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Nils Stotz shared thisSeptember was a very good month for anyone interested in experimentation, AI and how products are changing ;) I continued my “30 Days of Ideas” format: one thing worth reading, watching or listening to every day. A few highlights: 🎥 YouTube introducing A/B testing for video cuts is super exciting. Creators will be able to test up to three versions and learn which opening retains attention best. Experimentation is moving directly into the creative process. 📄 I was very happy to see David Holtz and his co-authors’ article on the future of large-scale experiments finally published in Nature Human Behaviour. Congratulations! A very good overview of where experimentation is heading and the challenges that come with operating at this scale. 🥄 I remain fascinated by Bending Spoons. They have now signed an agreement to acquire Miro (something I predicted in a few WhatsApp groups 😄) The interview with Luca Ferrari offers a fascinating look at the operating model, talent philosophy and relentless simplification behind the company. 🏢 Elena Verna’s reflections on working at an AI-native company were another highlight. Less about simply “using AI” and more about how AI changes roles, management, meetings, autonomy and the structure of the company itself. 👥 And finally, it was nice to see a VC perspective on synthetic users and behavioural simulation from Bessemer Venture Partners. I suggested a few additions, but it is a thoughtful overview of the emerging category and the questions still left to answer. These were only a few of the things that made me think this month. If you want the clickable version with the complete list of links, let me know below 🚀 #Experimentation #ProductManagement
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Nils Stotz shared this“Synthetic users will never work.” And yet, the money moves. 💸 I’ve been following this space for quite a while. Each time I update our landscape, the market sends more signals: more companies, broader use cases and increasingly serious funding rounds. We have now mapped 66 companies focused on synthetic users. A few recent examples: 💰 Simile raised a $200M Series B—only five months after its $100M Series A. 🧠 Aaru reportedly raised more than $50M in a Series A. 👥 Electric Twin announced $14M in total funding, including a recent $10M round. 📊 BluePill raised a $6M seed round to develop its AI consumer twins. 🇪🇺 Delta Labs secured €4.4M to build its customer-simulation platform. VCs are not always right. And funding does not prove that a model can accurately reproduce real human behaviour. But hundreds of millions flowing into one category is a market signal worth taking seriously. I don’t expect synthetic users to replace all research. I increasingly see them becoming a first layer: generating hypotheses, screening concepts, simulating edge cases and identifying where research with real people will add the most value. The question is becoming more specific: Where do synthetic users reliably predict real behaviour—and where do they systematically fail? The money is moving. Now the validation has to catch up. 🔬 #SyntheticUsers #UserResearch
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Nils Stotz shared thisThe Austin three-peat is happening. And this time, I’m keynoting. 🎤🎤🎤 Two years ago, I went to #CIRCUS in Austin for the first time. And weirdly enough, I still connect that trip with a bit of a turning point for me. I remember actually debating whether I should go at all. I’m very glad I did. Until then, a lot of my experimentation world existed online. LinkedIn posts, comments, names I kept seeing, conversations with people I had never actually met. Then I arrived in Austin and suddenly all of that became real. People knew each other. Some people even knew things I had written. Conversations continued offline. And I realized that there was an actual community behind all those profile pictures on my screen. 😄 That genuinely motivated me to do more of this. Funny side story: I also accepted my offer to join Zalando while I was in Austin that year. So Austin somehow ended up being connected to quite a few changes in my life. And CIRCUS itself was a revelation. It just felt different from most conferences I had been to before. More creative. More personal. And just genuinely fun. So I was incredibly happy when Ben Labay and Speero brought #CIRCUS to London this year as well. 🇬🇧 And now we’re back in Austin for round three. 