Feature Prioritization
Feature Prioritization is a steady way to order work by value, proof, cost, and risk.
Another example
People ask for new profile themes. The team fixes unreliable payments first because they pose more risk.
Main parts
- 01Candidate work
- 02Value and evidence
- 03Cost, effort, and risk
Use it when
Use it when the team has more useful work than it can finish.
Do not use it when
Never put guesses into a scorecard and act as if the final number is exact.
Name used in code
RICE / impact-effort Before you ship
Check the target user, wanted result, current baseline, evidence source, scope, owner, and success level. List assumptions, open risks, and the next test or decision.
Outcome: Use Feature Prioritization to make a focused product decision backed by evidence. User context: People ask for new profile themes. The team fixes unreliable payments first because they pose more risk. Product concept to apply: Feature Prioritization. Why it fits: Use it when the team has more useful work than it can finish. Do not use it when: Never put guesses into a scorecard and act as if the final number is exact. Product framing: Name the target user and wanted result. Record the current baseline and evidence. Set what is in and out of scope, the limits, the owner, and the time frame. Decision quality: Keep facts, guesses, and unknowns separate. Record the options you did not choose and what evidence would change the decision. Acceptance criteria: The document names an owner, target user, measurable result, scope limit, evidence source, open risks, and next test. Evidence and limits (evidence boundary): No single official standard controls the whole term. Its meaning may change across teams, platforms, or frameworks. Unknowns to confirm: Target group, baseline, success level, owner, time frame, dependencies, and review schedule.
Check this request
Review the current use of Feature Prioritization. Definition: Feature Prioritization is a steady way to order work by value, proof, cost, and risk. Release checks: Check the target user, wanted result, current baseline, evidence source, scope, owner, and success level. List assumptions, open risks, and the next test or decision. Before changing code, report the evidence you found, gaps, severity, and the smallest safe fix.