Applying RICE to a product scenario

Once the RICE framework is understood in theory, the next step is to see how it works in practice. Imagine a team considering whether to build an onboarding tutorial for a new app. They estimate that the feature will reach about 5,000 new users per month. The impact is judged as medium, since it should improve activation but not directly affect revenue, giving it a value of 2 on a typical scale. The team feels reasonably confident about these estimates, rating confidence at 80 percent. The effort required is calculated at two person-months. When reach, impact, and confidence are multiplied together and divided by effort, the resulting RICE score can be compared with other initiatives.

What makes this exercise powerful is not the number itself but the discussion it generates. Team members must explain why they believe onboarding will affect activation or why they are confident in the data. These conversations often reveal blind spots, such as overlooked dependencies or uncertain metrics. By applying RICE to a real case, even a simplified one, teams practice moving from intuition to structured reasoning. It also shows that RICE is not about eliminating judgment but about anchoring decisions in transparent criteria that can be reviewed and challenged.