🤠 This time feels particularly surreal: 🎤 It will be the third time Katie Dove and I are headlining a conference together, but the first time in the US ;) 🤯 And somehow I’m on the same stage as Marty Cagan and John Cutler. That is still pretty wild to me. Massive thanks to Ben Labay and the Speero team for inviting me back and for creating a conference that has become quite special to me over the last few years. Going from wondering whether I should even fly to Austin to coming back as a keynote speaker two years later is a pretty nice full-circle moment. Looking forward to meet all the amazing people in the experimentation community again Makram Mansour Travis Brooks Martin Tingley Kevin W. Washington Iqbal Ali Divya Isaiah Tom J. O'Connor, Jr. Guilherme Csorgo Carlos Trujillo and many many more ❤️ See you in Austin. 🤠
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Nils Stotz shared thisThe history of online experimentation has a few experiments people would rather forget. I recently took Ron Kohavi’s Accelerating Innovation with A/B Testing course, and one section that really stuck with me was about ethics. Some of the examples are pretty wild. 🧪 🔵 Facebook changed the amount of positive and negative content shown in the feeds of 689,003 users to study emotional contagion. The study later triggered a major debate around informed consent. 💘 OkCupid showed some users a 90% compatibility score even though its algorithm had calculated 30% — essentially testing whether the score itself would influence who people talked to. 📀 Amazon randomly showed different customers different prices for the same DVDs. After complaints, Amazon apologized and refunded affected customers. 🛒 Instacart tested different prices for the same grocery items at the same store with retail partners. The program was later stopped after backlash. 💼 LinkedIn varied the strength of connections shown in “People You May Know” for around 20 million users, then studied how those connections related to job mobility. What makes these cases interesting is that the hard question is not statistical significance but if experimentation should have limits: When does an experiment require explicit consent? When does experimentation become deception? And should we treat experiments on prices, emotions, relationships or job opportunities the same way as experiments on UI? In general: I really got inspired by a lot of the course material and this one was particularly interesting! If you want the links to more details about these cases let me know :) #experimentation #abtesting
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Nils Stotz shared this6️⃣6️⃣ companies are betting that synthetic users can work. Are they all wrong? 👀 I keep hearing some version of: “AI can’t replace real users.” “Synthetic users will never work.” “You can’t simulate human behaviour.” Maybe. But the market is certainly not behaving as if the question is settled. We’ve now identified 66 dedicated companies working on synthetic users up significantly from the first landscapes I shared. And what I find particularly interesting is how differently they are approaching the problem. 🧪 Some examples from the companies we added more recently: 🧑💻 AppVelocity, arato.ai, Sanctum and Synthers AI simulate people interacting with products to find UX issues and friction. 📊 Lakmoos AI, Panelia, Pollia and Xpolls are experimenting with synthetic respondents and survey panels. 🧠 Polyhyle, Market Twins and DoppelIQ are trying to simulate behaviour, decisions and reactions before something is launched. 🔬BluePill, DTwin and Synthetic Consumer Lab go broader across surveys, interviews, focus groups and other research methods. Across the full landscape, we currently see roughly four approaches: → Behaviour simulation → Surveys / synthetic panels → Interviews → Mixed research methods Now, to be clear: 66 companies ≠ proof that synthetic users work. A lot of the hard questions remain unanswered. How closely do they replicate real behaviour? Where do they systematically fail? How stable are personas over time? When do synthetic and human responses diverge? And for which research questions are they actually useful? Those are exactly the questions I think we should be studying. But I also think the debate needs to move beyond: “Synthetic users work” vs. “Synthetic users don't work.” The much more interesting question is: Where do they work, how well, and under which conditions? Because with 66 companies already trying to figure that out, dismissing the entire category is becoming increasingly difficult. And I’m pretty sure this landscape will keep growing. 🤖 Pretty sure I missed some companies so please get in touch and I will include it in the next version. #syntheticusers #ai
