For data-intensive AI products, an initial consulting project can solve the cold-start problem. 7 Learnings' first client paid for consulting and allowed data usage to develop a separate SaaS product, as the problem was too complex for them to solve alone.
Your first customers will be patient with major failures if you're tackling a critical problem they have no other solution for. 7 Learnings' first price upload was a "disaster," but the customer stuck with them because the need was immense.
For complex enterprise SaaS, founders must handle sales for at least the first 10 customers and remain heavily involved up to 40. Delegating this crucial early feedback loop and trust-building process is nearly impossible.
Instead of passively hoping for customer referrals, build advocacy directly into sales contracts. The founder of 7 Learnings recommends adding clauses that require customers to co-present at trade fairs or participate in webinars, formalizing their role as champions.
While A/B tests make selling easier by proving value, they transform your offering from "software as a service" to "profit as a service." This creates immense operational burden and pressure on your implementation team to deliver the promised results every time.
LLMs are unsuitable for critical business functions like pricing optimization. These tasks require deterministic, cheap, and accurate outputs—three criteria that current LLMs fail to meet, making them a poor fit for enterprise decision automation.
When your total addressable market is small, mass cold outreach is dangerous because you risk getting blocked and permanently losing a potential high-value customer. A better strategy is a targeted account-based approach, spending significant time personalizing each message.
When selling a complex, high-impact product, price sensitivity is a key signal. If potential customers aren't pushing back or walking away because your price is too high, it's a clear indication that you're underpricing your solution and leaving value on the table.