Blog Product ManagementRICE Score Framework: Definition, Template, and Prioritization Guide

RICE Score Framework: Definition, Template, and Prioritization Guide

Having a backlog full of feature ideas is great, but how do you decide which ones deserve your team's resources the most? The RICE score provides a data-driven framework to prioritize features with the most potential, ensuring your roadmap is strategically aligned with your goals.

Product Management
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Rice score prioritization guide and definitio
💡 Free Rice score prioritization template →

Having a backlog full of feature ideas is great, but it also presents a challenge—how do you decide which ones deserve your team's time, resources, and energy the most? We all know you can’t just go with your gut instincts —that rarely ends well.

Luckily, there are data-driven frameworks to help you pick the features with the most potential. One of the best is the RICE score.

In this guide, we’ll explain the RICE score, how to calculate it, some downsides, and how to use it to prioritize your roadmap. 👇

What is the RICE framework?

The RICE framework is a handy decision-making tool for prioritizing tasks and stands for reach, impact, confidence, and effort.

You assign each factor a score and calculate a RICE score. Then, you can compare the RICE scores of different potential projects to quickly prioritize them. 

It's created by Intercom’s product manager, Sean McBride, and adds ‘reach’ to the traditional ICE framework.

But how do you actually calculate a RICE score? It’s simple—just use this formula:

Rice score forumla
RICE Score = (Reach x Impact x Confidence) / Effort

To make sense of this calculation, let’s break down each factor:

1. Reach

Reach is the number of people your initiative or feature will impact within a specific time frame—per quarter, month, or year. 

Let’s say you want to add a new messaging feature to a mobile app with 50,000 monthly active users. Based on user surveys and historical data, you estimate that 40% of the users will use the new messaging feature. This means the feature's reach is 20,000 users per month.

2. Impact

Impact measures how much the project or feature will affect your users or your business. 

This is usually subjective and is assigned a value on some scale—like this:

  • 5 = Massive impact
  • 4 = High impact
  • 3 = Medium impact
  • 2 = Low impact
  • 1 = Minimal impact

For example, if you’re talking about a feature that almost 80% of beta testers have requested, you’d probably rate it a 5 (massive impact). On the other hand, a minor UI tweak might be rated as a 1 (minimal impact).

You can get as granular as you want with the scale. Just be mindful that more detailed scales require more granular analytics. If your analytics aren’t fine-tuned enough to distinguish between a 20 and a 22 on a 30-point scale, a 30-point scale is pointless.

3. Confidence

Confidence is your certainty about your reach, impact, and effort estimates. It helps account for uncertainty in your predictions. 

We measure confidence as a percentage, ranging from 0-100:

  • >80% (high confidence) 
  • 50% to 80% (medium confidence)
  • >50% (low confidence) 

The higher your confidence, the more certain you are about your estimates. For example, if you used a feature voting tool to estimate reach, you might have a confidence level of 90%. If data is sparse or based on assumptions, your confidence might be around 50%.

Featurebase's public feedback portal.
Let your customers submit feedback and vote on ideas in Featurebase

4. Effort

Finally, effort represents the work required to complete the project or feature. It’s typically measured in person-months (i.e., how many months it would take 1 person to complete a project).

This can be a bit confusing, so here’s a quick example to illustrate:

If it takes your team of 4 developers 1 month to complete a project, then the effort would be 4 person-months (1 month x 4 developers). If it takes your team 6 months to complete a project, then the effort would be 24 person-months (6 months x 4 developers).

How to use RICE (+ relevant examples)

1. Define the project or feature

The first step is to make a list of features you want to evaluate. This could be new features suggested by customers or ones you’ve had in your backlog for a while. The key is to ensure the features are:

  • Feasible—you have the technical expertise to execute them.
  • Aligned with your business goals.
  • In demand by users.

Next, clearly define the goals you want to achieve with each feature. Are you aiming for:

  • More customer retention
  • Higher satisfaction
  • Increased revenue
  • or something else? 