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Nils Stotz shared thisTwo 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 better outcomes. 🧠 The “triple threat” still matters: The people who can combine product, design and technology thinking become even more valuable as AI changes how teams work. ⚙️ AI seems to be pushing product in two directions at once: automate more of the execution (software, prototypes, tests, repetitive work) while at the same time going deeper into the craft of discovering better solutions. 🌎 And I always enjoy hearing the Silicon Valley perspective. One message came through pretty clearly: competition is not slowing down. If anything, it is getting more intense. And the funny part: Marty and I will actually both be giving keynotes at CIRCUS Austin in October. So if you want to see one of the most influential product thinkers of the last decades — and Marty Cagan — come say hi in Austin. 😂 Thanks a lot for the invite and making this happen! ProductTank Berlin Productlab Delivery Hero #circusaustin #product
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Nils Stotz shared this𝐖𝐡𝐚𝐭 𝐜𝐚𝐧 𝐨𝐧𝐥𝐢𝐧𝐞 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐥𝐞𝐚𝐫𝐧 𝐟𝐫𝐨𝐦 𝐩𝐬𝐲𝐜𝐡𝐨𝐥𝐨𝐠𝐲’𝐬 𝐫𝐞𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐜𝐫𝐢𝐬𝐢𝐬? 🧪🧠 That’s the question Prof. Dr. Karen Eilers, Fabian Hans and I have been working on over the past few months. The idea is something I kept coming across again and again in conversations with experimentation experts: We like to describe A/B testing as a scientific way of making product decisions. But many of the problems we see in practice sound surprisingly familiar: 🧪 Hypotheses formulated or adjusted after seeing results 📊 Hunting through metrics or segments until something looks interesting 👀 Repeatedly checking results and changing decisions along the way 🏆 Celebrating “winning” experiments while neutral results disappear 🧠 Weak documentation and little reuse of previous learnings Psychology has spent more than a decade dealing with very similar questions through the replication crisis and the broader open science movement. This led to practices such as preregistration, Registered Reports, stronger transparency, replication, and a clearer separation between confirmatory and exploratory analysis. So our question is: What can we transfer, what needs to be adapted, and what simply does not work in the context of digital product experimentation? That is what we currently call 𝑶𝒑𝒆𝒏 𝑬𝒙𝒑𝒆𝒓𝒊𝒎𝒆𝒏𝒕𝒂𝒕𝒊𝒐𝒏: bringing ideas from psychology and open science into the realities of product teams, experimentation platforms, organizational incentives and fast-moving businesses. Prof. Dr. Karen Eilers and Fabian Hans bring the psychology and open-science perspective, I bring the online experimentation side. And we are definitely not done yet. 🙂 If you work in psychology, open science, research methodology or experimentation and have thoughts on this intersection, we would love to hear them. Especially if you have a psychology background and now work somewhere close to experimentation or digital products. 👉 Let us know if you would be interested in exchanging ideas or contributing to the research. #experimentation #research
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Nils Stotz shared this𝘐𝘯𝘴𝘪𝘨𝘯𝘪𝘧𝘪𝘤𝘢𝘯𝘵 𝘳𝘦𝘴𝘶𝘭𝘵𝘴. 𝘚𝘭𝘰𝘸 𝘵𝘦𝘴𝘵𝘴. 𝘉𝘳𝘰𝘬𝘦𝘯 𝘳𝘢𝘯𝘥𝘰𝘮𝘪𝘻𝘢𝘵𝘪𝘰𝘯. 𝘊𝘰𝘯𝘧𝘭𝘪𝘤𝘵𝘪𝘯𝘨 𝘮𝘦𝘵𝘳𝘪𝘤𝘴. I built a library with two carefully selected articles for each of six common experimentation problems. 📚 Recently, I reread many of the experimentation articles I had saved from teams like Microsoft, Netflix, Spotify, Statsig and Eppo by Datadog. What struck me was just how good and helpful many of them still are. Some were published years ago, but their core lessons still hold up. The tools may evolve, but the practical problems experimenters face remain surprisingly familiar. So I organized the articles around the moment when you might actually need them: 🔎 Insignificant results How do you distinguish “no effect” from “not enough evidence”? ⏳ Tests taking too long How can you reduce duration without ignoring the statistical trade-offs? ⚠️ Broken randomization How do you detect and diagnose issues such as sample-ratio mismatch? ⚖️ Conflicting metrics How should you balance positive movement, guardrails, and product risk? 🧩 Users cannot be randomized Which alternatives, such as quasi-experiments or encouragement designs, could help? 🔁 Results do not reproduce Why might measured lift disappear after launch, and how can you validate it? For me, this feels like a more practical way to organize an experimentation library: start with the problem, then find the method or company case that can help. But I’m sure the collection is not complete. If you know a great article that belongs under one of these six problems, please share it in the comments—and mention which category you would add it to. I’d love to crowdsource an even stronger version of this library. 🚀 #Experimentation #ABTesting
