The Use Case Scoring Framework: Ten Dimensions Explained
The Use Case Scoring Framework: Ten Dimensions Explained
An opportunity score exists to make use case candidates comparable — to replace “the one we discussed longest wins” with a fixed set of questions asked of every candidate. It is decision support, not market truth: a score summarizes today’s evidence and reasoning, and real feedback can and should change it.
The ten dimensions
Each dimension is reasoned independently, with its own justification, so you can see why a candidate scores the way it does — and challenge it.
| Dimension | The question it asks |
|---|---|
| Pain frequency | How often does the target user hit this problem? |
| Pain severity | What does it cost them when they do? |
| Urgency | Do they need it solved now, or someday? |
| Existing spend | Is money (or paid staff time) already going to alternatives? |
| Product fit | How well does the actual product solve this specific problem? |
| Reachability | Can this audience be reached through channels you actually have? |
| Differentiation | In this use case, is there a reason to pick you over the obvious alternative? |
| Evidence quality | How strong and how plural are the sources behind the above? |
| Testability | Can this be validated cheaply and quickly? |
| Strategic fit | Does winning here fit the team’s capabilities and direction? |
How the total is calculated
The total is a deterministic weighted sum:
Total score = Σ (dimension score × configured weight)
The arithmetic is not a model’s overall impression — the same dimension scores always produce the same total. The exact production weights may be tuned during beta; the public framework explains the reasoning model, not a permanent ranking guarantee.
Two guard rules apply regardless of weights: a candidate with a confirmed product capability gap cannot score high on product fit, and a candidate that cannot be tested is floored on testability.
Confidence is not part of the score
Alongside every score, UseCaseify reports confidence (low / medium / high) and an evidence level (insufficient → single signal → repeated signal → prospect feedback → behavioral signal). A candidate can score 75 with low confidence: promising on paper, thin underneath. Blending that uncertainty into the number would hide exactly what you need to see.
Missing data and contradictions
Missing data lowers evidence quality and confidence — it does not silently default to average. Contradicting evidence is kept attached to the opportunity, and unresolved contradictions are surfaced with the recommendation, not smoothed over.
Manual override
Scores can be overridden by the user, because the team knows things the public web does not. Overrides are recorded, and downstream conclusions (recommendations, reports) are invalidated and recomputed rather than left stale.
Supported recommendation, priority candidate, or no recommendation
A non-rejected candidate with non-low confidence can qualify as a supported recommendation. If no candidate qualifies, UseCaseify does not silently turn the highest score into a recommendation. It may label a candidate a priority validation candidate only when the candidate has non-low confidence, repeated signals, at least one supporting source, adequate product fit and testability, and has survived red-team review. That label means “test this early hypothesis next,” not “the market wants this.”
If no candidate passes those guardrails, UseCaseify names no recommendation and directs the user to collect stronger evidence.
Rescoring after validation
Real prospect responses generate score-change suggestions with stated reasons. They apply only when you accept them, with provenance kept — so the score’s history tells you not just where the bet stands, but what moved it.
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