RICE scoring explained: What it is and how to use it

When a fruitful brainstorming session ends with a bounty of ideas for a life-changing product, figuring out where to begin can be your biggest problem. 

Pursuing every idea would be costly, time-consuming, and make it difficult to understand what's working and what isn't. 

Using a formula to rank initiatives in order of importance enables team members to put their opinions aside and score every initiative according to essential factors. 

The RICE scoring model is a popular way to help teams consider and compare each factor of a project idea.

Let’s get into the RICE framework and its strengths and weaknesses. 

The RICE scoring model is a product management framework that prioritizes features. It minimizes personal biases in decision-making by judging features on specific factors. 

It uses four key components to assess value. If you're developing a new product, you'll create a roadmap of priorities by scoring each feature based on the following:

• Reach: How many people will be affected by the feature in a specific timeframe

• Impact: How much the feature will impact individual users 

• Confidence: The level of confidence you have in the impact and reach scores

• Effort: The amount of effort the feature will require (e.g., time investment, resources)

Reach and impact clarify the essential benefits of a feature. Confidence quantifies how much doubt is in the equation. Effort indicates the investment required for the payoff of the benefits. 

To successfully use RICE, your team should agree on a single goal and the metrics for scoring. 

RICE scoring uses different metrics to provide a data-centric approach for evaluating and ranking potential initiatives. Once you've assigned a score to each feature, you'll use a simple formula to calculate the final total. 

Reach

Reach determines the specific number of customers you’ll impact with a proposed feature within a measurable time frame. When possible, scoring should accurately reflect a real-life situation. 

For example, if the change will affect 1,500 customers who use the feature in a given quarter, the score is 1,500.

Impact

You measure impact on a multiple-choice scale ranging from minimal to massive impact: 

• Minimal is 0.25

• Low is 0.5

• Medium is 1

• High is 2

• Massive is 3

Confidence

You measure confidence as a percentage to curb enthusiasm and deliver a realistic view of whether reach and impact metrics are accurate:

• 100% is high confidence

• 80% is medium

• 50% is low

• Anything below 50% is essentially a guess

Effort

Effort is the only metric that calculates cost in the RICE scoring model. 

It estimates the amount of work one team member can do in a whole month. The time calculation should include planning, design, and engineering. 

Once you've estimated a score for each category, multiply the benefits and divide the total by the cost. 

Reach x Impact x Confidence / Effort = RICE score

So, if reach is 1,500 people, the anticipated impact is high, confidence is medium, and the project needs three months of effort, your calculation will be: 

1,500 x 2 x 80% / 3 = 800

After calculating a RICE score for competing initiatives, you can rank them from the highest to the lowest score.

Sean McBride introduced the RICE scoring model as a product manager at Intercom. 

Although Intercom's team worked with prioritization models, they couldn’t find one that offered an effective scoring method to determine which initiatives to prioritize. 

After testing and iteration to determine which factors were important, Intercom developers settled on RICE scoring. 

The simple formula enables product management teams to clearly identify priorities and when trade-offs are necessary to yield the best results.

Prioritization frameworks can help teams decide how to prioritize specific ideas. Yet a single model isn't likely to always be the best tool for every job. 

Evaluating the strengths and weaknesses of RICE scoring can help you evaluate the framework's effectiveness for your current needs.

Strengths of the RICE scoring model

Provides objectivity

Everyone has preferences that influence the ideas we like best. The RICE framework provides a structure with a data-driven approach that eliminates personal biases.

Uses the most important factors

The RICE framework quantifies the factors most likely to provide a robust reflection of ROI. The biggest value factors are the number of impacted customers and how powerful a feature is. 

Accounting for uncertainty and effort adds credibility by introducing real-world drawbacks.

Easy to understand

The framework is adaptable, and its four elements make it easy to use. It also clarifies the reasoning behind decisions, increasing its acceptance by all team members and stakeholders.

Ideal for creating roadmaps

The framework's versatility makes it suitable for developing prioritization roadmaps for short- and long-term goals. 

Since you can order RICE scores from high to low, you can easily create lists of priorities. These lists can range from features of a single product to a line of products to produce over a longer period.

Weaknesses of RICE scoring

Potentially inaccurate scores

RICE scoring requires estimations, which can be inaccurate. Estimating reach can require guessing customers’ actions. Similarly, capturing accurate data for impact and effort can be difficult.