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Nils Stotz shared this𝐁𝐞𝐟𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚𝐧𝐨𝐭𝐡𝐞𝐫 𝐞𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐟𝐞𝐚𝐭𝐮𝐫𝐞, 𝐈 𝐰𝐨𝐮𝐥𝐝 𝐬𝐭𝐮𝐝𝐲 𝐡𝐨𝐰 𝐧𝐢𝐧𝐞 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡𝐞𝐝 𝐭𝐡𝐞 𝐩𝐫𝐨𝐛𝐥𝐞𝐦𝐬 𝐭𝐡𝐚𝐭 𝐚𝐩𝐩𝐞𝐚𝐫 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞. Experimentation has become a shared capability across many major product companies. That makes sharing knowledge across company boundaries especially valuable: the products differ, but many of the challenges repeat. 📚 In one of my papers, I even used a grey literature review (a structured review of practitioner and non-academic sources) to capture knowledge that academic literature alone may miss (let me know if you are interested). That review showed me how valuable these sources can be when they are brought together and examined systematically. So I gathered 45 articles from nine companies in one place. 🔎 The collection includes Spotify on turning multiple metrics into risk-aware product decisions, Microsoft on diagnosing sample-ratio mismatches before trusting results, Netflix on quasi-experimentation when individual randomisation is not feasible, and DoorDash on interleaving designs with more than 100× the sensitivity of traditional methods. I think this collection could also support more comparative research: How do companies design their platforms, choose guardrails and build decision rules as experimentation scales? 🤝 If anyone is interested in developing a study or paper together, feel free to reach out. And if I missed an article—or a broader collection already exists—please send it my way. 💬 Also: Comment ARTICLES and I’ll send you the complete high-resolution list with all 45 links. #Experimentation #ABTesting
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Nils Stotz liked thisNils Stotz liked thisLooking forward to Circus Austin in several weeks! #CircusAustin brings together leaders in experimentation in a great format to foster conversations and learning. Really looking forward to the table sessions to meet peers focused on the same challenges + opportunities. Find me if you want to talk about how we're using AI throughout the experimentation lifecycle. Ben Labay Jonny Longden Nils Stotz Alec Donahue Tom J. O'Connor, Jr. looking forward to meet y'all there! Speero
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Nils Stotz liked thisNils Stotz liked thisMost experimentation programs never look back. Not because nothing gets logged. Most teams tag their tests, store the results, maybe even keep a tidy dashboard. That part is genuinely easy to set up, and most programs already do it. What's rare is using it. Stopping after the fifteenth test, or the thirtieth, and asking what all of it actually says about the customer, not just which variant won. I've seen test findings make it into a TV commercial. Into offline ads, online ads, a sales team's talk track, a display in a physical store. That only happens when a team treats results as evidence about how people decide, not just a verdict on a page based on a single test. Most programs stop one level too early. The test ends, the result gets logged, the tag gets applied, everyone moves to the next hypothesis. The step where someone asks what the last thirty tests are actually saying rarely happens, because nobody owns it. 🔹 Log the mechanism, not just the outcome. 🔹 After every experiment, consider how its result ties into what you already know from previous related experiments and research 🔹 Schedule the step back. Quarterly is enough (depending on the number of experiments, of course). 🔹 Ask who else in the business could use this. Marketing, sales, retail, not just the roadmap Logging your tests is not the same as learning from them. The teams getting real value out of experimentation treat their test history as a source of customer insight, not an archive. --- 🤝 𝐅𝐫𝐨𝐦 𝐍𝐨𝐯𝐞𝐦𝐛𝐞𝐫 𝐨𝐧𝐰𝐚𝐫𝐝𝐬, 𝐈'𝐥𝐥 𝐡𝐚𝐯𝐞 𝐫𝐨𝐨𝐦 𝐟𝐨𝐫 𝐨𝐧𝐞 𝐧𝐞𝐰 𝐜𝐥𝐢𝐞𝐧𝐭. If you're looking to strengthen experimentation capability in the age of AI, I'd be happy to explore whether we're a good fit. ✉️ 𝐎𝐫 𝐣𝐨𝐢𝐧 2,000+ 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞𝐫𝐬 𝐭𝐨 𝐦𝐲 𝐂𝐑𝐎 𝐓𝐢𝐩𝐬 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫.
