PROOF6 – A Simple SaaS validation Framework
SaaS validation can easily become complicated. There are dozens of methods, experiments and metrics you could use — but ultimately, […]
SaaS validation can easily become complicated. There are dozens of methods, experiments and metrics you could use — but ultimately, […]
Discover the essential SaaS tech guide for 2026 Explore trends, AI integration, validation, security, and strategies to future proof your SaaS success
Customer discovery interviews produce confident answers to the wrong question. They tell you what someone thinks they want, not what they will actually do. Founders who mistake interview signal for behavioral evidence are building on stated preference, which predicts enthusiasm but not usage.
Tool adoption in organizations fails because the tool required behavior change that leadership never modeled. Training completion and UI quality are the wrong variables. This post explains the real cause and what to do about it.
AI assistants have made internal documentation cheaper to produce and less likely to be consulted. The bottleneck in organizational knowledge transfer was never creation — it was the conditions under which people actually look something up. More documentation does not fix that.
Willingness to pay cannot be measured through surveys or interviews — it can only be observed through actual transactions. Founders who delay pricing are not being cautious. They are systematically avoiding the one experiment that would tell them whether their product has economic value.
The fastest way to invalidate a SaaS idea is not to build an MVP. It is to manually do the job the product would automate and see whether anyone pays before you automate anything. Manual delivery validates the job, the price, and the customer — without the sunk cost of a build.
Most SaaS feedback systems are collection systems, not decision systems. They accumulate signal without a defined protocol for converting it into action. This post explains why the response mechanism matters more than the feedback itself, and how to build a loop that closes.
Prompt engineering is a competency built on a limitation, and the organizations most motivated to close that limitation are the model providers themselves. Founders who invested in prompt engineering as a strategic moat are building on a foundation its architects intend to remove.