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Every product team faces a common problem: too many features to build, and too little time.
Prioritization is a never-ending challenge when building a product roadmap.
Everyone has different opinions on what to build next. Sales want new features, customers want improvements, and developers need to deal with technical issues.
Without a clear way to decide what’s most important, teams get stuck in endless discussions or work on the wrong things.
Prioritization helps you align your product development efforts with your broader business objectives. Given that every product team has limited resources, prioritization ensures that these resources are allocated to the most impactful features.
Now to address this challenge, product managers use different prioritization methods, one of which is the RICE scoring framework.
It’s a prioritization framework specifically tailored for product management. RICE framework is a simple method that helps product managers prioritize different project features and specifications based on four key factors.
In this article, we’ll talk about the RICE framework in detail, along with how it’s calculated, why you should use it, and what benefits it brings.
What is the RICE Prioritization Method?
The RICE prioritization framework is a scoring system that helps product teams evaluate and prioritize projects, tasks, or features based on four key factors: Reach, Impact, Confidence, and Effort. Each factor contributes to a numerical score that makes it easier to rank and prioritize potential features objectively.
This framework was developed by Sean McBride, a former product manager at Intercom. After struggling with subjective prioritization methods, McBride created RICE to bring more structure and objectivity to product decisions.
McBride later shared this methodology in an article that gained widespread adoption across the product management community.
The RICE scoring model is one of several methodologies product teams use to prioritize user stories. Other popular frameworks for prioritization include the Kano model, the ICE scoring model, the MoSCoW method, etc.
While these frameworks are really good, the RICE framework takes prioritization a step further by combining all the crucial factors methodologically to give you a better picture of your product roadmap objectively.
Why should you use the RICE Score Framework?
The RICE framework brings clarity and consistency to product prioritization, moving teams away from gut feelings and HiPPO (Highest Paid Person’s Opinion) decision-making.
It offers several benefits for product managers and development teams:
- Data-Driven Decision-Making: Instead of relying on subjective opinions, RICE uses quantifiable metrics and provides a structured, data-driven approach to prioritization. This makes it easier to align stakeholders around a common understanding.
- Balanced Evaluation: By considering both potential impact and required effort, RICE helps teams identify the “quick wins” and avoid investing in low-value features. The confidence factor adds a layer of risk assessment to the decision-making process.
- Resource Optimization: By considering effort and feasibility, it helps allocate resources efficiently to projects with the highest potential return.
- Better Team Alignment: Having a structured approach not only reduces debates but also helps product managers focus on gathering data rather than arguing about priorities.
- Scalability: Whether you’re evaluating small feature updates or major product initiatives, RICE analysis provides a consistent framework that can scale with your team’s needs. It works equally well for B2B and B2C products.
The RICE framework assesses the business criticality and effort needed to deliver a specific project request. It helps prioritize projects based on what is most important and which ones are least important.
4 Key Factors of the RICE Score
RICE is an acronym for the four key factors: Reach, Impact, Confidence, and Effort. Each of these factors is assigned a numerical score that is used to evaluate the potential value of a user story or theme.
Let’s look at each factor in detail.
- Reach
Reach measures the number of people or events your initiative will affect within a specific timeframe (usually per quarter). It quantifies the scale of impact and measures how widely the feature will be used or how many users will benefit from it.
This can be determined by looking at user demographics, usage data, or market size.
For example, if a product feature would affect 3,000 users per quarter, your Reach score is 3,000. Let’s say you estimate a new product feature will bring in 1,000 new prospects to your SaaS signup page within the next month, and 20% of those prospects will sign up. Your Reach score is 200.
- Impact
Impact evaluates how much your initiative will contribute to the desired outcome for each person reached. It assesses the potential impact of a project or feature on the target audience.
It measures how significant the change or improvement will be in terms of customer satisfaction, revenue generation, and strategic alignment.
Intercom suggests using a multiple-choice scale to maintain consistency:
- Massive impact = 3
- High impact = 2
- Medium impact = 1
- Low impact = 0.5
- Minimal impact = 0.25
Impact is scored on a scale of 1 to 10, with 1 representing minimal impact and 10 indicating a feature that could have a significant impact.
- Confidence
Confidence represents how confident you are about your estimates of reach and impact. It helps account for uncertainty and risk in your predictions. The score reflects your ability to deliver the feature or improvement within a given timeframe.
It is scored as a percentage, ranging from 1% to 100%.
- High confidence (100%) – when you have solid historical data
- Medium confidence (80%) – when you have some data but you’re making a few assumptions
- Low confidence (50%) – when you’re trying to deal with new, untested ideas
- Anything below 50% suggests a lack of research
For example, if you’re basing your estimates on clear customer feedback and usage data, you might assign a 100% confidence score. For a new initiative with limited data, the score could be 50%.
