RICE Score: A Balanced Prioritization Framework

rice framework overview for product managers

Why RICE?

Imagine you are a product manager at a company, and your goal is to drive user engagement. 

The engineering team has encouraged you to develop “feature A” since users will like it.  

The design team says that a website revamp is the best possible option at the moment. 

The key stakeholder says that you should do neither of those two things. Instead, it would be best if you worked on another feature first.  

How do you sort out the noise? 

We recommend using a prioritization framework.

Everyone who came to you with advice did so with good intentions. They were basing their recommendations on gut feelings and emotions. However, your job as a product manager is to turn this emotional-based decision-making process into one that is grounded in fact.  

Currently, you have many different ideas to help you reach your one true goal.

Frameworks like the RICE prioritization method can help these ideas put the product in the best sport from now on.

What Is a RICE Score?

RICE stands for Reach, Impact, Confidence, and Effort. 

It is a simple and popular prioritization framework. 

The RICE framework determines the importance of various features, ideas, and initiatives that people may have for a product. 

A RICE score lets the PM quantify the specific importance and compare them to many others.

The formula for calculating the RICE score is as follows:

RICE= [(Reach x Impact x Confidence)/Effort]

Due to the nature of a product manager’s job, they have many different ideas that they can work on at any moment. 

RICE prioritization helps organize and ensure that the product manager knows what to work on next.

Reach

The reach score is the number of people you will impact by implementing a feature in a given timeframe. 

One beneficial aspect of the RICE prioritization framework is defining the timeframe for the impact and what type of user you plan to reach with it.  

You can quantify this via internal metrics during the product development and external surveys answered by your target audience.  

An example of calculation reach is as follows: Let’s say you are the product manager for a file transfer application. 

Let’s say you wanted to calculate the reach score for a feature that tells people when they are close to running out of storage. 

For this, you should find out how many people are close to that storage cap in a given period (let’s say a day). Then, consider how long you want the period to be (let’s say one month).  

If 50 people a day get close to reaching the storage cap and your reach time is one month, then the reach for the feature would be 50×30=1500 people or 1500.

Sometimes calculating the reach may be difficult if a product is very new and does not have many users. It is also tough to calculate this score if the internal tools aren’t accurate or unavailable.  

In such a case, it may be beneficial to try and get statistics from other companies in your competing field. 

However, that isn’t perfect as those companies may be involved with and serve target audiences who are not that close to your own.  

Another way to get around this would be to try and combine the reach and impact score into one combined score. What matters the most is how much impact a feature will have on the revenue that it will generate.

Impact

The impact score calculates your feature’s effect on your users. In short, reach is about how many people, while the impact is how much it will affect a customer.  

To understand the impact, think about how much implementing a particular goal will help your company.

One common goal for most companies is “how the feature could affect the likelihood to convert someone into a repeat, long-term customer.”

The best way to go about and standardize this impact is by providing a scale that rates features. 

A typical scale is 3 for “massive impact,” 2 for “high impact,” 1 for “medium impact,” 0.5 for “low impact,” and 0.25 for “minimal.”

Without an excellent goal, the effectiveness of the impact score and RICE framework as a whole has decreased. 

It is helpful to have strong communication with all teams and stakeholders. That helps determine the goal for a given time so that prioritization can be more effective.

Confidence

This number represents how certain you are about the reach, impact score, and their corresponding benefit. 

Effectively, it aims to act as a fail-safe against reach and impact scores that are too high due to accidental bias in planning.

Let’s say, you understand what the reach, impact, and effort scores are, yet there are gaps. You should add a confidence score to check that uncertainty in such a case.

Like the impact score, you calculate the confidence by providing a scale to measure the confidence level against. 

A typical scale is 100% for high confidence, 80% for medium confidence, and 50% for low confidence.

Product managers need to enhance the confidence score and rating accuracy whenever there is a low confidence score. 

A low confidence score should be a last resort when there is no alternative. It shouldn’t be a default option when one is too lazy to seek more information.

Similarly, a high confidence score should reflect the product manager’s confidence in the success of a feature. That is based on several data points, mockups, and designs of the actual feature.

Effort

This score represents how long it will take the various product, design, and engineering teams to implement a feature. 

While the other three aspects of RICE calculate the upside of a feature, the effort score calculates the downside of implementation.

The most common way to quantify efforts is the number of persons per month or “person-months” it will take to complete the round-up. 

If it took seven people one week to work on the feature in the file transfer application example, it would have an effort score of seven person-weeks.  

The standardizing effort allows people to compare how long various features and products will take.  

However, it requires tight communication with various engineering and design teams. That ensures these ratings are as accurate as possible and may be hard to calculate at first glance.

Where Did the RICE Score Model Come From?

The RICE model was developed by Intercom – a popular messenger and customer communications platform. 

Though Intercom had been using scoring systems for prioritizing different sets of ideas, they couldn’t find the one that best suited their needs. 

After many trials and tribulations, they came up with the RICE framework and the RICE prioritization formula to combine the four factors. 