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Nils Stotz liked thisDemocratization of UX research is happening whether research approves or not. Not because anyone decided to. Because AI made it cheap. Transcription is free. Synthesis is a prompt. An ai moderated test runs overnight. When a method costs less than asking for it, people stop asking. So your PM ran six interviews on Tuesday. Your designer ran a test nobody reviewed. Support has years of tickets and an agent that summarizes them into confident paragraphs. All of it is shaping specs right now. The research team has three options. Forbid it, and it keeps happening without a label. Wave it all through, and whoever sounds most confident wins. Or publish, on one page, what something needs before it can be called evidence, and let anyone who meets it make evidence with their name on it. Most teams are still arguing about option one. The argument was over years ago. They just didn't get the memo, because the memo was six interviews in a spec they didn't read. New article on research democratization on The Voice of User!Democratization is happening whether research approves or not. Who decides what counts as evidence?Democratization is happening whether research approves or not. Who decides what counts as evidence?
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Nils Stotz liked thisNils Stotz liked thisAustin is getting closer and I cannot wait. What excites me most is not the stage, it is the room. Being surrounded by people who think deeply about experimentation, who debate, challenge each other and share what is actually working in the field. That is the kind of energy that pushes you forward. And this year, I get to contribute to it. My work on AI and automation in CRO was selected for a poster presentation at Circus by Speero. It is an honour I did not take for granted. A huge thank you to Speero, Ben Labay, Jonny Longden, Carrie Skhilias Ralph and Annika Thompson for this opportunity. It means a lot. My poster is called 'The 16-hour gain loop'. It is about how I built a system that saved 16 hours per testing cycle by integrating AI and automation directly into the CRO workflow. Not a quick fix, but a repeatable loop that keeps building on itself over time. If you have ever felt like half your time goes to work around the work, this one is for you. The details are for Austin. Who is joining in Austin? #circusaustin #Speero #Datadog #Circus #Experimentation
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Nils Stotz liked thisNils Stotz liked thisI go to a lot of conferences, and by far the most useful parts are always the hallway side conversations. You never have to travel for content anymore (every piece of content ends up online), so the only point to attend an event is the other people there. That's the origin story of Spryng, a B2B marketing event by Wynter. Peer-to-peer learning and conversations is the main thing. And nobody's idea of a good time is casually hanging out in freezing cold hotel ballrooms with a red carpet. You go somewhere where it's fun. That's why we do this at a kickass brewery with a huge backyard. March 23-24 in Austin TX. This upcoming event is extra special as it's our 5th annual. Search for 'spryng by wynter' to find the website. We can only fit ~200 people, so go grab your spot. P.S. Thanks to Ding and Jake for the kickass video! These guys are true video pros (hire them).
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Nils Stotz liked thisNils Stotz liked thisWe evaluate thousands of experiments every month at Booking.com. How can we learn from them at scale, rather than only looking at each experiment in isolation? To enable more cross-experiment learning, I developed a zero-shot LLM classifier that tags experiments across multiple dimensions. I’m excited that this work has been selected to be shared as a poster at Circus Austin - the AI conference for experimentation leaders. Big thanks to Ben Labay, Annika Thompson, and Carrie Skhilias Ralph for the opportunity. Looking forward to seeing everyone in Austin!
Experience & Education
Licenses & Certifications
Volunteer Experience
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Student Mentor
University of Heidelberg
- 1 month
Education
Evaluated and challenged 13 pitch ideas (Entrepreneurship Course)
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Student Mentor
University of Mannheim
- 7 months
Education
Mentored four students in a business research project (MAN 633 Entrepreneurial Spirit Course)
Publications
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🇩🇪 Product-Market-Fit
Springer
See publicationSummary of main concepts and my learnings about product-market-fit
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🇩🇪 Experimentelle Produktentwicklung
Springer
See publicationSummary of my product philosophy emphasising the need for more experimentation.
