RICE prioritization calculator
Score your backlog with Reach × Impact × Confidence ÷ Effort. Edit the rows below — the scores and your prioritized order update instantly. No signup, no spreadsheet.
| Feature / idea | Reach | Impact | Confidence | Effort | RICE score | |
|---|---|---|---|---|---|---|
| 960#1 | ||||||
| 900#2 | ||||||
| 400#3 |
Your prioritized order
Highest RICE score first — the math, not the loudest voice in the room.
- 1Bulk CSV import960
- 2AI smart search900
- 3Dark mode400
RICE scores are guesses until they're grounded.
Reach and confidence are estimates — and in a roomful of stakeholders, estimates get argued. Specky scores your backlog against your real customer signals from Slack, Gong, and support, so each number comes with the evidence that justifies it. Prioritize with receipts, not opinions.
Score your backlog on real evidenceWhat is the RICE prioritization framework?
RICE is a scoring model for prioritizing product work, popularized by Intercom. It rates each idea on four factors and combines them into a single comparable number: Reach (how many people or events the work affects in a time period), Impact (how much it moves the needle for each one — scored as a multiplier: 3 for massive, 2 high, 1 medium, 0.5 low, 0.25 minimal), Confidence (a percentage capturing how sure you are of your reach and impact estimates), and Effort (the work required, in person-months). The score is (Reach × Impact × Confidence) ÷ Effort. The point isn't false precision — it's a shared, transparent way to compare apples and oranges, so prioritization becomes a conversation about inputs instead of a contest of conviction.
How to calculate a RICE score
Start with Reach: a real number over a fixed window — '1,200 users per quarter', not 'a lot'. Pick Impact as a multiplier on a feeling-led scale (3/2/1/0.5/0.25); resist the urge to make everything a 3. Set Confidence honestly — 100% means you have hard data, 80% means some evidence, 50% means it's mostly a hunch. Estimate Effort in person-months across design, engineering, and QA. Then divide: (Reach × Impact × Confidence%) ÷ Effort. A high-reach, low-effort item with solid confidence will rise; a flashy idea you're only 50% sure about will sink unless the reach justifies it. Compare scores across the backlog — the absolute number is meaningless, the ranking is the output.
Where RICE breaks down — and how to fix it
RICE has one structural weakness: two of its four inputs (Reach and Confidence) are estimates, and estimates are exactly what stakeholders argue about. A score is only as trustworthy as the evidence behind the numbers, and most RICE sheets have none — they're a spreadsheet of educated guesses dressed up as math. The fix isn't a better formula; it's grounding the inputs in real customer signal. That's what Specky does: it scores your backlog against the actual Slack threads, Gong calls, and support tickets behind each item, so 'Reach: 1,200' and 'Confidence: 80%' come with the receipts that justify them. The framework is sound — it just needs evidence underneath it to survive the prioritization meeting.