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How to optimize product planning with the RICE framework

PostsDesign & UX
Georgina Guthrie

Georgina Guthrie

October 11, 2023

Deciding on the next feature to develop is a daunting task. Should you listen to that customer who emailed last week? Or should you follow the intuition of your lead engineer? Enter the RICE framework: a method for slicing through indecision and prioritizing features based on their impact and feasibility. 

Grounded in metrics and offering a balance between data and intuition, RICE is increasingly adopted by startups and big corporations alike. In this article, we’ll shed light on how it can simplify decision-making, all while lowering risk and boosting confidence. Let’s go! 

Why prioritization is difficult 

Prioritizing features in product development is complex. It’s an art as much as a science. Here’s where people commonly trip up:

1. Multiple stakeholders

Different teams and individuals have varied opinions and interests. From sales teams looking to close a deal to engineers focusing on feasibility — the spectrum of opinions can be vast and disorienting. 

2. Lack of clear data

Sometimes, the data about a particular feature’s potential impact or feasibility is ambiguous, making it hard to gauge its true value.

3. Emotional attachments

Team members may have personal attachments to certain features, clouding objective decision-making.

4. Shifting market conditions

The market landscape can change rapidly. What seems essential today might become irrelevant in a few months due to evolving customer preferences or external disruptions.

5. Resource constraints

Limited resources, be it time, peoplepower, or budget, can force hard trade-offs, making prioritization even more challenging.

6. Fear of Missing Out (FOMO)

Seeing competitors roll out certain features can put pressure on teams, leading to reactionary decision-making rather than strategic choices.

7. Mismatched visions

Without a unified product vision, team members might pull in different directions, causing misalignment in prioritization.

8. Customer feedback overload

While customer feedback is invaluable, too much of it can be paralyzing, especially if there are conflicting requests.

9. Technical debt

Prioritizing new features without addressing underlying technical issues can lead to long-term problems, but addressing these issues might delay the release of attractive features.

10. Scalability concerns

As products grow, there’s a need to consider how new features will scale, which can add another layer of complexity to the prioritization process.

11. Overvaluing complexity

Sometimes, there’s a temptation to prioritize features that seem intricate or sophisticated, thinking they’ll be more impressive to users, even if simpler solutions might serve better.

12. Desire for quick wins

The immediate satisfaction of checking something off a list can lead teams to tackle easier, less impactful features at the expense of more critical ones.

13. Avoidance of difficult conversations

Prioritizing might mean saying no to certain ideas, which can be emotionally challenging if it involves disappointing colleagues or stakeholders.

What is the RICE Framework?

The RICE framework is your go-to method for deciding what product features should hit the frontlines next. No more guesswork or endless debates. RICE breaks down the decision-making process into four clear components:

1. Reach

How many users will this feature impact within a specific time frame? If you’re developing an app and you add a new tool, how many of your users will actually benefit from it?

2. Impact

When users engage with this feature, what’s the potential effect? Is it a slight improvement or a game-changer? Typically measured on a scale from 0.25 to 3, it gives a sense of the magnitude.

3. Confidence

How certain are you about your estimates on reach and impact? Sometimes, you’ve got solid data backing your assumptions; other times, it’s more of a hunch. A percentage score (like 80% confidence) can help pin this down.

4. Effort

How much work will the feature demand? This is usually estimated in terms of developer time. If one feature takes a week to implement and another takes three months, that’s a significant difference in effort.

Now, blend these factors together, and you get a RICE score: a tangible, comparative number that aids in making informed choices. It’s systematic, it’s logical, and it empowers teams to navigate the product development maze with clarity and confidence.

We’ll explain how to do this in more detail a little later on. But first — some context.

What’s the history of the RICE scoring model?

The RICE framework wasn’t born out of mere theory but from practical necessity. It was invented by Intercom, a leading company in the software and messaging space. Faced with needing to prioritize their product features, they sought a method that was both methodical and adaptive. Existing frameworks either leaned too heavily on subjective judgments or became tangled in their own complexities.

RICE was their answer. It was designed to combine quantitative data with qualitative judgment, making feature prioritization more systematic and less prone to the pitfalls of intuition alone.

Its efficacy wasn’t contained just within the walls of Intercom. As the broader industry noted, RICE’s utility began resonating with a diverse range of businesses. Its applicability spanned from nimble startups eager to optimize their roadmaps to large corporations looking to refine their prioritization processes.

The core appeal of RICE? A structured, yet flexible approach to product development, ensuring that decisions are backed by data, reason, and clear rationale.

How to use the RICE scoring model (with examples) 

Let’s break down how to deploy this framework effectively, complete with examples to illustrate each step:

1. Reach

This measures how many users a particular feature or project will impact over a given time frame, usually a month.