Knowing your goals now will be crucial when evaluating the "impact" in the next step.

2. Assign scores for each factor

By now, you should have a good grasp of each factor, but the tricky part is finding actual numbers to plug in. This requires a bit of research on your end, but a customer feedback tool like Featurebase can make it a lot easier.

Here's how Featurebase can help you fill in each factor:


You can use Featurebase to run customer surveys and collect feedback. Ask your users questions that help you estimate how many people will be affected by the change. It could be a question like: 

  • "We're considering implementing a new feature that allows for [a brief description of the change]. How likely are you to use this feature?
Customer satisfaction surveys in Featurebase.
Example of surveys in Featurebase.

You can collect and prioritize feature requests with Featurebase to see which ones are trending and how many people are asking for them. This will give you an idea of your reach.

And let’s say you eventually estimate your reach to be 5,000 users.

Prioritizing feedback in Featurebase.
Prioritizing feature ideas using votes and user revenue.


Before determining impact, you need a clear idea of what “impact” means to you. This will depend on the goal you've set. Impact can mean all kinds of different things to different kinds of products, including:

  • Revenue
  • Profit
  • User satisfaction
  • NPS

Data collection sources are key here, and Featurebase can again be a huge help.

Let’s say your main focus is revenue. With Featurebase, you can connect your users’ data to customer feedback so you can see exactly the revenue behind each feature idea.

Then, you can use actual revenue as a metric to sort and prioritize ideas. 👇

Illustration of sorting feedback by uvpoter revenue contribution in Featurebase.
Sorting feature requests & bug reports by upvoters' revenue contribution.

For example, if many small customers have required one feature, but a couple of really big ones have another, it might be worth rating the latter's impact higher, like a 4.


Since we’ve been helping you make data-driven decisions so far, you should have a high degree of confidence in your estimates. A confidence rating of 80% isn’t far-fetched—and we’d recommend avoiding features with less than 50%.

Collect more data and come back to the uncertain ones later.


Estimating effort takes a bit of effort

You’ll need to sit down with the people who will actually be completing the work, and reach a consensus. This helps minimize the impact of optimism and pessimism. Use techniques like planning poker to help facilitate this process.

For the sake of our example, let’s assume the effort score we arrive at is 4 person-months.

3. Calculate the RICE score

Now that we have all our numbers, it's time to crunch them and calculate the RICE score using this formula:

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

Let's plug in our numbers:

  • Reach = 5,000
  • Impact = 2
  • Confidence = 80% 
  • Effort = 4 (person-months)

So, our RICE formula is:

  • (5000 × 2 × 0.8)/4 = 2,000

You'll need to repeat this process for every feature or initiative you consider so that you can compare features.

4. Compare and prioritize

The next step is to rank each initiative based on their RICE scores. You can easily do this using a simple spreadsheet like the one provided here:

Rice score template in google sheets.
Get a free Rice score spreadsheet template →

The linked spreadsheet above also automates each calculation for you, so you don’t have to do it manually for all your features.

The basic idea is pretty simple:

Features with high RICE scores typically have a broad reach, significant positive impact, high confidence in estimates, and require a reasonable amount of effort. These heavy hitters deserve a spot at the top of your priority list. Conversely, those with low scores (meaning limited reach with high effort) might not be the best use of your resources.

Don't prioritize features solely based on their RICE, though. Instead, use these scores to start data-driven discussions. Talk about the pros and cons of each feature and how they fit into your overall product strategy. Then, decide on your feature priorities.

Sometimes, a lower-scoring feature might better address a critical user need.

5. Execute

Finally—roll up your sleeves and implement the initiatives based on the priorities you’ve hashed out. 👏

To keep things organized, create a roadmap that visualizes these priorities and closes the feedback loop with users. Featurebase automatically syncs your feature requests and ideas with a roadmap that you can publicly present to your users.