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Learn, Grow, Repeat — I am leaving my first startup job
Medium
See publicationLearnings from two years of my first startup jobs
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Growth Notes - Referral: How to make people refer their friends to your business
Amazon
See publicationSummary of all my learnings about the referral stage in the pirate funnel.
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Growth Frameworks — The Retention Lifecycle Framework
Medium
See publicationArticle about a framework that I use in my everyday work.
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Building a Referral Feature — MVP Style
Medium
See publicationDescription of some ideas on implementing a referral feature with very limited resources
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How to automate a user feedback culture at a German InsurTech
Medium
See publicationDescription of a holistic feedback system that I automated and how I use it to create further value.
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Applications and tools used at a German InsurTech
Medium
See publicationDescription of applications and tools I use for growth hacking and quick wins
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Entrepreneur in Residence (EiR) at a German InsurTech
Medium
See publicationDescription of my first year on the job.
Projects
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Public Speaking
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I am actively seeking opportunities to speak in podcasts and on conferences.
Track Record:
BBC - Experimentation Day 🇬🇧(2026)
Working Products - Hamburg 🇩🇪 (2026)
conversATion - Milan 🇮🇹 (2026)
POLIMI Guest Workshop - Milan 🇮🇹 (2026)
CIRCUS by Speero - London 🇬🇧 (2026)
CHASE co-located with ICSE - Rio de Janeiro 🇧🇷 (2026)
XP - International Conference on Agile Software Development - São Paulo 🇧🇷 (2026)
Meetup de Experimentação - São Paulo 🇧🇷…I am actively seeking opportunities to speak in podcasts and on conferences.
Track Record:
BBC - Experimentation Day 🇬🇧(2026)
Working Products - Hamburg 🇩🇪 (2026)
conversATion - Milan 🇮🇹 (2026)
POLIMI Guest Workshop - Milan 🇮🇹 (2026)
CIRCUS by Speero - London 🇬🇧 (2026)
CHASE co-located with ICSE - Rio de Janeiro 🇧🇷 (2026)
XP - International Conference on Agile Software Development - São Paulo 🇧🇷 (2026)
Meetup de Experimentação - São Paulo 🇧🇷 (2026)
CODE University Guest Lecture - Berlin 🇩🇪 (2026)
CPO Track - Berlin 🇩🇪 (2026)
E-Commerce EXPO - Berlin 🇩🇪 (2026)
Digital Leaders Cruise - Frankfurt 🇩🇪(2026)
Farfetch - Experimentation Day 🇵🇹(2025)
Berlin Experimentation Meetup #11 (Panel) - Berlin 🇩🇪 (2025)
Experimentation Unite - Paris 🇫🇷 (2025)
Zalando - Product Day - Berlin 🇩🇪 (2025)
SEAA - Euromicro Conference Series on Software Engineering and Advanced Applications - Salerno 🇮🇹 (2025)
ExperimentationElite - Birmingham 🇬🇧 (2025)
XP - International Conference on Agile Software Development - Switzerland 🇨🇭 (2025)
Booking.com Conference - Amsterdam 🇳🇱 (2025)
Emerce Conversion and Analytics - Amsterdam 🇳🇱 (2025)
Experimentation Unite - Dortmund 🇩🇪 (2025)
Berlin Experimentation MeetUp #5 - Berlin 🇩🇪 (2024)
MBA Guest Lecture at Leuphana University - Lüneburg 🇩🇪 (2024)
International Conference on Product-Focused Software Process Improvement (2024)
Experiment Nation Conference (2024)
Hotfix Podcast (2024)
ProductTank Heidelberg - Heidelberg 🇩🇪 (2024)
Languages
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Englisch
Native or bilingual proficiency
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Deutsch
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Italienisch
Limited working proficiency
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House of Finance & Tech Berlin
