As a Product Manager, backlog prioritization can be a massive headache as you have to worry about multiple competing features, projects, and initiatives.
This is where the RICE score model comes in handy. It is a prioritization framework designed specifically for Product Managers to objectively evaluate and decide what goes on your product roadmap first
RICE framework analyzes potential projects based on reach, impact, confidence, and effort. By quantifying these key factors, RICE helps you score and compare features to build data-driven product roadmaps.
This guide walks you through the RICE prioritization framework, and how to implement it in your projects. We’ll also provide situations where it is best used, as well as an example of it being used.
At the end of the article, you will be ready to prioritize your backlog using this simple but effective prioritization framework.
What is the RICE Score Model?
The RICE score model is a framework for Product Managers to help prioritize features and initiatives.
RICE stands for:
- Reach
- Impact
- Confidence
- Effort
These are the four factors used to evaluate and score potential projects.
Reach
Reach refers to the number of users that a feature or project will affect in a given timeframe.
To estimate reach, think about how many of your users will encounter or use the feature within a set period. For example, you might have 300 users in a month.
A higher reach implies broader exposure and more users benefiting from the feature.
Impact
Impact represents the value a feature is expected to deliver, such as its potential effect on user satisfaction, retention, or revenue.
You can estimate the impact on a relative scale like:
- Minimal = 0.25
- Low = 0.5
- Medium = 1
- High = 2
- Massive = 3
The higher the expected impact, the higher this factor in the RICE score.
Confidence
Confidence indicates the certainty of estimates for reach and impact and is scored as a percentage:
- 100% = High confidence
- 80% = Medium confidence
- 50% = Low confidence
Higher confidence increases the overall RICE score.
Effort
Effort measures the required resources and work needed to complete the feature, typically in time units like person-months. The more effort a feature needs, the lower its RICE score.
By comparing RICE scores, you can prioritize your backlog and product roadmap based on reach, impact, confidence, and effort.
RICE Prioritization Formula
The RICE prioritization formula is straightforward:
RICE Score = (Reach * Impact * Confidence) / Effort
Let’s break it down:
- Reach is quantified as the number of users affected
- The impact is scored on a relative scale of 1-5
- Confidence is a percentage
- Effort is the estimated time/resources required
You’ll first multiply the Reach, Impact, and Confidence scores. This gives you a sense of the potential overall benefits of the feature.
Then you divide that product by the Effort score. This accounts for the required resources and “costs” to implement the feature.
The result is a normalized RICE score you can use to compare multiple features.
Higher RICE scores indicate:
- Broad reach
- High potential impact
- Confidence in estimates
- Lower required effort
Features with higher RICE scores generally should receive higher priority on your product roadmap.
The RICE prioritization formula lets you quantify the key factors to make data-driven prioritization decisions rather than using your gut.
After calculating the RICE scores, you can then rank your list of features from the highest to lowest score to enable you to begin tackling the projects or features with maximum potential value and minimum effort first.
RICE Prioritization Example: How to Calculate the RICE Score
To better understand the RICE framework for prioritization, Let’s walk through an example of calculating RICE scores for sample features.
Imagine you’re a Product Manager prioritizing your Product Backlog and roadmap and have identified 3 potential features:
- Feature A: Add social sharing buttons
- Feature B: Improve search relevance
- Feature C: Automate reporting
To prioritize these items using the RICE framework, follow these steps:
1. Estimate Reach
First, estimate the reach for each feature – how many users will it impact per month?
- Feature A: Reach = 350 users/month
- Feature B: Reach = 2,500 users/month
- Feature C: Reach = 1,200 users/month
2. Score Impact
Next, rate the expected impact of each feature on a 1-5 scale:
- Feature A: Impact = 2
- Feature B: Impact = 4
- Feature C: Impact = 3
3. Evaluate Confidence
Now assess your confidence in the Reach and Impact estimates as percentages:
- Feature A: Confidence = 50%
- Feature B: Confidence = 90%
- Feature C: Confidence = 80%
4. Estimate Effort
Finally, quantify the implementation effort required per feature in time units:
- Feature A: Effort = 60 person-hours
- Feature B: Effort = 90 person-hours
- Feature C: Effort = 120 person-hours
5. Calculate RICE Scores
With those factors estimated, you can now calculate the RICE score for each feature:
Feature A:
RICE = (350 * 2 * 50%) / 60 = 11.7
Feature B:
RICE = (2500 * 4 * 90%) / 90 = 100
Feature C:
RICE = (1200 * 3 * 80%) / 120 = 24
Feature B has the highest RICE score, so it should receive the highest priority. By quantifying these key factors, you now have an objective way to compare features and make data-driven prioritization decisions.
