The RICE method was developed by Intercom for prioritizing features on the product roadmap. It's suitable for mature product with audience where you have a product usage data available.
The RICE method helps you remove bias opinions from your prioritization meetings and encourages you to take data-driven decisions.
If you're looking for method to prioritize new product or MVPs it's better to go for the ICE method.
How to use the RICE method
The RICE stands for Reach, Impact, Confidence and Ease. These are the metric we're going to assign to each issue to calculate the RICE score. Let's have a closer look on them.
Ask "How many customers does this impact in this quarter?". You can choose any time period that suits your project but try not to guess but look at the data you have in hand. Are you planning to redesign filters in your app? Have a look how many users used them last months and take it from there.
Think about "How does this contribute to our goal?" and choose one of the following options:
- 3 for massive impact,
- 2 for high,
- 1 for medium,
- 0.5 for low,
- 0.25 for minimal.
Now it's time to asses "How confident you're that this delivers assigned Impact". Let's be honest, sometimes we're just too excited about new things and we overestimate the impact. The Confidence metric should bring us back to the ground. Choose one of these options:
- 100% for high confidence,
- 80% for medium,
- 50% for low.
Last question to ask is "How hard will this be to implement". Don't forget to involve everyones effort. Sometimes UI changes might be easiest to develop but hardest to design and test.
You can estimate number of months tasks would take to deliver for one person. If you find this tricky you can use t-shirt size estimation:
- 25 for XS,
- 50 for S,
- 100 for M,
- 200 for L,
- 300 for XL.
The RICE score
Now once you have all your metric you can calculate final RICE score using this formula:
"Luck is what happens when preparation meets opportunity." - Seneca
As you can see the RICE is great method that brings more data-driven approach into the prioritization 📈 It is great for any project size for already established products.