Unified Predictive Decision Making for Retail Growth - with Felix Hoffman of 7Learnings

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Felix Hoffmann CEO at 7Learnings. 7Learnings is a Berlin based retail AI company that uses machine learning to help retailers and brands optimize pricing, marketing and ordering decisions. Felix examines why retail margin pressure is increasingly structural rather than cyclical. He outlines how predictive decision-making platforms enable retailers to coordinate commercial actions across the full stack, using demand simulation to evaluate pricing and marketing options before committing and connecting inventory reordering to expected demand and planned pricing simultaneously. Today's episode is sponsored by 7Learnings. If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance. That means the right title at the right type of organization. And of course readiness. Emerge attracts VP and higher ranking enterprise audiences who are already convinced that they need to move beyond traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit emerge.com/addone. That's emerj.com/adone.
Now the conversation with Felix Hoffmann.
Felix, welcome to the AI in Business Podcast.

**Felix Hoffmann** (1:53)
Thanks for having me.

**Daniel Faggella** (1:54)
I've mentioned this to you already, but I'm super excited to get into commerce and retail discussions with you today. You've got a very interesting point of view on what's happening in the industry. And I want to start with what we see happening on the ground and not get too out there, keep it to what's really, really happening and what's really going to change. There's been a lot of discussion in retail circles about margin pressures and things like market volatility.
And some of these things we just gloss over and we don't really look at how much of that pressure is in fact self-inflicted. And for you, having watched this problem play out across a wide range of retail environments, I want to ask you what's actually driving the urgency right now and why hasn't the industry solved for this?

**Felix Hoffmann** (2:40)
Yeah, I mean, having a background from Zalando, we had like, I think when I left 5 million products in 28 markets, you see that also with more and more of our customers from 7Learnings, is that just you keep adding more products, and at the same time, you keep adding sales channels.
And that means you need to make more and more decisions for those products on those sales channels, like what price you want to use, which marketing campaign you want to participate, what is your marketing steering going to be. So at the same time, you're still trying to solve that with Excel often, or really rule-based decision-making. And that is creating the problem. It's not optimal what you're doing there. And so basically, you're suffering from that, or many retailers are suffering from that and brands as well.

**Daniel Faggella** (3:25)
So I'm wondering if you can take us through the scene at the retailers, at the core of their struggle. What does a day in the life look like when pricing, marketing and inventory are handled in silos?

**Felix Hoffmann** (3:38)
Yeah, often you have one central warehouse, or a small amount of warehouses, right? So you have a central stock. And then, let's say you are at Orlando, actually, we had one gigantic marketing campaign for sneakers, I think, in the UK. And suddenly, all the nice sneakers we bought were sold out in the UK. It looked really good, actually, for the UK team.
But it wasn't really optimal for the entire company, because you could have sold the sneakers globally at a much higher price. And that's just an example on product level, what you have to consider. It's often, even retrospective, difficult to know where you made a mistake, because it's tough to know what would have happened if you would have made a different decision. And that's, so in my opinion, that's where people are sometimes thinking, oh, I'm doing a great job, but they don't even know what they're missing out, basically, because it's so difficult to analyze retrospective, what could have changed if you would have optimized in a better way.

**Daniel Faggella** (4:29)
That's interesting. I'm going to tap on that a little bit later in our conversation, because I have a specific question that this will be very relevant in. So I'm going to keep that in the back of my mind.
You've been on both the consulting and the technology side of this. Where do you see the most self-deception? Retailers who think they're managing pricing, but they actually aren't.

**Felix Hoffmann** (4:51)
Yeah, I think often people in the beginning of the conversations when we're on-board, they say they're doing a great job on data. And then when they're on-boarding, we say, well, there's a couple of issues we have to solve first. So I think that's one point. And that's important because if you want to become more automated, more optimized with your decisions, you need to have clean data. Just as an example, you need to have purchase prices, which are correct on all your products. And that seems like a simple thing. And people might say it's easy, but it's not actually in the end for many.