What does RICE stand for in product management?

RICE stands for Reach, Impact, Confidence, and Effort. It's a prioritization scoring framework developed at Intercom that helps product managers rank features and initiatives by calculating a score: (Reach × Impact × Confidence) ÷ Effort.

How do you calculate a RICE score?

Multiply the number of people reached per quarter (Reach) by the impact level (0.25 to 3), by confidence percentage (as a decimal, e.g., 0.8 for 80%), then divide by the estimated effort in person-months. Higher scores indicate higher priority.

When should you use RICE vs. other prioritization frameworks?

Use RICE when you have quantitative data on reach and impact and need to compare many features objectively. Use MoSCoW for stakeholder alignment sessions, ICE for quick estimates, and Kano for understanding customer satisfaction drivers.

What is a good RICE score?

RICE scores are relative, not absolute — a 'good' score depends on your other initiatives. Compare scores within the same product context. A feature with a score of 10 is twice the priority of one scoring 5, but scores aren't meaningful in isolation.

RICE Prioritization Framework

Quick Answer (TL;DR)

RICE is a prioritization framework that scores features using four factors: Reach (how many users are affected), Impact (how much each user is affected), Confidence (how sure you are of your estimates), and Effort (how much work it takes). The formula is (Reach x Impact x Confidence) / Effort = RICE Score. Higher scores indicate higher priority. It was popularized by Intercom and is one of the most widely adopted quantitative prioritization methods in product management.

What Is the RICE Prioritization Framework?

The RICE framework is a scoring model that helps product teams make objective decisions about which features, projects, or initiatives to pursue. Developed and popularized by Sean McAllister at Intercom, RICE replaces gut-feel prioritization with a structured, repeatable formula that considers both the potential upside and the cost of each initiative.

RICE stands for:

Reach -- How many people will this impact in a given time period?

Impact -- How much will it impact each person?

Confidence -- How confident are you in your estimates?

Effort -- How much time and resources will it take?

The beauty of RICE lies in its simplicity. By reducing prioritization to a single numerical score, it gives teams a common language for comparing wildly different initiatives -- from a small UX tweak to a major platform overhaul.

The RICE Formula Explained

The core formula is straightforward:

RICE Score = (Reach x Impact x Confidence) / Effort

Let's break down each component with precise definitions so your team scores consistently.

Reach

Reach measures how many users or customers will be affected by an initiative within a defined time period (typically one quarter). Use real data wherever possible.

How to estimate Reach:

Pull from product analytics: DAU/MAU data, funnel conversion rates, segment sizes

Use customer support ticket volume for pain-point-driven features

Reference market research for new-market initiatives

Examples:

Initiative	Reach Estimate	Source
Redesign onboarding flow	5,000 new signups/quarter	Signup analytics
Add CSV export	800 users requesting/quarter	Support tickets + feature requests
Mobile app push notifications	12,000 active mobile users/quarter	Mobile analytics
Enterprise SSO integration	50 enterprise accounts/quarter	Sales pipeline

Always express Reach as a number of people or accounts per time period. Avoid vague terms like "a lot" or "most users."

Impact

Impact measures how much this initiative will move the needle for each person reached. Since individual impact is harder to quantify than reach, RICE uses a standardized scale:

Score	Label	Meaning
3	Massive	Transforms the user experience or eliminates a critical blocker
2	High	Significant improvement that meaningfully changes behavior
1	Medium	Noticeable improvement
0.5	Low	Minor improvement
0.25	Minimal	Barely noticeable

Guidelines for scoring Impact:

3 (Massive): Slack adding threaded messages -- it fundamentally changed how teams communicated and reduced noise in channels.

2 (High): Spotify adding offline downloads -- a significant feature that changed user behavior and drove subscriptions.

1 (Medium): Adding keyboard shortcuts to an existing workflow -- helpful, used regularly, but not transformative.

0.5 (Low): A tooltip that clarifies a confusing label.

0.25 (Minimal): A color change on a non-critical UI element.

Tie Impact to a specific metric you're trying to move: activation rate, retention, NPS, revenue, or time-to-value.

Confidence

Confidence is a percentage that reflects how sure you are about your Reach and Impact estimates. This is the factor that keeps teams honest -- it penalizes wishful thinking.