Doesn't take tech debt into account

Unless you specifically calculate tech debt into your effort score, the RICE model overlooks the need to improve the underlying tech infrastructure. This can be a serious roadblock for software developers.

Bias can creep in

Since the RICE framework requires estimations, team members can inflate scores to align with their feelings about certain initiatives. This can happen intentionally or unintentionally, as enthusiasm for a great idea prevents rational thinking.

Customers aren't part of the equation

Reach defines how many customers a feature would affect, while impact estimates what development teams think the impact will be for those customers. Without teams consulting customers for their opinions, RICE doesn't prioritize customers' needs.

Rice scoring is not an unbreakable rule for every decision. It helps product managers make informed decisions about prioritizing initiatives based on factors that influence the best results. 

You may choose to work on projects out of order due to dependencies or critical needs based on customer feedback. RICE can help you recognize when working on projects out of order makes sense. It allows you to logically identify the reasons for and value of trade-offs. 

When using RICE scoring, remember that the model doesn’t replace crucial processes like sprint planning and price forecasting. 

While it helps you identify top priorities, you'll need to consider variables that might not arise in every situation. For example, you may need to sequence work properly to avoid bottlenecks and bugs in some cases. 

The weaknesses of the RICE scoring model don't entirely eliminate the model's effectiveness. Adjusting the formula can customize RICE scoring to your needs.

Try these tips to overcome issues with RICE scores:

Ensure data is accurate

Gather hard data backed by historical efforts, customer feedback, or other performance indicators to calculate accurate scores. This can avoid wild guesses and overestimations. 

Add specificity

Impact is a wide category with many influences. Use your goals to determine exactly what impact means for this priority roadmap.

For instance, if your goal is to increase conversions, calculate how many visitors will become long-term customers based on a specific upgrade.

Similarly, you assign effort to a person per month. You could change this to days if it aligns more with your project or consider resources if cost is more of a factor.

Change the scoring process

From reach to effort, you'll occasionally find some scores difficult to calculate. You can adapt the scoring process to fit your needs and avoid making your prioritization framework a time-sink.

For example, assessing reach can require substantial research. You can group customers into categories represented by numbers (1 = 1-100 people, 2 = 100-300 people, etc.).

Changing the scoring process can add granularity. Consider how a more defined impact or confidence scale would look. Instead of minimal to massive impact, you could assign different values.

For example:

• 1: Below 100 conversions

• 2: 100-500 conversions

• 3: 500-1,000 conversions

• 4: 1,000+ conversions

Similarly, you could change the percentage rates for confidence:

• 100% is excellent

• 80% is good 

• 60% is medium

• 40% is low

• 25% is poor

The RICE scoring model is a popular prioritization framework that’s flexible enough to use in various situations. However, it's not the best option for every development team. 

If RICE scoring doesn't seem right for you, consider one of these prioritization frameworks:

Value vs. complexity quadrant

Similar to RICE scoring, this model compares value to complexity to rank priorities. 

You assign features as low or high value and low or high complexity. High value, low complexity features rank as top priority. 

Since no specific factors define value, complexity, or numeric scores, this model is generally simpler than the RICE framework.

Kano model

This model focuses directly on customer satisfaction by rating features based on five levels of customer delight: Basic, excitement, performance, indifferent, and reverse. 

Opportunity scoring

This model puts customers in the driver's seat, requiring them to rank features based on importance and satisfaction. 

Customers rate the importance of several features and their satisfaction level with each. Features that rank high in importance and low in performance are priorities that immediately demand attention. 

MoSCoW method

This method is ideal for teams with tight deadlines. It classifies features based on four priority levels, including must-have, should have, could have, and won't have. 

Prioritizing features on necessity helps product managers quickly assess what is most important.

RICE and ICE scoring are prioritization frameworks that help you determine which features will provide the most value. However, they use different factors and calculations for scoring. 

RICE scoring requires team members to multiply scores for reach, impact, and confidence before dividing the result by effort. 

You calculate an ICE score by multiplying impact, confidence, and ease. It doesn't consider reach.

Feature scoring methods are prioritization frameworks that you can use to determine which features will be most effective. RICE scoring is one example of using a prioritization model as a feature scoring model.