How to use the RICE Framework for Prioritization (Step-by-Step Guide)
The RICE score model provides a structured process to guide your Product Backlog prioritization and roadmapping driving decisions with real user and business impact data.
Here is a step-by-step guide to using the RICE framework effectively:
1. Start with a List of Features
First, compile a list of potential features, projects, and enhancements you are considering for your product roadmap. This can include both new ideas as well as existing backlog items.
Make sure to gather input from different stakeholders like your team, executives, customers, sales, and support. Crowdsource ideas from across your organization.
2. Define the Factors
Next, ensure your team aligns on how each of the RICE factors will be defined and measured:
- Reach: How will you quantify the number of users impacted? Number of customers? Transactions? Page views? Choose a metric fitting your product.
- Impact: Decide on the tiers for scoring impact (e.g. 1-5 scale). What does each level represent? How will you estimate?
- Confidence: Are you using percentages for confidence level? Make sure everyone understands what 100% vs. 50% means.
- Effort: Will you estimate in time units like person-hours or person-months? However you measure, align on the approach.
3. Estimate and Score Each Factor
With the factors defined, analyze each potential feature and estimate its Reach, Impact, Confidence, and Effort scores based on the criteria you set.
Be data-driven here rather than relying on hunches. Leverage usage analytics, customer feedback, and past priorities to inform your analysis.
4. Calculate RICE Scores
Now crunch the numbers. For each feature, multiply Reach * Impact * Confidence, then divide by Effort per the RICE formula.
This quantifies the potential customer value delivered proportional to the implementation effort required.
5. Rank by RICE Score
Sort your list of features by RICE score from highest to lowest. The higher the RICE score, the higher the priority that item should receive for Product and Engineering.
This ranked list of scored features provides a solid data-backed way to sequence your roadmap priorities.
6. Gut Check with the Team
Review the ranked feature list with your team and stakeholders.
Discuss any items that seem potentially mis-scored and re-evaluate factors where needed to get confidence the scoring makes sense.
7. Decide on Timeframes
Finally, decide which features you can deliver in upcoming roadmap timeframes based on your ranked priority list.
It’s unlikely you can build every item in the next quarter or two, so use the RICE ranking to choose what gets scheduled and when.
8. Continuously Re-prioritize
Regularly re-run this prioritization process as new ideas and context emerge.
Re-scoring and re-ranking features help dynamically shift priorities in line with the latest customer data and business objectives.
When to Use the RICE Prioritization Method
As a Product Manager, when should you apply the RICE model for backlog prioritization? The key is matching your situation to the right prioritization method
Here are good situations to leverage RICE:
You’re Comparing Diverse Features
If you need to evaluate very different potential features, RICE allows quantifying their value in a consistent way as the factors make it possible to compare “apples to oranges”.
You Have Usage Data Available
Since RICE relies on estimating reach and impact, it works best when you have product analytics to inform those scores as the more customer data you have, the more accurate RICE scores will be.
You’re Facing Resource Constraints
If your engineering team is overloaded, using the RICE framework ensures you prioritize the most valuable features as it helps maximize return on limited development resources.
You Need Objective Prioritization
RICE framework reduces individual biases and agendas during roadmapping. The formula emphasizes data over opinions for impartial decision-making.
You Want Data-Backed Reasoning
Compared to other prioritization frameworks, the RICE framework provides clear quantitative scores. This data-driven approach helps justify priorities to executives and stakeholders.
You Have a Mature Product
RICE works best for established products with a customer base. For brand new products, other frameworks like the ICE scoring model allow more speculative scoring.
Pros of the RICE Prioritization Framework
There are a ton of key benefits of using the RICE model for prioritization. These include:
Provides Structured Decision-Making
The RICE framework gives Product Managers a standardized, consistent process for evaluating features. This structured approach promotes a more objective analysis rather than relying on gut feelings alone.
Simple and Understandable
With just 4 factors to consider, the RICE framework is straightforward to comprehend and apply. The simplicity makes it accessible even for those without formal business training.
Accommodates Diverse Features
By scoring based on reach and impact, the RICE prioritization framework can compare very different features on an “apples to oranges” basis. The flexibility makes it widely applicable.
Drives Data-Based Choices
The RICE model reduces individual biases by grounding analysis in customer data and usage metrics. This emphasizes objective data over opinions and agendas.
Provides Clear Priorities
The final RICE score quantifies feature value allowing easy ranking. Higher scores clearly indicate higher priorities to sequence the roadmap.
