He needed a big retailer's data to build the product. No big retailer gives data to a company with no product. Felix Hoffmann solved it sideways: 7Learnings sold a paid consulting project, kept the right to use the data, and built its predictive pricing product on top of it. Ten customers later it was at $1M ARR, and he had closed every one himself.

Felix explains why a demand forecasting product cannot start with a small customer, how he structured the first pilot as an A/B test so a retailer could hand over half its prices without betting the business, and what happened when the first run came back far too expensive.

Plus: how a pricing optimization company prices itself, and why he refuses success-based fees even though he can prove the uplift.

7Learnings is a Berlin company whose software forecasts demand for each product at each price, then sets the price that hits a retailer's goal. It is now at multiple seven figures in ARR with around 40 customers. Felix spent six years as a pricing consultant at Kearney and two years running price optimization at Zalando before founding it.

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🔑 Key Lessons

• 🎯 Solve the data cold start by selling something else first: 7Learnings could not train a forecasting model without a large retailer's sales history, so it sold a paid consulting project and kept the right to use that dataset.
• 🤝 Shrink a scary ask into a reversible test: Retailers would not hand pricing to an algorithm outright, so 7Learnings ran an A/B test on half the assortment while the retailer's own team priced the rest.
• 📉 Pick an early customer who can survive a failure: The first live pricing run was badly wrong on high-priced products. It survived because the buyer had a big enough problem, no alternative, and understood they were working with a startup.
• 💰 Price high enough to lose some deals: His test is blunt. If nobody is walking away because you are too expensive, you are too cheap, especially for a complex product carrying real delivery cost.
• 🚀 Founder-led sales lasts longer than founders expect: Felix closed all ten customers behind the first $1M ARR himself, and stayed closely involved through the next forty, because handing off enterprise sales is genuinely hard.
• ⚡ Pick the technology after the problem, not before: Felix argues founders are all digging in the same technical space, and that decisions needing determinism, low cost and explainability should not be handed to an LLM.

Chapters

• Where the idea came from: Kearney, then Zalando
• The hardest part was finding co-founders
• The consulting project that funded the product
• Finding the first paying customer
• Structuring the first deal as an A/B test
• The first upload was a disaster
• How a pricing company prices itself
• Ten customers to $1M ARR
• The price matching objection
• Why LLMs don't belong in the pricing decision

Resources

• Full show notes: https://saasclub.io/494
• Join 5,000+ SaaS founders: https://saasclub.io/email