- Effort
Effort estimates the total time investment needed from all team members involved in the project: product, design, and engineering. It answers this crucial question: “How much effort will it take to deliver this story?”
It considers factors such as development time, technical expertise, potential risks, and dependencies. It’s the effort that needs to be put into a project for its completion.
Effort is typically scored in person-months or person-weeks, depending on your preferred unit of measurement. For instance, if a simple feature would take one developer to deliver in two weeks, the effort score is 0.5 person-months.
On the other hand, a complex feature that requires:
- Two developers for one month (2 person-months)
- One designer for two weeks (0.5 person-months)
- One QA engineer for one week (0.25 person-months)
- Total effort is 2.75 person-months
Remember that effort should include all work required. Be realistic and include buffer time for unexpected challenges.
Please note that since Effort is a divisor in the RICE score formula, a smaller effort score will result in a higher RICE score, which reflects better resource efficiency.
How is a RICE score calculated?
Once you’ve assigned values for Reach, Impact, Confidence, and Effort to each project or feature, you can calculate the RICE score using a simple formula that combines all four factors:
RICE score = (Reach × Impact × Confidence) / Effort
By multiplying the first three factors and dividing by effort, RICE produces a comparable score that helps teams prioritize initiatives more objectively. Higher scores indicate better use of resources relative to potential impact.
While the RICE score provides a structured framework for decision-making, it still allows teams to incorporate both quantitative data and qualitative insights into their prioritization process.
Here’s a practical example:
Let’s say you’re evaluating a new feature for your product.
Reach = 500 users per quarter
Impact = 2.0 (High impact)
Confidence = 80% (0.8)
Effort = 2 person-months
Now, RICE score = (500 × 2 × 0.8) / 2 = 400
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This template includes:
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- Example scenarios for reference
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- Comments explaining each component
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Download Our FREE
RICE Prioritization Framework
Template!
- Streamline decision-making with a proven scoring system
- Prioritize your projects with data-driven confidence
- Save time and focus on what drives the highest impact
Examples of using a RICE prioritization framework
When building a SaaS product, teams often find themselves juggling between tasks such as adding new features, improving existing ones, and running experiments to optimize user experience.
Here’s how RICE score can help prioritize these different initiatives:
Product Feature Prioritization
- In-app Onboarding Flow
Reach = 2,000 new users per quarter entering the onboarding flow.
Impact = 3.0 (Massive) – Data shows that improved feature adoption directly correlates with higher activation rates and reduced early churn.
Confidence = 90% (0.9) – High confidence based on extensive user interviews, support tickets, and session recordings showing users struggling with current onboarding.
Effort = 3 person-months – Requires UX research, design iterations, development work, and content creation.
RICE score = (2,000 × 3 × 0.9) / 3 = 1,800
- Team Collaboration Features
Reach = 5,000 users per quarter across all team accounts
Impact = 2.0 (High) – Will significantly improve workflow for teams, leading to higher engagement. Current data shows team accounts are 3x more likely to upgrade.
Confidence = 100% (1.0) – Very high confidence based on direct customer feedback and competitor analysis.
Effort = 2 person-months – Includes building shared workspaces, permissions system, and activity tracking.
RICE score = (5,000 × 2 × 1) / 2 = 5,000
- API Integration System
Reach = 1,000 enterprise users per quarter who have requested API access
Impact = 3.0 (Massive) – Opens up enterprise opportunities and allows for deeper platform integration. Could lead to 50% higher contract values.
Confidence = 80% (0.8) – Good confidence score based on market research and enterprise customer reviews
Effort = 4 person-months – Complex project running API design, documentation, security implementation, and SDK development
RICE score = (1,000 × 3 × 0.8) / 4 = 600
Experiment Prioritization
- New User Dashboard Layout
Reach = 8,000 active users per quarter using the dashboard
Impact = 1.0 (Medium) – Expected to improve feature discovery and daily active usage. Heat mapping shows that 40% of dashboard features are currently overlooked.
Confidence = 70% (0.7) – Moderate confidence based on usability testing and prototype feedback
Effort = 0.5 person-months – Primarily involves frontend changes and can be A/B tested easily
RICE score = (8,000 × 1 × 0.7) / 0.5 = 11,200
- Upgrade Flow Optimization
Reach = 1,500 users per quarter visiting pricing range
Impact = 2.0 (High) – Directly impacts SaaS conversion funnel. Similar optimizations improved conversions by 25% in past tests.
