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 , 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 .
Let’s get into the RICE framework and its strengths and weaknesses.
How does the RICE scoring model work?
The RICE scoring model is a framework that prioritizes features. It minimizes personal biases in by judging features on specific factors.
It uses four key components to assess value. If you're , 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.
How is a RICE score calculated?
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.
The history of the RICE scoring model
Sean McBride introduced the RICE scoring model as a 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.
Strengths and weaknesses of the RICE scoring model
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
Everyone has preferences that influence the ideas we like best. The RICE framework provides a structure with a that eliminates personal biases.
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.
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.
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 to produce over a longer period.
Weaknesses of RICE scoring
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.
Unless you specifically calculate 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.
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.
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 .
How to use RICE scoring effectively
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 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.
Ways to improve RICE scores
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, , 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 , 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
Alternative prioritization frameworks
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 :
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.
This model focuses directly on 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.
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.
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