Score	Label	Criteria
100%	High	Backed by quantitative data (analytics, A/B test results, large sample research)
80%	Medium	Supported by qualitative data (user interviews, surveys, competitive analysis)
50%	Low	Based on intuition, anecdotal feedback, or very small sample sizes

Rules of thumb:

If you have strong analytics data supporting both Reach and Impact, use 100%.

If you have user interviews or survey data but limited quantitative evidence, use 80%.

If you're largely guessing based on gut instinct or a single customer request, use 50%.

Never go below 50%. If your confidence is lower than 50%, you need to do more research before scoring -- not just assign a low confidence number.

Effort

Effort is measured in person-months (or person-weeks, or story points -- just be consistent across all initiatives). This is the total effort across all disciplines: engineering, design, QA, data science, marketing, and anything else required.

How to estimate Effort:

Break initiatives into rough work packages

Get time estimates from each discipline involved

Include QA, documentation, and rollout effort

Round up to account for unknowns

Examples:

Initiative	Engineering	Design	QA	Total Effort
Redesign onboarding	2 months	1 month	0.5 months	3.5 person-months
CSV export	0.5 months	0.25 months	0.25 months	1 person-month
Push notifications	1.5 months	0.5 months	0.5 months	2.5 person-months
Enterprise SSO	3 months	0.5 months	1 month	4.5 person-months

Step-by-Step: How to Run a RICE Scoring Session

Step 1: Prepare Your Candidate List

Gather all features, projects, and initiatives being considered. Aim for 10-25 items -- too few and you don't need a framework, too many and the session becomes exhausting.

Step 2: Align on Definitions

Before scoring, ensure everyone agrees on:

The time period for Reach (usually one quarter)

The unit of measurement for Effort (person-months is standard)

The metric that Impact is measured against (activation, retention, revenue, etc.)

The confidence thresholds and what evidence is required for each level

Step 3: Score Each Initiative

Work through each initiative as a team. For each one:

State the initiative clearly

Discuss and agree on Reach (use data, not opinion)

Discuss and agree on Impact (reference the 3/2/1/0.5/0.25 scale)

Discuss and agree on Confidence (what evidence do you have?)

Discuss and agree on Effort (get input from engineering and design leads)

Calculate the RICE score

Step 4: Rank and Discuss

Sort all initiatives by RICE score from highest to lowest. Then have a critical discussion:

Do the top items align with your strategy?

Are there any surprises in the ranking?

Do any scores feel wrong? If so, revisit the individual components.

Step 5: Make Decisions

Use the RICE scores as a strong input to your prioritization, not the final word. Adjust for strategic considerations, dependencies, and team capacity.

Real-World RICE Scoring Example

Imagine you're a product manager at a B2B SaaS company with 10,000 active users. Your team has four initiatives to compare:

Initiative	Reach	Impact	Confidence	Effort	RICE Score
Smart search with filters	6,000/quarter	2 (High)	80%	3 person-months	3,200
Bulk action toolbar	4,000/quarter	1 (Medium)	100%	1 person-month	4,000
Dashboard customization	8,000/quarter	1 (Medium)	50%	4 person-months	1,000
Slack integration	2,000/quarter	2 (High)	80%	2 person-months	1,600

Calculations:

Smart search: (6,000 x 2 x 0.8) / 3 = 3,200

Bulk action toolbar: (4,000 x 1 x 1.0) / 1 = 4,000

Dashboard customization: (8,000 x 1 x 0.5) / 4 = 1,000

Slack integration: (2,000 x 2 x 0.8) / 2 = 1,600

The bulk action toolbar wins despite having lower reach and impact than some alternatives because it's fast to build and the team has high confidence in the estimates. Dashboard customization, despite reaching the most users, ranks last because the low confidence score and high effort drag it down.

