Execute Test Runs and Review Evidence
Expose the no-code prototype to a cohort of target users and systematically record interaction metrics, feedback, and system performance logs. Measure real user behaviour against the predefined quantitative success criteria and failure thresholds. Evaluate the reliability, speed, and overall fidelity of the setup to identify whether tool constraints impacted user actions.
Reviewing evidence quality provides an objective assessment of whether the prototype validated or invalidated key venture hypotheses. It prevents false positives by rigorously testing user engagement signals against hard evidence rather than founder intuition.
Produce a comprehensive validation report summarising quantitative usage data, conversion rates, user feedback, and platform performance logs. The output must state unambiguously whether core hypotheses were validated, invalidated, or remain inconclusive.
Five questions an expert would ask when reviewing your output
Use these to challenge assumptions, pressure-test your logic, and check the quality of this action's output in the context of the parent task and wider venture development.
- 1
What proportion of user interactions represented genuine commercial intent or engagement versus superficial curiosity?
- 2
How do you distinguish between user friction caused by platform limitations and true lack of interest in the core proposition?
- 3
What unexpected user behaviours or edge cases emerged during live testing that challenge your business model assumptions?
- 4
How robust is the collected dataset, and do you have a sufficient sample size to make capital allocation decisions?
- 5
Why should the venture trust these test results as a reliable baseline for committing to full custom engineering?