RICE and ICE Prioritization Framework
Every product manager sooner or later faces the issue of prioritization when planning a strategy and product roadmap. Is it always easy and quick to decide what to work on first?
A high-quality prioritization system will help to consider each feature or idea, each project or task and consistently combine all these factors.
Today, PM offers a variety of popular methodologies for prioritization from gaming to the most complex, quantitative and qualitative. All of them help managers and teams answer a very important question: how to choose the right features for development?
In this article, we will look at two simple but very useful techniques — RICE Scoring and the ICE prioritization method.
RICE Score Method
If you have several important and urgent features in your implementation plan, how do you understand which one to start first?
This important issue of prioritization is at the heart of all product management. The fee for choosing the wrong option may be too high.
RICE is a method of prioritizing product ideas and features. The abbreviation includes 4 factors that a product manager can safely use to evaluate and prioritize product features:
To get a RICE score, you need to combine these factors.
Reach
The level of coverage is measured by the number of people/events over a certain period of time. This factor is designed to estimate how many people each feature or project will affect over a certain period of time, and how many of your users will see such changes.
It is important to focus on real metrics rather than using obscure numbers.
For example:
Features will be used by 800 users per month.
1000 users are involved in onboarding, and 70% — only 700 users will see this feature.
Impact
The impact shows what contribution this feature brings to the product.
Value is understood differently in each product. For example, in Hygger (B2B SaaS) for the current quarter, features get a high value if they:
1. Improve trial-to-paid conversion (metric movers)
Based on your current goals, you will have your own metrics.
2. Help attract new users
These are features that help us get new users during onboarding. But do not forget that most users “disappear” on the second day.
For example, in SaaS, an excellent indicator of retention on the first day is 15%. This means that 85% of people just leave on the second day. Therefore, here you should think about the features that most new users will be able to see in the first session.
3. Help to keep current users
Customers have bought a subscription and are now asking to make some features. We are not “in a hurry” to blindly do everything in a row. We accumulate statistics on each feature — how many customers have asked for it. And then we will implement the most popular features.
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4. Add value to the product and set us apart from competitors
There are more than five hundred project management systems on the market today. To survive and succeed, we need to do something completely new, preferably to increase the service life for users or reduce costs several times. Here we are looking for opportunities that can give us a competitive advantage, create a reason why competitors’ customers will come to us. This competitive advantage should be unique, difficult to repeat and, ideally, not reproducible.
By the way, the impact is difficult to measure accurately. So, we choose from a scale with many options: 3 for “mass influence”, 2 for “high”, 1 for “medium”, 0.5 for “low” and, finally, 0.25 for “minimum”. These numbers are multiplied by the final result to scale it lower or higher.
Confidence
If you think that a feature can have a huge impact, but you don’t have the data to prove it, Confidence allows you to control this moment. Confidence is measured as a percentage.
For example
Project A: The product manager has quantitative indicators for the impact of the feature, and an estimate of labor costs. Thus, the project receives a 100% confidence score.
Project B: The product manager has data on coverage and labor costs, but he is not sure about the influence factor. The project receives a confidence factor of 80%.
Project C: Coverage and impact data may be lower than expected. Labor costs may be higher. The project receives a 50% confidence rating.
Effort
Labor costs are estimated as the number of “man-months”, weeks or hours, depending on the needs.
For example:
Project A will take about a week of planning, 2 weeks of design and 3 weeks for development, so the labor costs will be 2 man-months.
Project B will only need a week of planning, 1–2 weeks for development and will not require design. Labor costs will be equal to 1 person-month.
ICE Assessment Method
The ICE prioritization method was coined by Sean Ellis, who is known for authoring the term Growth Hacker.
ICE was originally intended to prioritize growth experiments. Later, ICE was also used to prioritize features.
ICE Scoring: How does it work?
Calculate the score for each feature or idea, according to the formula:
Impact — Impact shows how much your idea will positively affect the key indicator that you are trying to improve.
Ease — Ease of implementation is about simplicity of implementation. This is an assessment of how much effort and resources are required to implement this idea.
Confidence — Confidence shows how confident you are in the impact assessments and ease of implementation.
Disadvantages of ICE
ICE Scoring is sometimes criticized for its subjectivity:
the same feature can be evaluated differently by the same person at different times. This may affect the final priority list.
if different people evaluate features, they will all evaluate it differently.
team members who want their features to be prioritized can manipulate the results to get an appruv.
Conclusion
Today we got acquainted with such frameworks as RICE and ICE. Using live examples, we saw how these models can be used for productive work with hypotheses. Next, we will look at no less interesting and cool prioritization frameworks.
Great insights on prioritization frameworks for product managers!