When to Use RICE (and When Not To)

RICE Works Best When:

You have a large backlog of competing features and need a structured way to compare them

Your team tends toward opinion-based prioritization and needs a more objective framework

You have access to product analytics and customer data to inform Reach and Impact estimates

You're prioritizing within a single product where reach and effort are comparable across initiatives

RICE Is Less Effective When:

You're working on a brand-new product with no user data (Reach and Impact become pure guesses)

The initiatives are vastly different in nature (comparing a bug fix to a new product line doesn't produce meaningful scores)

Strategic alignment matters more than incremental optimization (RICE doesn't account for vision or market positioning)

You need to factor in risk, urgency, or dependencies that RICE doesn't capture

RICE vs. Other Prioritization Frameworks

Factor	RICE	MoSCoW	Weighted Scoring	Kano Model	Value vs. Effort
Quantitative	Yes	No	Yes	Partially	Partially
Accounts for reach	Yes	No	Optional	No	No
Accounts for confidence	Yes	No	No	No	No
Ease of use	Medium	Easy	Medium	Hard	Easy
Best for	Feature backlogs	Release planning	Complex criteria	Customer delight	Quick triage
Stakeholder buy-in	High (data-driven)	High (simple)	Medium	Low	Medium
Handles strategic alignment	No	Somewhat	Yes (custom criteria)	No	No

Common Mistakes and Pitfalls

1. Inflating Impact Scores

Teams consistently overestimate Impact because they're emotionally attached to their ideas. Combat this by requiring a written justification for any Impact score of 2 or 3, tied to a specific metric and evidence.

2. Ignoring the Confidence Factor

Some teams set Confidence to 100% for everything, which defeats the purpose. Enforce the rule: if you don't have quantitative data, you can't score above 80%. If you don't have qualitative data, you can't score above 50%.

3. Inconsistent Effort Estimates

One team measures Effort in story points, another in weeks, another in "t-shirt sizes." Pick one unit and stick with it. Person-months is the most universally understood.

4. Scoring in a Vacuum

Never let one person score all initiatives alone. RICE works best when engineers estimate Effort, data analysts inform Reach, and product managers calibrate Impact. Cross-functional input reduces bias.

5. Treating RICE Scores as Gospel

The score is an input to your decision, not the decision itself. A feature with a RICE score of 500 might still be the right thing to build if it's strategically critical. Use RICE to inform, not to dictate.

6. Not Revisiting Scores

Conditions change. A feature you scored six months ago may have very different Reach, Impact, or Effort numbers today. Re-score your top candidates at the start of each planning cycle.

Best Practices for RICE Implementation

Calibrate as a Team

Before your first scoring session, score 3-5 past features that have already shipped. Compare the predicted RICE scores to actual outcomes. This calibration exercise helps the team develop shared intuitions for what "Impact: 2" or "Reach: 5,000" actually means.

Document Your Assumptions

For every initiative, record why you chose each score. "Reach: 6,000 because our funnel shows 6,000 users hit the search page per quarter" is far more valuable than just "6,000." When you revisit scores later, you'll know whether the assumptions still hold.

Use a Spreadsheet or Tool

RICE scoring is best done in a shared spreadsheet or purpose-built tool like IdeaPlan where everyone can see the inputs, challenge assumptions, and track scores over time. Transparency builds trust in the process.

Set a Minimum Confidence Threshold

Establish a rule: no initiative with Confidence below 50% goes into the final ranking. Instead, those items go onto a "research needed" list. This creates a healthy pipeline where discovery work feeds into prioritization.

Combine RICE with Strategic Themes

RICE optimizes for incremental value. To ensure you're also investing in long-term bets, layer strategic themes on top: allocate 70% of capacity to high-RICE items and 30% to strategic initiatives that might not score well on RICE but are critical for your long-term vision.

Review and Iterate

After shipping a high-scoring feature, compare predicted Reach and Impact against actual results. Did 6,000 users really use the new search? Did activation increase as expected? This feedback loop makes your future RICE estimates more accurate over time.

Getting Started with RICE Today

Pick your top 10-15 backlog items that are candidates for the next quarter

Gather data on user counts, support tickets, and usage patterns for each

Schedule a 90-minute session with your PM, engineering lead, designer, and data analyst

Walk through each initiative using the RICE components

Rank by score and discuss whether the ranking aligns with your strategy

Commit to a plan and document your reasoning

RICE won't solve every prioritization challenge, but it will give your team a shared vocabulary and a repeatable process for making better decisions. The framework's real power isn't in the formula itself -- it's in the structured conversations it forces your team to have about reach, impact, confidence, and effort.

RICE Prioritization Framework: The Complete Guide to Scoring and Ranking Features