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Start with the problem, not the proposed feature
A feature request describes a possible solution, not necessarily the underlying need. Before ranking it, state the user’s task or friction in plain language and name the outcome the product should improve: for example, task success, adoption, conversion, or satisfaction. Then ask what would change for the user if the work shipped.
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This reframing also applies to technically elegant proposals. A cleaner architecture or a clever implementation may be valuable, but its elegance alone does not establish user impact. It matters to prioritization when it improves an outcome, reduces meaningful risk, or materially enables future delivery.
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How to decide what to work on first
- Define the opportunity. Describe the unmet need and the product outcome it is expected to affect. Make the intended user group explicit.
- Check whether the problem is real. Combine product metrics with discovery evidence such as interviews, support feedback, and input from sales or other customer-facing teams. Look at which users or events would be affected over a defined period; a loud request is not automatically representative demand. Intercom recommends measuring reach where possible, while Atlassian cautions against relying on gut feel or the loudest voices: Intercom’s RICE guide and Atlassian’s prioritization guidance.
- Separate reach from impact. Reach is how many users or events encounter the change during the chosen period. Impact is the size of the benefit for each affected user, judged against the outcome you selected. This distinction prevents a small improvement for many people from being treated as equivalent to a major improvement for a smaller group.
- Record confidence. Note how well-supported each reach and impact estimate is. If a proposal has potentially high impact but weak evidence, it may be better to research or test it before committing to a build.
- Estimate total effort consistently. Include product, design, and engineering work—not just the implementation task. Choose one unit for all candidates; Intercom’s example uses person-months, but a team can choose another unit if it applies it consistently.
- Review the ranking in context. Check dependencies, table-stakes commitments, reliability and usability needs, strategic bets, and the balance of investments across the roadmap. If one of these changes the order, record why.
- Revisit when evidence changes. Update estimates as research, usage data, implementation discoveries, or market conditions change. Prioritization is an ongoing decision, not a one-time score.
Use RICE as a comparison aid, not a verdict
RICE stands for Reach, Impact, Confidence, and Effort. Intercom’s formula is (Reach × Impact × Confidence) / Effort. Define a time period for reach, estimate benefit per affected user for impact, use confidence to show how well the estimates are supported, and count total team effort. The formula and example scales below come from Intercom’s published framework; they are suggested conventions, not measured evidence that a particular score predicts product success: Intercom, “RICE Prioritization Framework for Product Managers”.
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| Input | What to estimate | Intercom’s example |
|---|---|---|
| Reach | Users or events affected within a defined period | No fixed example scale stated |
| Impact | Benefit per affected user against the chosen outcome | 0.25 minimal; 0.5 low; 1 medium; 2 high; 3 massive |
| Confidence | How strongly evidence supports reach and impact estimates | 50% low; 80% medium; 100% high |
| Effort | Total work across the team, including product, design, and engineering | Illustrated in person-months |
Those impact and confidence values are Intercom’s example anchors, with no publication year stated on the reviewed page. They are not empirical findings. Teams can define different anchors, but should keep them stable enough that candidates can be compared fairly.
After calculating scores, sort the proposals and inspect the result. If a score seems implausibly high or low, revisit its inputs. A precise-looking number cannot repair a weak assumption; use the score to focus discussion, not to conceal uncertainty. Intercom also notes that dependencies and table-stakes work can warrant a different order.
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Choose a prioritization method that fits the decision
There is no single framework that decides every roadmap. Atlassian recommends choosing based on goals, product complexity, team expertise, and available data. Its overview compares several common approaches: Atlassian’s guide to product prioritization frameworks.
| Method | Useful when | Main caution |
|---|---|---|
| RICE | You can estimate reach, benefit per user, confidence, and effort across comparable opportunities. | Inputs can take time to validate and remain subjective; a numeric result may look more certain than the evidence warrants. |
| Opportunity scoring | You have customer ratings for importance and satisfaction and want to find important, underserved needs. | It captures only part of an opportunity and cannot predict market response by itself. |
| Kano | You need to distinguish expected basics, performance improvements, and unexpected delighters. | It classifies satisfaction patterns but does not independently resolve strategy, reach, or delivery cost. |
| Value versus effort | You need a quick team discussion about likely value and implementation work. | Estimates can be imprecise and vary by team. |
| Cost of delay | Timing matters and postponing an opportunity has an ongoing economic cost. | Inaccurate value or time estimates distort the comparison. |
Atlassian points to opportunity scoring for customer-satisfaction goals and value versus effort as a simpler option for newer teams. Choose a method your team can apply with the information it has; changing methods does not remove the need to examine assumptions.
Rank #3
Keep technical elegance from distorting the roadmap
For every proposed feature or technical investment, ask how many users face the problem, how severe it is, what evidence supports the claim, and what the full team effort will be. Then consider whether the proposed work addresses reliability, usability, or a strategic need—not just whether it adds visible functionality. Atlassian warns that shipping requested features alone can leave onboarding gaps, create feature bloat, or push bugs and reliability work aside: Atlassian’s handbook on prioritizing ideas.
Customer-facing evidence needs context. Support tickets, sales requests, and interview anecdotes can reveal real problems, but they do not automatically represent the whole audience. Combine qualitative signals with quantitative evidence and consider which users are represented—and which may be missing—before treating a request count as a measure of demand.
Rank #4
Make the decision and its trade-offs visible
A useful prioritization record can be brief: the user problem, desired outcome, affected group and period, estimated reach and impact, confidence, total effort, and the reason for any adjustment to the score-based order. This makes it easier to compare candidates and to revisit the decision when new evidence arrives. A tool is optional; Atlassian describes Jira Product Discovery as a way to organize ideas and roadmaps, but the decision process does not require particular software: Atlassian’s Jira Product Discovery handbook.
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Prioritization frameworks structure the comparison; they do not guarantee a successful product outcome. The practical goal is to make assumptions and trade-offs explicit, then choose work that best serves the product’s goals and users under the team’s actual constraints.
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