The RICE score can give product managers and the team a bird’s view of the various project pipelines and which ones to prioritize.

The Power of Rice

RICE is not just some equation that you can use to calculate the effect of a feature. 

Instead, it facilitates communication between stakeholders and sharing ideas by getting rid of jargon.

What Product Managers Need To Consider During Prioritizing Tasks?

Prioritizing tasks is vital after the team has decided the ideas, found solutions, and built the product roadmap. 

But this journey of deciding which you should undertake from the roadmap can be a tedious task for a product manager. 

You may ask why? 

It is always possible to bring our unique personalities and ideologies to the table as an individual. That’s good! 

The problem arises when we push our ideas without thinking about the other possible solutions to reach the end goal. 

Let us look at some of the points product managers can consider while prioritizing tasks using the RICE framework.

1. PM may bring biases and preconceived notions along with them unknowingly. There are two types of considerations that come up when prioritizing tasks:

a) Objective Consideration: Product managers may consider the company’s financial data, customer experience, and other measurable aspects while prioritizing tasks.

b) Subjective Consideration: The other aspect that product managers may consider while prioritizing the tasks are subconscious beliefs. An individual’s strengths and weaknesses may back these. 

The other biases could be the product manager’s beliefs about the company’s/project’s outcome, team’s abilities, stakeholders’ beliefs, and others.

2. Before prioritizing the new tasks, product managers need to consider if there are already some projects that the team could work on.

3. It’s easy to go ahead and prioritize the ideas you consider are worthwhile. But it’s essential to look at the other pool of ideas and the reach and impact they might have.

4. Another bias that product managers need to consider would be, not to sideline the tasks that may require more effort than the others on the roadmap.

These are some preconceived notions that should be addressed while prioritizing the tasks. And this job is better organized when you use the RICE prioritization method. 

The RICE framework and the RICE score can help product managers to view the projects on the product roadmap with much more discipline and almost no biases.

What are the Pros of the RICE Prioritization Method?

Easier to grasp

The RICE model is simple for non-technical stakeholders to understand. It’s a great way to introduce the concept of prioritization and tradeoffs in product development. 

Based on the data

The RICE Prioritization model can be used as input data for the product roadmap planning meetings. It will clarify what you should offer next, which you can use as input data for product roadmap planning meetings. 

With the RICE framework in mind, your team can establish clear success criteria for each project or feature to help inform priorities and decision-making. 

It is conducive when deciding on new features because you have a set of guidelines for how this will be measured going forward. 

For example, You can measure “reach” by looking at how many new users sign in due to the new product launch/feature. 

Or suppose you’re working with an existing product. In that case, you can measure “impact” by looking at how your app usage changes among existing users. 

This type of information becomes incredibly helpful in objectively evaluating whether or not a feature is working. 

Realistic view

The RICE prioritization formula is helpful when planning out your product roadmap. You have to be realistic about what you can accomplish within a specific time frame and how many resources it will take. Otherwise, things might fall through the cracks or end up being half-baked. 

The RICE prioritization also helps teams understand their users’ goals and impact when using a new product or feature. 

It provides a checklist for evaluating priorities and making decisions around product development to avoid expensive mistakes.

User friendly

The RICE framework is also helpful in making decisions about optimizing features and identifying new opportunities.  

RICE prioritization method keeps track of feature requests from customers and other user feedback. In short, user experience is an essential factor in the RICE model. 

SMART goals

Setting SMART goals improves efficiency in any working model. And this is true even when using the RICE prioritization method. 

When using the RICE model to arrive at the single rice score, it is advisable to arrange the product metrics in a SMART (specific, measurable, attainable, relevant, and timely) way. For example, one of the factors in the RICE model is REACH which is based on time.

What are the Cons of the RICE Prioritization Method?

Comprehensiveness

RICE prioritization formula is simple to understand. However, it is designed for complex tasks/projects. Hence, using the rice score to decide the smaller and simpler tasks may complicate things. 

Inaccurate estimations

The rice score and its estimates may not always be accurate. The RICE prioritization method quantifies the features from the lists using the confidence factor.

Dependencies not considered

The RICE score does not include the dependencies of the product, product managers, and teams. Often they have to de-prioritize tasks that may have a higher RICE score after putting them through the RICE framework. 

Labor-intensive

Along with considering the feature outcomes before moving ahead, product managers should analyze a particular feature against all the four factors of the RICE framework. It can be tedious and time-consuming. 

Unavailability of data

Measuring the reach and impact factors of the RICE model is essential. But not all the products or features may have the data available for such metrics to reach a higher RICE score. 

The suggestion here is to cut down your confidence score accordingly, which can drop the overall RICE score. But due to the low scoring in the RICE framework, the feature may never be released.

Conclusion

A prioritization process such as the RICE framework works wonders to keep the product managers and teams on track and make informed decisions. 

You can achieve many more benefits once you familiarize yourself with the RICE model. 

RICE prioritization method may have its pros and cons. 

Yet, Intercom suggests not using this scoring model as a hard and fast rule. 

You can always switch when required and use it as per your company’s needs.

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