The RICE model enables Product Managers to assess competing ideas, scoring them in different dimensions and deriving a prioritization based on that. The key elements are:
Reach in the sense of how many customers are affected or new prospects will be won, just the plain number can be given here.
Impact is an assessment of how important a new feature is, so for example to what degree it will help win new customers or improve customer satisfaction. For the formula below, the impact can be expressed on a scale from 0.25 (minimal) to 3 (massive).
<CustomHighlight>Confidence/CustomHighlight> comes in to take uncertainty into account such that projects that are too risky are down-scoped. Confidence can be expressed on a percentage scale with 100% being absolutely sure.
Effort is an estimation of the resources required to build a feature, so most often person months. With those data points, the RICE score can be computed according to the following formula:
RICE = Reach * Impact * Confidence / Effort
The key idea is that higher-scoring features shall be prioritized because they promise a bigger bang for the buck.
While useful to know, we recommend to handle with care for several reasons:
Both impact as well as effort are typically unknown or estimated at best.
Estimations of efforts, or sizing, are typically not comparable across teams.
Consequently, applying the simple formula suggests a pseudo-accurate assessment given the raw input data.
Moreover, according to our experience, RICE and related methods typically favor the low-hanging fruit while postponing moonshot projects.
MoSCoW, RICE, KANO model, Walking Skeleton, and others.