Promotes Alignment
The RICE model provides a shared framework across product, engineering, and stakeholders which facilitates alignment on priorities driven by consistent data-based scores.
Identifies Unknowns
Low confidence scores highlight uncertainty in data estimates showing where teams need to invest more in research and metrics to raise confidence.
Focuses on Key Drivers
By isolating key inputs like reach and impact, the RICE framework focuses evaluation on the core factors that should drive sound product decisions and strategy.
Cons of the RICE Framework for Prioritization
Despite its many benefits, the RICE model comes with its fair share of limitations to consider:
Subjective Scoring
Despite aiming for objectivity, the RICE model still requires subjective opinions when scoring factors like impact and effort. These individual biases can skew outcomes.
Not Directly Customer-Driven
RICE doesn’t require input directly from customers as teams could theoretically RICE score features without any customer data or feedback.
Effort Estimates Prone to Error
Effort accounting is notoriously difficult. Under or overestimating implementation effort can significantly skew RICE scores.
Favors Immediate Impact
The RICE framework tends to prioritize features with clear near-term impact vs longer-term strategic initiatives since their impact is immediate.
Rewards Broad Appeal
Since reach is quantified by the number of users affected, the RICE model biases towards features applicable to large segments rather than niche needs.
Technical Debt Not Considered
RICE focuses on end-user features, whereas important tech infrastructure and technical debt work are poorly addressed since it has minimal direct reach/impact.
Gaming Potential
RICE relies on team member estimates, which opens the door for office politics and the manipulation of scores by those with agendas/incentives.
Ongoing Maintenance
To stay relevant, RICE scoring must be redone regularly as priorities/context evolves. This recurring rescoring adds overhead for product teams.
RICE Framework Alternatives
While the RICE model is a powerful prioritization technique that strikes the right balance of simplicity, objectivity, and comprehensive criteria coverage, it’s not the only viable framework that you can use.
Every model has its pros and cons, and you need to evaluate which aligns best with your team’s context and needs. Here are some alternative options to consider:
Weighted Shortest Job First (WSJF)
Like RICE, WSJF involves scoring features on multiple factors. But you can customize the specific factors beyond reach, impact, etc. Typical factors include:
- Business value: Revenue potential, cost savings, etc.
- Customer value: Satisfaction, retention, reduced pain points
- Risk: Feasibility, technology constraints
- Cost: Development resources required
Each factor gets a customized weight based on importance. Features are scored on each dimension and then ranked by the weighted total. You can learn how to calculate WSJF here.
Kano Model Prioritization
The Kano model is a prioritization framework that categorizes features based on how they affect customer satisfaction:
- Thresholds: Must have or users will be dissatisfied
- Performance: Linear satisfaction improvement
- Exciters: Unexpected delighted
Teams target thresholds first to meet basic needs, then performance and exciters to go beyond.
MoSCoW Method
The MoSCoW method separates features into 4 priority groups:
- Must Have: Critical features
- Should Have: Important but lower priority
- Could Have: Nice-to-have enhancements
- Won’t Have: Not planned for the current release
This explicit grouping provides focus on what’s essential versus discretionary.
Opportunity Scoring
This framework scores features based on:
- Opportunity: Business value potential
- Confidence: In estimates and ability to deliver
- Cost: Resources required
It combines value and cost like RICE but is more flexible in the factors used.
Buy-a-Feature Prioritization Model
In this model, stakeholders get “money” to spend bidding on which features get built. The amount bid becomes the prioritization score.
This makes priorities explicitly clear based on who is willing to “pay” the most for each feature.
Outcome-Based Roadmapping
Rather than features, this framework prioritizes by starting with ideal customer and business outcomes and then identifying capabilities to drive those outcomes.
User Story Mapping
User story mapping visualizes features on a 2×2 matrix showing the user journey vs implementation timeframe. This helps to showcase how they fit in the user experience overall, as well as identify logical sequencing and dependencies.
Conclusion
The RICE framework provides an intuitive yet robust model for prioritizing complex backlogs for Product Managers in Agile development.
It is a consistent, impartial way to rank your priorities enabling sound roadmapping, Sprint Planning, and backlog refinement decisions grounded in metrics
Its transparent scoring fosters alignment and shared understanding across cross-functional teams.
While no framework is perfect, RICE strikes a pragmatic balance between simplicity and comprehensive criteria coverage.
For most teams, RICE serves as an invaluable tool for systematically maximizing the business value delivered through effective requirements prioritization and release planning.