Confidence = 90% (0.9) – High confidence based on previous A/B tests and clear friction points in analytics
Effort = 0.25 person-months – Simple changes to pricing page layout and signup flow
RICE score = (1,500 × 2 × 0.9) / 0.25 = 10,800
Based on these calculations above, the team should prioritize testing the new dashboard layout, followed by upgrade flow optimization. For larger features, the team collaboration tools score highest, which suggests they should be the priority for the development team.
This demonstrates how RICE helps compare both major features and optimization experiments on the same scale, while considering user activation, team collaboration, and conversion optimization.
Key Mistakes to Watch Out For
While the RICE model is a powerful product prioritization framework, product teams often encounter several pitfalls when implementing it for the first time. Being aware of these common mistakes can help you use the RICE framework more effectively.
- Overcomplicating the scoring process
Teams sometimes get stuck trying to be too precise with their numbers. Remember that the RICE framework is meant to be a relative scoring system, not an exact science.
What can you do?
Use rough estimates and ranges when exact numbers aren’t available.
- Ignoring customer segments
When calculating and assessing the Reach score, teams often use total user numbers while they completely ignore which user segments matter most. A feature that reaches 1,000 enterprise customers might be more valuable than the one that reaches 10,000 free users.
What can you do?
Try segmenting your Reach calculations based on customer value or strategic importance.
- Underestimating Effort
Many product teams focus solely on development time when calculating Effort. They forget to consider design, testing, documentation, and maintenance costs. This is a mistake.
What can you do?
Include all resources required to ship and maintain the feature, including potential technical debt and long-term support needs. Consider integrating with your existing stack of SaaS analytics tools to measure success.
- Not adjusting Confidence scores based on risk
One of the common mistakes teams make is they sometimes assign high Confidence scores without properly evaluating risk factors involved.
What can you do?
Consider technical complexities, market uncertainties, and implementation challenges when scoring Confidence. If you’re entering a relatively new market or using an unfamiliar technology, your Confidence score should reflect these uncertainties.
- Trusting RICE scores blindly
Some teams trust RICE scores blindly without considering the strategic context. Remember that RICE is a decision-supporting tool, not a decision-making tool.
What can you do?
Factor in business strategy, market timing, and customer commitments into your final prioritization decisions.
- Inconsistent scoring across teams
When multiple teams use the RICE model, they might score similar initiatives differently, which may lead to misaligned priorities.
What can you do?
Establish clear scoring guidelines and calibrate regularly across teams to ensure consistency in how different groups apply the framework.
When evaluating initiatives, it’s helpful to use frameworks like the LIFT CRO Model alongside RICE scoring to ensure a comprehensive analysis of each feature’s potential impact.
Final Takeaways on RICE Scoring
In a nutshell, the RICE prioritization framework offers product teams a structured, data-informed approach to making difficult prioritization decisions. By considering Reach, Impact, Confidence, and Effort, you can make informed product decisions.
While the framework isn’t perfect, it provides a common language for discussing and comparing different opportunities.
Whether you’re managing a small product or a complex portfolio, RICE can help align your team around what matters most.
Need help optimizing your product decisions?
At Vakulski-Group, we help SaaS companies make better and more informed product decisions through data-driven prioritization and optimization. Our team of experienced product strategists and CRO specialists can help you identify high-impact opportunities for business growth and optimize your product experience for maximum conversions.
Schedule a free consultation call with our team to learn how we can help you make informed product decisions and drive growth through strategic prioritization.

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Frequently Asked Questions
The RICE model is a powerful product prioritization framework that helps product teams make objective decisions about what to build next. It’s a scoring system that evaluates initiatives based on four factors and produces a single score that can be used to compare and rank different features, projects, or experiments.
The four key factors of the RICE framework are:
1. Reach: The number of users/customers affected in a given timeframe
2. Impact: The effect on individual users
3. Confidence: How certain are you about your estimates (expressed as a percentage)
4. Effort: The total time invested, measured in person-months
Use the RICE framework when:
1. Prioritizing feature requests or product improvements
2. Creating product roadmaps
3. Justifying product decisions to stakeholders
4. Aligning teams around priorities
5. Evaluating different experiments or A/B tests
The RICE score is calculated using the following formula:
RICE score = (Reach × Impact × Confidence) / Effort
For example, if a feature reaches 1,000 users, has a high impact (2.0), 80% (0.8) confidence, and requires 2 person-months of effort, the RICE score would be calculated as:
RICE score = (1,000 × 2 × 0.8) / 2 = 800
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Written By
Ihar Vakulski
With over 8 years of experience working with SaaS, iGaming, and eCommerce companies, Ihar shares expert insights on building and scaling businesses for sustainable growth and success.
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