology can be an abler for that. All of a sudden, technology arrives, and that happens once in a while. Some may say the iPhone was such a moment. So every now and then, some technology lands, and all of a sudden, things become possible that weren't possible before. No

## 00:00:40 · The leaky bucket

Your bucket, a.k.a. your business, is full of holes. The entire value chain is leaky. Everything. I mean, literally, there is no single part of your value chain that's not leaking. Whether it's the way you generate traffic, your paid media, whether it's the way your shop works, your app works, web conversion, engagement rates, average order value, however you want to call this. The

## 00:01:10 · Remove friction

so keep that friction that's helpful but please try to identify the rest of the not so helpful friction and then get rid of it so understand what mission your customer is on what jobs to be done are your customers trying to achieve and then remove friction out of that process has nothing to do with AI necessarily maybe it does, maybe it doesn't, who cares the end game is to remove friction on your customer's job-to-be-done mission. Identify, solve.

## 00:01:45 · Create

the second vector I find a bit more intriguing to be honest and that's what I call the create vector so that's trying to step into a territory where demand somehow is hidden but we didn't think about it because for example it was technically simply not possible and of course what changes from the technology standpoint needs to be reflected in order to become, let's say, appreciative of the options at hand.

## 00:02:17 · Discovery and agents

And things that happen and that simply weren't there, number one is discovery goes AI. And for those that still look at that zero-something referral rate, happy to talk afterwards where this is a very, very dangerous interpretation of the reality. So there is already a fundamental a fundamental shift in the discovery, things moving to AI, but at the same time we now have agents starting to go mainstream.

## 00:02:47 · Agents and trust

The other thing that I think we need to appreciate is that whenever we discuss agents and principals, and the counterpart to the agent is the principal, so who owns the relationship? So you're my agent, am I the principal? So that discussion is super important because trust obviously comes with the question of who is the principal. So if I want to use your agent, but your principal is, I don't know, a tech vendor? as an advertising business, that becomes a bit fishy, doesn't it? So you have vendor agents, and at the moment, everything is more or less in that box. So all the agents that we see are somehow related to a vendor of any kind, of some kind. And of course, people may not bother, let's see, but were they to think about it, they would realize it is a bit fishy. I mean, the broader the assortment, the broader the offering, the less fishy it gets, But ultimately, you're talking to Nike and asking, what are the best running shoes? Well, what would the principal Nike tell you? So that Nike agent most likely is not the most neutral source. Maybe.

## 00:04:03 · Supply-led commerce

so when we talk about um what to create we as a as a business for how long we can possibly think about our business we've been in the business of pushing inventory it's maybe a very harsh way of summarizing it but we have been in what i call sales-led commerce so we buy 12 months in advance when you look at fashion or even more, and then we get it wrong, and then we push it to the market. Or it doesn't sell, we put discount on top of it. And that's basically how we roll, right?

## 00:04:38 · Demand-led commerce

Zara, she in, I know it's under discussion, but two very famous concepts that start at the opposite end of it. And I call this the demand-led commerce, where they try to understand what demand is in the market, and they try to fulfill that demand short notice. So production cycles limited from months in advance to weeks in advance. so they listen and learn, produce, test and the replenishment is the bigger order than the initial order in normal business it's the other way around normal fashion business, big order is initial small order is replenishment they turn it by 180 degrees and

## 00:05:19 · Trade demand

Now I would say we're in the create phase, so bear with me. Don't throw problems at it. But I'm just saying, now let's add one dimension to it. Let's not just work on demand-led sales. Let's trade demand.

## 00:05:33 · Imagine

Imagine someone would know when you will need something even before you start to search. Imagine. Imagine someone would know. Imagine someone would know what you're planning or intending to buy before you even type in a search query. Just imagine. And now imagine that same someone would even know your preference, your taste.

## 00:06:01 · Demand profile

The first part of that magic is what I call the demand profile. So you guys that run technology, you solve this, yeah? I'm just describing what we need. So we need a demand profile. We all are, you know, we sit on different pockets of demand. Some stuff we just need to replenish. Toilet paper, paper tissues, you name it. It's a long list of stuff we just need in a replenishment mode. No emotions, no nothing. You know, cheap, cheerful. Delivery time more important than, you know, who's the retailer. The brand of the product mostly, mainly matters more than, you know, the retailer that sells it to you. And then we have things that are easy to anticipate. You have kids, well, it's back to school. You know, a new sports season, well, there you go. So you have certain things that are not difficult to guess. There's Christmas, Mother's Day, Father's Day, Easter, blah, blah. That's easy to anticipate. And in most cases, that somehow is a moment where commerce happens. And then you have things that are even plannable. So it's your wedding, it's your grandmother's birthday, it's whatever it is in your personal life that's plannable, but