📊 𝗕𝗲𝗿𝗹𝗶𝗻 𝗿𝗲𝗺𝗮𝗶𝗻𝘀 𝗚𝗲𝗿𝗺𝗮𝗻𝘆’𝘀 𝗙𝗶𝗻𝘁𝗲𝗰𝗵 𝗰𝗮𝗽𝗶𝘁𝗮�� — 𝗲𝘃𝗲𝗻 𝗮𝘀 𝗳𝘂𝗻𝗱𝗶𝗻𝗴 𝗰𝗼𝗻𝗰𝗲𝗻𝘁𝗿𝗮𝘁𝗲𝘀 In 2025, Berlin-based Fintech & InsurTech startups captured 61% of Germany’s sector funding: €334M out of €545M, across 14 rounds — an average ticket size of ~€24M. This signals where Berlin’s strength now sits: scale-stage conviction, not volume. Where the capital went (EY Startup Barometer): 🔹 Saving: €160M 🔹 Banking: €151M 🔹 Payment: €99M Together, these three subsectors accounted for ~75% of all Fintech/InsurTech investment nationwide. 𝗪𝗵𝗮𝘁 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻𝘀 𝗳𝗼𝗿 𝗙𝗶𝗻𝘁𝗲𝗰𝗵 𝗼𝗽𝗲𝗿𝗮𝘁𝗼𝗿𝘀 𝗶𝗻 𝗕𝗲𝗿𝗹𝗶𝗻: 🔹 Financial wellbeing is moving to the core With €160M flowing into Saving, investors are backing products that improve long-term financial resilience — shifting focus from transaction volume to sustainable user outcomes. 🔹 Leadership with fewer bets Berlin still dominates nationally, but capital is flowing into fewer, more mature companies. Traction and institutional readiness now define competitiveness. 🔹 Subsector pressure Outside the core trio, funding drops sharply: Insurance (€97M), Lending (€32M), Trading (€1M). 🔹 Later-stage bias is accelerating Across Germany, €100M+ rounds rose from 12 to 18, while sub-€5M deals edged down only slightly (440 → 430). Investors are prioritizing scale. 𝗧𝗵𝗲 𝗶𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻: Winning in Germany’s Fintech capital requires earlier corporate validation. Banks, insurers, and financial institutions are increasingly acting as scalability filters — not just customers. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺 𝗰𝗼𝗼𝗿𝗱𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗺𝗮𝘁𝘁𝗲𝗿𝘀. Structured exchange between fintechs, incumbents, and regulators unlocks partnership pathways that don’t emerge in isolation. This is the role HoFT Berlin plays across 60+ ecosystem members. 𝗜𝗻𝘁𝗲𝗿𝗲𝘀𝘁𝗲𝗱 𝗶𝗻 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗽𝗮𝗿𝘁 𝗼𝗳 𝗶𝘁? Learn more & apply: https://hoft.berlin/ #HoFTBerlin #FinancialWellbeing
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Amaranth Advisory
Expert Opinion by Maximilian Dietz: 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻 𝗻𝗼𝘄 𝗿𝗲𝗮𝗰𝗵𝗲𝘀 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲! Fintechs automated client-facing processes first – today, they are increasingly automating compliance. Traditional banks need to keep pace. Our latest whitepaper on §44 KWG IT audits reveals a clear pattern we see repeatedly in practice: supervisory findings are rarely caused by missing regulations. Instead, they stem from fragmented implementation, manual processes, and limited transparency across IT, security, and compliance functions. To close this gap, compliance needs to evolve – away from reactive documentation and towards structured, technology-enabled governance. Solutions such as AutoCompliance support institutions in operationalising regulatory requirements, automating the written order, and closing the gap between supervisory expectations and day-to-day practice. Used the right way, §44 KWG audits are not just stress tests. They become a meaningful benchmark for regulatory maturity and operational resilience. Read the full whitepaper by Amaranth Advisory: https://lnkd.in/e42bgJFf
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Startup Researcher Europe
Hamburg-based FinTech startup DivTax has raised $1.18 million in a pre-seed financing round to accelerate product development and institutional partnerships. The round included participants such as Angel Invest, CapCircle, Superangels, ING COO Dr. Ralph Müller, and lemon. markets founder Max Linden. Founded in 2023 by Nicolas Oldag, Linus Holzer, and Julius Holzer, DivTax is developing a platform for international withholding tax management. The company aims to help private and institutional investors digitally handle the refund of withholding tax on foreign dividends, a process it claims can increase dividends by up to 25%. More at: https://lnkd.in/dzGbsR2M #FinTech #TaxTech #Funding #VentureCapital #Startups
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