Jira Product Discovery: Configure weighted scoring
Use a Jira Product Discovery Custom formula field’s Weighted Score option to combine inputs with documented weights and normalization.
This score is relative to the ideas in the space. For a product review, explain the inputs and weighting before presenting the sorted list as a recommendation.
Step-by-step guide
Choose inputs that answer a decision
Define two or three fields such as Customer impact and Strategic fit, with shared rating meanings. Ask a space admin to create missing fields. Keep a written example of what a high and low rating mean so weighting does not disguise inconsistent judgments.
Configure the weighted formula
As a space admin, create a Custom formula (Fx) field, choose Weighted Score and add the selected input fields with their intended weights. Start with positive weights if the model is additive. Document any negatively weighted input because that changes the calculation to a ratio of weighted groups.
Check normalization with simple data
In a single positive Number input example, ideas with values 6 and 3 normalize to 100 and 50. Use a small example to explain why a normalized score is not an absolute business-value estimate or necessarily comparable with another space.
Review sensitivity before using the ranking
Change a test input and inspect which ideas move. Compare the ordering with the underlying evidence, and record why the chosen weighting reflects this decision. Negative-input sums of zero can produce an empty output, so investigate blanks before treating them as low priority.
Common mistakes
Presenting a relative score as fixed value
Normalization depends on the input values across the space. Explain the scope and model when sharing the ranking.
Sources and verification
Based on product documentation checked on 2026-11-04. This is a documentation-based guide, not a hands-on benchmark. Features and availability can change.