Common challenges of calculating reach

  • User values differ: Not all users are created equal, and a raw user count might not always give the whole picture. 
  • User segmentation is complex: Not every feature will appeal to or be used by every user. It’s crucial to define which segment of your user base the feature targets. Ignoring this can lead to overestimation or underestimation of Reach.
  • Evolution of user base: If your user base is rapidly expanding or contracting, your Reach calculations for features in the pipeline might need frequent adjustment.
  • Overlap with other features: Introducing multiple features around the same time? There might be an overlap in the users they target, which can complicate Reach calculations.
  • Over-generalization: Simply using the total number of app users can skew the Reach estimate. Not every user might find the feature relevant.
  • Static calculations: Treating your user base as a constant figure without accounting for growth or churn can lead to miscalculations.

How to calculate reach 

Calculating reach is a blend of data analysis, user understanding, and a bit of forecasting. Here’s a step-by-step guide.

1. Define your user segments: Before you calculate reach, you need to define the segments of your users who you believe the feature will impact. Segment by user demographics, behavior, product usage patterns, or other relevant criteria.

2. Use historical data: Examine similar features or updates you’ve released in the past. How many users adopted them? This will give you a baseline estimate.

3. Survey and feedback: User research is a must. If possible, run surveys or beta tests to gauge interest in the feature. This can give a more immediate (and tangible) sense of potential.

4. Analytics forecast: Use your analytics tools to predict potential user engagement. For example, if 25% of your users have shown interest in similar features, and you have 100,000 active users, your forecasted reach might be 25,000.

5. Adjust for overlaps: If you’re launching multiple features, and there’s potential overlap in user interest, be sure to account for this. For example, if Feature A will be used by 10,000 users and Feature B by another 10,000 users, but you estimate that 2,000 users will use both, your combined reach is not 20,000 but 18,000.

6. Factor in growth or churn: If you’re in a rapidly growing market, you might want to increase your reach estimates to account for new users. Conversely, if there’s significant churn, you’ll need to adjust downwards.

7. Final calculation: After considering the above factors, come to a final estimate. Remember, it’s always a good idea to revisit this number post-launch to refine your future predictions.

An example of calculating reach

Let’s imagine you own a fitness app that’s adding a yoga feature. If your data shows that 30% of your users are interested in yoga and you have a user base of 50,000, your estimated reach is 15,000. 

But if, after a preliminary survey, you find out that an additional 5% of users who previously weren’t interested in yoga would like to try it via the app. Adjusting for this new information, your potential reach becomes 17,500 (30% of 50,000 + 5% of 50,000). 

Top tips for calculating reach

  • Use analytics tools: Leverage tools like Google Analytics or Mixpanel to gain insights into user behavior and demographics. This data helps you get a more accurate reach estimate.
  • Regularly update your user data: Make it a practice to periodically review and adjust your user base numbers, especially if your product is in a changeable market.
  • User surveys: If you’re in doubt, ask. A well-structured user survey will give you useful insight into how many users might be interested in or benefit from a potential feature.

2. Impact

This assesses the depth or extent of value a user gets from a specific feature or project. It dives into not just the quantity (how many people use it) but the quality of the improvement in their experience. The scale typically ranges from 0.25 (minimal impact) to 3 (massive impact).

Common challenges of calculating impact

Qualitative vs. quantitative: While some impacts are measurable in numbers (e.g., increased conversion rate), others, like improved user satisfaction, might be more qualitative and harder to measure.

Varying degrees: Features range from transformative changes to minute refinements. Deciphering the gravity of their influence is a challenge. 

Subjectivity: Impact is often in the eye of the beholder. What one segment of users sees as a monumental improvement might be inconsequential to another.

Balancing act: Ensuring that the implementation of one impactful feature doesn’t negatively influence another aspect of the user experience.

Dependencies: The potential positive impact of one feature could be contingent upon the successful implementation of another, adding another layer of estimation complexity.

Lack of historical data: For companies new to a market or those launching innovative features without direct predecessors, estimating impact can be a challenge due to the absence of historical benchmarks.

How to calculate impact 

Gauging impact requires a blend of data-driven insights and user sentiment understanding.

1.  Feedback and testimonials: Encourage users to share their thoughts after using the feature. Positive testimonials can be a clear indicator of a feature’s impact.

2.  A/B testing: By testing a feature with one user group and not another, you can measure its specific influence on metrics like engagement or conversion.

3.  Benchmarking: Compare the performance metrics before and after the feature’s rollout. Significant positive changes in metrics like user activity, retention, or sales can signify high impact.

4.  User surveys: Directly ask users how a feature has changed their product experience, giving them options ranging from ‘not at all’ to ‘significantly.’

