Founder Amanda Kahlow deliberately targeted large enterprise customers first for both her companies. This defies the common advice to start with SMBs. Her rationale: it’s easier to simplify an enterprise-grade product for smaller markets than it is to scale a simple product up.

Before building, founders in complex industries must deeply understand the operational rigor and nuances of their target vertical. This 'operator market fit' ensures the solution addresses real-world workflows, as a one-size-fits-all approach is doomed to fail.

Startups often fail to displace incumbents because they become successful 'point solutions' and get acquired. The harder path to a much larger outcome is to build the entire integrated stack from the start, but initially serve a simpler, down-market customer segment before moving up.

Most SaaS startups begin with SMBs for faster sales cycles. Nexla did the opposite, targeting complex enterprise problems from day one. This forced them to build a deeply capable platform that could later be simplified for smaller customers, rather than trying to scale up an SMB solution.

Don't build a perfect, feature-complete product for the mass market from day one. It's too expensive and risky. Instead, deliver a beta to innovator customers who are willing to go on the journey with you. Their feedback provides crucial signals for a more strategic, measured rollout.

Believing you must *convince* the market leads to a dangerous product strategy: building a feature-rich platform to persuade buyers. This delays sales, burns capital, and prevents learning. A "buyer pull" approach focuses on building the minimum product needed to solve one pre-existing problem.

Scout's expansion strategy isn't to move from online schools to traditional ones. Instead, they're betting that online/hybrid education will become the dominant model, forcing traditional districts to adopt their platform to compete. They are building for the future market, not the current one.

Nominal followed Peter Thiel's advice by first targeting the small, acutely painful problem of post-test data review. By building a 10x better solution for this specific niche, they established a strong beachhead from which they could then credibly expand into adjacent markets like manufacturing and fleet operations.

Instead of starting with a scalable platform, Decagon built bespoke, perfect solutions for its first few enterprise customers. This validated their ability to solve the core problem deeply. Only after proving this value did they abstract the common patterns into a platform.