Nils Stotz — Head of Product | Experimentation & AI | Lecturer | Researcher | Springer Author | Bocconi
Head of Product | Experimentation & AI | Lecturer | Researcher | Springer Author | Bocconi
Nils Stotz ranks #555 of 18,566 LinkedIn creators in Retail, and is a standout voice in Germany. They have 10.9K followers and published 22 posts in the last 60 days at a 0.5% average engagement rate.
- 10.9K followers
- 22 posts / 60d
- 0.5% avg engagement
- 203 follower growth / 30d
The roast
Nils claims 80 percent of features are useless, which explains why he spends his work week writing Springer books nobody reads to keep his 0.69 percent engagement rate company. He’s the human equivalent of a Zalando return pile: technically inventory, but nobody is actually buying it.
About Nils
80% of product features are rarely used. Yet companies still invest billions in gut decisions. I believe we can change that – by making Experimentation the central principle of product, strategy, and innovation. I explore how experiments can answer not just small UX questions, but the big bets: business models, growth strategies, and organizational choices. Here on LinkedIn, I share tools, ideas, and stories from the global Experimentation community — to inspire a shift towards truly evidence-based innovation.
Highlights
- Top 5% in Retail — Ranked #11 of 858 creators
- Top 5% in Germany — Ranked #17 of 762 creators
- Top 5% Creator — 22 posts in 30 days
- Top 25% Audience — 10,945 followers
Recent posts
Two keynote speakers for CIRCUS Austin. One is a product legend. The other is Marty Cagan ;) I tried to make a similar joke recently and the other person didn’t really get it. Maybe I’m just too German. Or I naturally sound far too serious when I say ridiculous things. 😅 On a more serious note: it was genuinely great to meet Marty Cagan in Berlin last week and hear him and Elias Lieberich discuss where product is heading. A few things that stuck with me: 🤖 The AI productivity paradox: We’re adding incredibly powerful tools, but that doesn’t automatically translate into better products or
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I was surprised I had never come across this AI × experimentation paper before. 🤔 And then I realized: This is actually one of the biggest challenges with AI research today. A lot of AI papers are evaluated on models that are already moving targets. By the time a paper goes through: 📄 Research design 🧪 Experiments ✍️ Writing 🔍 Review process 📢 Publication the model used in the study might already be replaced by a newer generation. Which creates an interesting question: How long do findings about AI models actually stay valid? But despite this challenge, I found this paper by Hyunji
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You don’t need to be a statistician to run experiments. But you should understand these 9 concepts. 🧪📊 A/B testing can look deceptively simple: split traffic, compare outcomes, check significance, make a decision. But a lot of the complexity sits underneath that last step. A few concepts I think every experimenter should be comfortable with: 📉 P-values They tell you how surprising your result would be if there were actually no effect. They do not tell you the probability that your hypothesis is true. 📊 Confidence intervals A point estimate alone can be misleading. The interval helps you
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Last updated 2026-08-01