Prioritization tools

Free RICE Score Calculator

Compare and rank multiple feature requests with (Reach × Impact × Confidence) ÷ Effort. Free, no signup, works in your browser.
Need more than a calculator? See the ProductLift workflow →

Compare your requests

3 requests

Edit the example data or import a CSV. Scores stay hidden until you are ready to compare.

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Every cell is editable. Click a request name or number, replace the examples with your own backlog, then select Score requests to reveal and sort the ranking.

Request Reach Impact Confidence Effort RICE score Actions
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CSV columns: request, reach, impact, confidence, effort

How it works

A higher score means more expected value for the effort. Rank only requests scored on the same 1-10 scale.

Need the method?

Read the practical RICE guide for calibration, examples and common scoring mistakes.

Prefer a spreadsheet?

Download the Excel template or browse all RICE templates.

The RICE Score Formula

Reach × Impact × Confidence
Effort

Intercom introduced the four-variable RICE formula. This calculator uses a normalized 1 to 10 input scale so you can compare requests quickly. Keep the same scale and time window for every row. For the classic scales and calibration advice, use the practical RICE guide. If you are unsure whether Reach belongs in the model, compare RICE vs ICE.

  • Reach: how many users the feature affects in a given time window (typically a quarter). Score 1-10, higher is more users.
  • Impact: how much the feature moves the needle per user. Score 1-10 where 10 is transformative and 1 is barely noticeable.
  • Confidence: how sure you are about your Reach and Impact estimates. Score 0.01-10 (0.01 = pure guess, 10 = validated with data).
  • Effort: total person-months to ship the feature. Score 1-10 where 1 is under a month and 10 is over a year.

Worked example

A SaaS team is scoring a "bulk edit for the backlog" feature. It affects 6,000 of their 20,000 monthly users (Reach = 6). It's a high-impact quality-of-life upgrade (Impact = 8). They interviewed 15 customers who all asked for it (Confidence = 9). Engineering estimates 6 weeks of work (Effort = 2). Applying the formula:

RICE = (6 × 8 × 9) / 2 = 216

There is no universal good score. Compare requests from the same backlog, scored with the same rubric, then investigate why the highest-ranked items lead.

Edge cases and common mistakes

  • Confidence set to 1 by default. Confidence is a discount factor. Setting it to 10 for every feature defeats the purpose. Force yourself to score below 5 unless you have data.
  • Effort in weeks vs person-months. Effort should be person-months of the whole team, not calendar weeks of one engineer. A "2-week feature" that needs a designer and PM is usually 1.5-2 person-months.
  • Reach reused across quarters. Pick a consistent time window (usually one quarter) and apply it to every feature. Mixing "annual reach" with "quarterly reach" makes scores incomparable.
  • Impact inflation. If more than 20% of your features score Impact = 8+, you're inflating. Calibrate by ranking historical shipped features first.
  • Ignoring strategic dependencies. RICE is a scoring model, not a planner. A high-RICE feature that depends on a low-RICE prerequisite still has to wait.

ProductLift keeps RICE scores beside votes, comments, customer segments, MRR, and roadmap status. That lets the team inspect the evidence behind a score before committing to the order. See the connected RICE workflow, browse the template formats, or compare all routes in the prioritization hub.

Other prioritization methods

ICE Calculator WSJF Calculator MoSCoW Kano Model Opportunity Scoring
View all tools →

Frequently Asked Questions

How do I score each field?
Reach (1-10):
  • 10: >100,000 users per quarter
  • 8: 50,000 - 100,000 users per quarter
  • 6: 10,000 - 50,000 users per quarter
  • 4: 1,000 - 10,000 users per quarter
  • 2: 100 - 1,000 users per quarter
  • 1: <100 users per quarter
Impact (1-10):
  • 10: Transformative - Game-changing for the product
  • 8-9: Very High - Significant improvement for many users
  • 6-7: High - Notable improvement for some users
  • 4-5: Medium - Moderate improvement for a few users
  • 2-3: Low - Minor improvement for a small number of users
  • 1: Very Low - Barely noticeable improvement
Confidence (0.01-10): Use the scale from 0.01 to 10, where 10 is the highest confidence.Effort (1-10):
  • 1: < 1 month
  • 2: 1-2 months
  • 3: 2-3 months
  • 4: 4-6 months
  • 5: 6-12 months
  • 6: > 12 months
For effort, you can use values between 1-10 to represent more granular estimates.
Why use RICE?
RICE helps teams objectively prioritize features or projects by considering multiple factors. It balances the potential value of an initiative with the effort required, helping teams focus on high-impact, efficient work.
How do I interpret the RICE score?
A higher RICE score indicates that a feature or project should be prioritized. Compare RICE scores across different initiatives to determine their relative priority. The higher the score, the higher the priority.
Is RICE only for product management?
While RICE is commonly used in product management, it can be applied to various fields where prioritization of initiatives is necessary, such as marketing campaigns, business strategies, or even personal projects.
How often should I recalculate RICE scores?
It's a good practice to recalculate RICE scores periodically, especially when there are significant changes in your product, market, or team capacity. Some teams do this quarterly or during major planning sessions.
Can RICE be used alongside other prioritization methods?
Yes, RICE can be used in conjunction with other methods. For example, you might use RICE to prioritize within a specific category of features that was identified using another method like MoSCoW or Kano.
What are some limitations of RICE?
RICE relies on estimates, which can be subjective or inaccurate. It also doesn't account for strategic alignment or dependencies between items. It's important to use RICE as a guide rather than a strict rule, and to combine it with other considerations and expert judgment.
How can I ensure consistent scoring across a team?
Consistent scoring can be achieved through team discussions, creating detailed guidelines for each parameter, and periodically calibrating scores as a group. It's also helpful to use historical data to inform estimates when possible.

Prioritization library

Choose a method, calculate a score, then apply it

This library connects every ProductLift prioritization guide, calculator, template, and product workflow. Start with the framework guide if you do not know which method fits your decision.

Learn the prioritization frameworks Prioritize inside ProductLift

Start and compare

Pick the right route before committing to a scoring model.

RICE

Balance reach, impact, confidence, and effort.

ICE

Score ideas quickly with impact, confidence, and ease.

MoSCoW

Classify requirements by what a release must, should, could, or will not include.

Other methods and tools

Use a visual matrix or a specialized model when simple scoring is not enough.