Understanding Prioritization and the RICE Framework

Designers, developers, indie-makers, and product managers face the prioritization challenge: What product feature should we work on first? Originally published by Sean McBride, a product manager at Intercom, the RICE framework is a simple way to score potential product features to reduce assumptions and guesswork in roadmap planning.

RICE became a widely adopted method in product development, helping teams focus their efforts and maximize the value of their time.

If you’ve put the effort into brainstorming new ideas, finding opportunities for improvement, and collecting feedback, you’ll have a solid product roadmap full of good ideas. But the order in which you tackle those ideas deserves just as much thought. It would be best if you took the time to prioritize well.

Including everything would make the product complicated and worse. Thus, UX relies heavily on prioritization to determine what features add the most value relative to the resources needed to deliver them.

Prioritization helps us to determine which features or elements to prioritize based on user needs, project goals, and other constraints. We prioritize and rank design requirements, features, or elements in order of importance.

This is where a scoring system comes in. A good prioritization framework can help you consider each factor of a project idea with clear-eyed discipline and combine those factors rigorously and consistently.

How The RICE Framework works

RICE is an acronym for four key elements in product development. The framework is a simple formula for these elements, giving you a final RICE score.

Reach: To avoid bias towards features you’d use yourself, estimate how many people each project will affect within a given period. For example:

  • 500 customers reach this point in the monthly signup funnel, and 30% choose this option.
  • The reach is 500 × 30% × 3 = 450 customers per quarter.

Impact: To focus on projects that move the needle on your goal, estimate the impact on a person. The impact is difficult to measure precisely. You can choose from a multiple-choice scale: Minimal (0.25), Small (0.5), Medium (1), Large (2) or Huge (3) impact. These numbers get multiplied into the final score to scale it up or down.

  • This feature will have a huge impact on each customer who sees it. The impact score is 4.

Confidence: To curb enthusiasm for exciting but ill-defined ideas, factor in your confidence level about your estimates. If you think a project could have a huge impact but don’t have data to back it up, confidence lets you control that.

  • We have quantitative metrics for reach, user research for impact, and an engineering estimate for effort. This project gets a 100% confidence score.

Effort: The number of resources (time, people, etc.) required to complete the project. To move quickly and have an impact with the least effort, estimate the total time a project will require from all team members: product, design, and engineering. Effort is estimated as several “person-months” – the work one team member can do in a month.

  • This project will require several weeks of planning, significant design time, and at least two months of one engineer’s time. I’ll give it an effort score of 4 person-months.

How is a RICE score calculated?

To calculate a RICE score, you assign a numerical value to each of these factors and plug them into the formula:

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

For example, if a project will reach 200 users, have a large impact, is 100% confident, and takes 2 weeks of effort, the RICE score would be:

(200 x 2 x 100%) / 2 = 200

The higher the RICE score, the more impactful and achievable the project is relative to the effort required. Projects or features are then ranked from highest to lowest score to determine priority.

Here is a simple scoring system to help you prioritize well. There is also an example spreadsheet for prioritizing project ideas using the RICE scoring system; feel free to duplicate and modify it for your projects.

Graphic explaining impact, starting from data, how dots connecting each other with knowledge and ingsight.

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