5.  Competitor analysis: If competitors don’t have a similar feature and your user base grows after implementation, this could indicate the feature’s strong impact.

An example of calculating impact

Suppose you manage an online bookstore. You decide to implement a feature that offers personalized book recommendations based on users’ reading history.

Before the feature, you had a monthly average of one book sold per active user. After implementing the personalized recommendation system, this number increases to one and a half books per user. This quantifiable jump not only signifies increased sales but also indicates the feature’s high impact on user purchasing behavior.

Later, you start receiving user reviews expressing appreciation for the ‘spot-on book suggestions,’ adding a qualitative validation of the feature’s impact, giving you further proof your feature is a keeper.

Top tips for calculating impact 

  • User-centric approach: Always prioritize the user’s perspective. A feature’s technical brilliance doesn’t always translate to a high user impact.
  • Iterative feedback loop: After initial implementation, gather feedback, make adjustments, and measure again. This iterative process can ensure the feature’s impact is optimized over time.
  • Stay updated: Market dynamics and user preferences can change. Regularly reassess the impact of features to ensure they continue to provide value.
  • Collaboration: Engage with teams across departments (e.g., sales, customer support) to gather insights about a feature’s real-world impact.

3. Confidence 

The RICE model doesn’t just revolve around numbers, though. It’s also about trust in those figures. When we talk about confidence, we’re basically diving into the assurance behind your estimates. Usually, you’d express this as a percentage, with 100% being absolute certainty.

Common challenges

  • The ever-shifting sands of projects: Ever notice how some elements of your project can change from one week to the next? That flux can dent your confidence, throwing you off course. 
  • Mix of perspectives: Everyone on your team might have a slightly different take on a feature’s potential success. Navigating these varying perspectives can be challenging.
  • The slippery nature of data: Just because you have a ton of data doesn’t mean it’s giving you a clear picture. Sometimes, it feels like trying to read tea leaves.
  • Past experiences casting shadows: Been burnt by a project before? Or maybe you’ve had a roaring success? Either way, these past experiences can color your confidence levels.
  • Surprises courtesy of your users: Even with all the research in the world, users can still throw you a curveball with their reactions.
  • Mistaking hope for confidence: There’s a thin line between what we hope will happen and what we truly believe will happen. Straddling this line can inflate Confidence scores.
  • Groupthink: While too many opinions can shake confidence, homogeneity is also cause for concern. If the team isn’t encouraged to share diverse opinions, there’s a risk of adopting a collective Confidence level that might not be reflective of individual assessments.

How to calculate confidence

Building up your confidence level isn’t just about intuition; it’s also about learning from the past and keeping an ear to the ground.

1.  History lessons: Think back on similar projects. How did they pan out? These past experiences can provide a blueprint for current expectations.

2.  The wisdom of the crowd: Engage with your team. A diverse range of insights can help refine your confidence percentage.

3.  Data strength check: Quality over quantity, always. If your data feels flimsy, your confidence might need recalibrating. But if it’s robust, that’s a strong foundation.

4.  Mapping out the rough patches: Recognize potential challenges or bottlenecks ahead. A clearer path generally equates to higher confidence.

5.  Feedback as your compass: A little pilot test never hurt anyone. Early feedback can be a game-changer for adjusting your confidence levels. Consider prototyping and MVPs to help you collect accurate data.

An example of calculating confidence 

Imagine you’re launching a new augmented reality feature in a shopping app. Initial in-house tests are promising, but there’s been some mixed reception to similar features in the market. You might start with a 65% confidence level. But after a beta test with a small group of users giving overwhelmingly positive feedback? That confidence could soar up to 80%.

Top tips for calculating confidence

  • Engage and listen: Don’t work in a bubble. The more feedback and perspectives you have, the better your confidence calibration.
  • Stay agile and ready to pivot: Confidence isn’t static. As new information rolls in, be ready to adjust your figures.
  • Balance instinct with insights: Gut feelings are essential, but so is concrete data. It’s all about finding that sweet spot.
  • Stay clued in: Keeping an eye on market trends and competitors can provide invaluable context for your confidence metrics.
  • Encourage structured feedback: Instead of open-ended discussions, structure feedback sessions to solicit specific insights, reducing ambiguity and providing clearer guidelines for assessing Confidence.
  • Use historical analysis: Review the accuracy of past predictions. Were past projects overestimated or underestimated in terms of Reach and Impact? This retrospective can offer a basis for adjusting current Confidence levels.
  • Third-party perspectives: Sometimes, an external viewpoint can offer a fresh perspective. Engage with user focus groups or industry experts for their take on a proposed feature.
  • Encourage individual assessments: Before team discussions, have each member assess confidence independently. This will help stop biases from forming.