Featurebase's public roadmap feature.
Public roadmap made with Featurebase.

Whenever you update the status of a feature, every upvoted user is automatically emailed a new update. This neat way keeps all relevant users up to date about their requests.

RICE vs. Value-Effort Matrix

The RICE framework might seem like a new concept, but it’s actually a higher-fidelity version of a well-established prioritization framework—the value-effort matrix. This matrix is a more straightforward cost/benefit prioritization tool that considers two key factors:

  • Value: This represents the overall benefit the feature will bring to users and the business (similar to “impact” in the RICE formula).
  • Effort: This reflects the resources (time and money) required to develop and implement the feature. It’s presented as a score on a scale rather than a person-month estimate.

Features are plotted on a matrix based on their perceived value and effort. Here’s an example created with Featurebase:

Featurebase's value/effort prioritization matrix.
Value/Effort Prioritization Matrix (made with Featurebase)

Featurebase makes value-effort analysis a breeze. Simply assign a value and effort score to each feature on your backlog. The result is a straightforward, user-friendly grid like the one shown above, where your priorities are immediately clear:

  1. Easy wins: Low-effort, high-value features
  2. Big bets: High-effort, high-value features
  3. Incremental improvements: Low-effort, low-value features
  4. Avoid: High-effort, low-value features

So, how exactly is RICE different from the value-effort matrix? 

As we mentioned earlier, RICE is a higher-fidelity approach. What we mean by this is that RICE scores (if calculated accurately) are able to give you a more precise understanding of each feature you’re considering.

Why? Because RICE considers four factors while effort-value matrices only consider two. Additionally, RICE incorporates a Confidence score to reflect the fact that not all estimates are equally well-defined.

This isn’t to say the value-effort matrices are bad, though. In fact, their simplicity is actually a good thing in many cases. If you have a smaller team or limited resources, using an effort-value matrix is usually a more practical approach.

Limitations of the RICE framework

  • Subjectivity in scoring: Assigning scores for reach, impact, confidence, and effort involves a bit of subjectivity—especially for confidence and effort.
  • Data dependence: The accuracy of your RICE scores hinges on the quality of your data. Limited user data or a lack of historical benchmarks can make scoring challenging.
  • Focus on quantification: RICE prioritizes features that can be easily quantified. This might overlook qualitative factors like UX improvements or brand perception—both of which are important, but not necessarily for reasons that are reflected in the numbers.
  • Could be inaccurate in the future: RICE scores can become dated quickly. You can’t always rely on old RICE scores, and recalculating them can be a hassle.

Overcoming limitations

  • Collaborative scoring: Involve stakeholders from different areas (product, development, marketing) to discuss and agree upon scoring criteria for each RICE factor.
  • Collect user feedback & data: Actively gather user feedback through surveys, polls, etc. With Featurebase, you can gather accurate data about your potential reach and impact, contributing to a more accurate score overall. 
  • Qualitative considerations: While RICE emphasizes quantitative data, don't neglect qualitative customer feedback or expert opinions. Use them to complement your scoring and identify potential benefits beyond easy quantification.
  • Strategic flexibility: RICE scores are a starting point, not an absolute truth. Integrate your product vision, long-term goals, and market trends into your final prioritization decisions.

Wrapping up

The RICE framework empowers you to eliminate guesswork and gut feelings when prioritizing features and initiatives. By focusing on Reach, Impact, Confidence, and Effort, you can make data-driven decisions that deliver impactful features aligned with user needs and business goals.

Remember—the RICE framework is a tool, not a mandate. Use it, but don’t follow it blindly.

Featurebase simplifies calculating accurate RICE scores thanks to powerful user feedback collection and analysis tools. From measuring reach with feature voting to estimating impact with MRR attribution, we plug the data gaps so you can prioritize with confidence.

Start collecting & prioritizing feature ideas with Featurebase for free →