## 00:07:11 · Preferences are personal

And then the second layer to this is our preference. And of course, bad news is we've been talking about personas for the last hundred years, right? Which is, if you think about it, a bit funny, huh? It's a bit funny. Look at yourself just for a moment, and you will have to confess, and I call it a distribution of daily contradictions. We have to confess or appreciate that we're nothing like a persona. We're completely schizophrenic, multi-personal. and just within a day. It does make a big difference whether you search for something 8 o'clock on the bus. You're very more into I need it quick and I just want a solution. My sock has a hole. I need a new sock and I want it in an hour. I don't care about the price. I want it in an hour and I want it black. So just solve that for me. I don't need inspirational photography. I don't want an Insta story. I don't want a video. No, no, no. I just need the bloody sock. but it needs to be quick whilst when I'm at the same day I don't know in the evening and I'm on my sofa and I think about cashmere socks, it's a completely different ball game, I want the whole Monty I want cashmere flowers flying through my window I want, you know, smell and sound and I want the butterflies and I want the whole cashmere, I want the cow it's a goat, sorry, a different animal so I want that animal to look at me smile at me and I want that goat to give me my socks. It's the same person a couple of hours later with a glass of red wine and everything changes. And one minute you want the cheapest stuff and the next minute you want the best quality possible. And it's all us, it's all us in the same day. And now you come along with your marketing folks and you have a persona. Well, good luck with that, good luck with that.

## 00:09:04 · Agentic operating systems

So imagine, imagine that we would capture your personal, our personal preferences as well. So demand and preference. Just imagine we would be able to capture that. And then we put it somewhere where it's not an Excel file, where it's not a Klaviyo post-purchase campaign, where it's not, I don't know, a Salesforce file. Just imagine we think more in agentic operating systems. So we think more in filing systems that are dynamic, that allow intelligence layers to do something. Just think in that direction. And you can capture demand and you can capture taste and you put it somewhere where intelligence can happen. Not static,

## 00:09:52 · Demand as an asset

And then you can package it up. And I call this demand as an asset. So all of a sudden, demand becomes an asset. Can you imagine? Demand packaged up becomes an asset. and it's an asset you can start to trade. You can start to trade the individual asset of that one consumer, but you can even start to pool it and you can start to trade pool demand. Let that sink in. So you have different levels of commitment you can trade, hardcore committed. You can then, once you understood the commitment, start to negotiate with merchant agents, with retailers. You can start to negotiate. Your agent, your consumer agent, can start to negotiate the demand it knows on a commitment level that's reliable, can start to negotiate that, and then can pool the demand and negotiate even further.

## 00:10:56 · Liquid Demand

I call it liquid demand. Because for the first time ever, we would package up demand with commitment levels that are clear, based upon a demand profile and based upon a preference profile that is true, not static and we can package it up and therefore it's liquid demand that we can now take to market.

## 00:11:16 · The concept in action

And I build a quick mock-up. This is how that could work. It's animated. Can you see it? So on the left, Juno, that's our agent. So Juno now gets the commitment levels and it's different levels. You see firm, firm, flexible date. So imagine that you're flexible on the date because you don't care. School is in three months I just need the new rucksack in two months ish so the retailer that provides you with that rucksack is very flexible in choosing logistics solutions so all of a sudden they're not forced into next day delivery all the time because they start to understand you don't need it you don't need it tomorrow so why pay that extra logistics no you need it in a month well fine let's agree so it needs a decent commitment but let's agree you get in a month the retailer has a saving on the logistics, can pass on the saving to you, for example.

## 00:12:09 · Demand Exchange

And of course, there is a next level to liquid demand even. Are

## 00:12:14 · Demand Exchange

Now imagine that we bring more of those consumer agents together. Just imagine that they would gang up. They would team up. So you have an army of demand packaged liquid demand agents. and they would all of a sudden meet up in what I now call, another trademark, the demand exchange.

## 00:12:36 · The marketplace is reversed

So demand agents meet supply agents and then they can trade based upon committed demand, based upon your taste profiles. This is nothing short of the exact 180 degree pivot to what we call marketplaces today. So today it's an aggregated supply base. The marketplaces today is a supply gathering. And what I'm saying is once you think through how that demand playbook could actually work, you turn it upside down and it becomes an aggregated demand destination.

## 00:13:20 · Schlusskarte

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