At the early stage, most startups do not have enough data to prove outcomes. Evaluation therefore depends on signals: indicators that reduce uncertainty and help reviewers assess whether the team is learning, executing, and progressing.
This pillar explains what signals typically matter in early-stage evaluation, how they are interpreted, and how founders can communicate them without exaggeration.
Signals vs metrics: the key difference
Metrics are numerical measurements. Signals are broader indicators that can be quantitative or qualitative. At the earliest stages, signals often matter more than metrics.
Categories of early-stage signals
1) Problem and user signals
- specificity of the problem statement
- clarity about the target user
- evidence of direct user interaction
2) Learning signals
- documented experiments
- iteration based on feedback
- clear hypotheses and what was learned
3) Execution signals
- shipping prototypes or MVPs
- moving from idea to tests quickly
- clear role ownership in the team
4) Adoption signals
- pilot engagement
- repeat usage patterns
- qualitative evidence of value
5) Credibility signals
- consistency across materials
- realistic assumptions and constraints
- awareness of alternatives and competition
How evaluators interpret weak signals
Weak signals do not automatically imply weak startups. Evaluators often ask whether the weakness is due to stage, context, or lack of learning. The difference is usually in the reasoning founders provide.
Common mistakes when communicating signals
- reporting vanity signals that do not reduce uncertainty
- inflating early numbers without context
- hiding unknowns instead of stating them clearly
A practical signal checklist
- What did you test in the last 30 days?
- What changed based on what you learned?
- What assumptions remain untested?
- What is the next experiment and why?