4. Effort

This metric is all about time and resources. When you evaluate effort, you’re sizing up how much work and time a project or feature will need before it’s up and running. It’s the ‘cost’ side of the equation. It’s generally estimated in terms of person-months. The amount of work one team member can complete in a month.

Common challenges for calculating effort

  • Changing project scopes: We’ve all been there. What starts as a ‘small’ addition can sometimes snowball, throwing original estimates out the window (aka ‘scope creep’).
  • Diverse skill requirements: A feature might require diverse talents, from developers to designers. Accounting for each can be a juggling act.
  • Unexpected roadblocks: Every project has its surprises. Sometimes these can majorly bump up the effort.
  • Interdependencies: Your feature might rely on other tasks getting done first. This can add layers of complexity to your estimates.
  • External dependencies: Third-party APIs, services, or contractors can introduce uncertainty. Their timelines become yours.
  • The learning curve: New technologies or unfamiliar domains can mean your team needs time to get up to speed.
  • Fixed mindsets: Assuming that the effort required for a task is constant can be problematic. Adjust your effort estimates as teams learn and grow or as tools and technologies evolve.

How to calculate effort

To assess effort accurately, you need a mix of forward-thinking, data from past projects, and some candid team conversations.

1. Break it down: Chunk out the project. What are the major tasks and milestones? This gives a clearer picture of the journey ahead.

2. Consult the experts: Your team knows their stuff. Get their input on how long each chunk might take.

3. Add a buffer: A bit of wiggle room can account for those unexpected hiccups.

4. Historical data as a guide: Past projects can offer a ballpark. If you’ve done something similar before, use that as a benchmark.

5. Consider external factors: Are there third parties involved? Account for their timelines.

6. Continuous review: As the project evolves, keep revisiting and tweaking your effort estimates. It’s not a one-time calculation.

An example of calculating effort

Say you’re adding a chat feature to your app. Initially, you estimate three weeks – one for frontend, one for backend, and one for testing. But when you dive deeper, you realize you also need an extra week for designing user interfaces and integrating a third-party notification service. Adjusting for these insights, your effort now becomes four weeks.

Top tips for calculating effort

  • Stay in the loop: Regular check-ins with your team can help catch effort deviations early.
  • Be flexible: Effort estimates can and should evolve. It’s a living, breathing number.
  • Learn from the past: Post-project retrospectives are gold mines. They can shed light on what went right and what went, well, not so right.
  • Use tools: Project management and time-tracking tools can give real-time insights, helping fine-tune effort estimations on the go.
  • Chunk tasks: Instead of looking at a feature as a monolithic task, break it down into sub-tasks. This micro-view can yield a more accurate effort estimate.

RICE calculations (with examples)

Once you’ve estimated each component, the RICE score is computed as the following:

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

Example calculation

Given our dark mode feature with a Reach of 20,000, an Impact of one, a Confidence of 85% (0.85 in decimal form), and an Effort of 3:

RICE score = (20,000 x 2 x 0.85) / 3 = 11,333.33

The final RICE score allows you to rank this feature against others, helping prioritize what gets built and deployed based on potential user benefit and resource investment. And that’s it! 

RICE prioritization: 8 tips for success

RICE is more than just mathematical equations. It’s about aligning the method with your product vision and the requirements of your team.

1. Consistency matters: When you’re evaluating elements like reach or effort, ensure uniformity. If you’re estimating potential users or the duration to develop a feature, stick to a consistent metric. It helps to compare like with like.

2. Stay updated: Things evolve. User preferences, emerging competitors, technological advancements — they all shift. It’s essential to revisit and reassess your projects periodically to stay relevant.

3. Collaborative insights: Pull in perspectives from different team members. Your developer can provide insights into Effort, while the sales team might have a pulse on Reach and Impact. Collective brainstorming often leads to more comprehensive insights.

4. Strive for balance: While RICE is methodical, avoid getting overly fixated on perfect scores. A well-informed decision made in a timely manner often trumps waiting indefinitely for the ‘perfect’ data set.

5. Value feedback: After implementing, gather feedback to gauge the accuracy of your predictions. Reflecting on outcomes like actual Reach and Impact can fine-tune your future evaluations.

6. Combine data with intuition: While RICE leans on data, it shouldn’t overshadow qualitative insights or experienced intuition. Use the scores as guidance, but also consider the broader strategic context.

7. Educate and align: Ensure the entire team is well-acquainted with the RICE framework and its significance. A well-informed team not only streamlines decision-making but also fosters mutual respect and understanding.

8. Harness technology: Quality project management tools can simplify the RICE process. Product management platforms and virtual whiteboarding tools like Cacoo aren’t just user-friendly — they allow for systematic tracking, visualization, and enhanced collaboration. Give them a try for free today! 



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