Spreading efforts across startups, SMBs, and enterprises created confusing signals. A deep dive into metrics revealed enterprises, despite being a smaller revenue portion, showed the highest expansion potential, prompting a decisive focus that unlocked growth.
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SaaS companies scale revenue not by adjusting price points, but by creating distinct packages for different segments. The same core software can be sold for vastly different amounts to enterprise versus mid-market clients by packaging features, services, and support to match their perceived value and needs.
The popular pursuit of massive user scale is often a trap. For bootstrapped SaaS, a sustainable, multi-million dollar business can be built on a few hundred happy, high-paying customers. This focus reduces support load, churn, and stress, creating a more resilient company.
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.
Stop targeting the ambiguous "mid-market." Your strategy, hiring, and ACV must align with either a marketing-led SMB motion or a sales-led enterprise motion. Blending them leads to failure as they are distinctly different games.
For consumption-based models, simple size-based segmentation (SMB, Enterprise) is insufficient. Stripe and Vercel use a two-axis model: company size (x-axis) and growth potential (y-axis). A small company growing at 200% YoY is more valuable and warrants more sales investment than a large, stagnant one.
While founder-led sales are critical, StackAI believes they waited too long to hire their first salesperson. Bringing in help earlier, around $500K ARR, would have accelerated their ability to test and refine their go-to-market strategy much faster.
StackAI found the bulk of enterprise revenue comes from expansion, not the initial deal. They operationalized this by creating a team of "AI strategists" who work with customers post-sale to proactively identify and build new use cases, driving deep account penetration and growth.
Initially building a tool for ML teams, they discovered the true pain point was creating AI-powered workflows for business users. This insight came from observing how first customers struggled with the infrastructure *around* their tool, not the tool itself.
The sweet spot for their transformational AI platform wasn't the largest corporations, which are too rigid to adopt new tech. Instead, it was mid-market companies (100-1,000 employees) that had budget and pain but were agile enough to implement new workflows successfully.
When growth flattens, data companies must expand their value proposition. This involves three key strategies: finding new end markets, solving the next step in the customer's workflow (e.g., location selection), and acquiring tangential datasets to create a more complete solution.
