Explain RICE scoring and its Confidence factor

WHAT THIS TESTS This checks that you know the RICE framework precisely and, more importantly, understand why Confidence exists. The interviewer is probing whether you can keep prioritization honest by discounting optimistic guesses, and whether you can make a soft factor objective.

A GOOD ANSWER COVERS RICE scores each idea by Reach times Impact times Confidence divided by Effort. Reach estimates how many users the change touches in a period. Impact estimates how much it moves the target metric per user, often on a fixed scale. Effort is the cost in person-time. Confidence is a multiplier, commonly 100, 80, or 50 percent, that scales down Reach and Impact according to how well-supported those estimates are. Dividing by Effort yields impact per unit of work, so small high-leverage bets surface. To set Confidence objectively, define evidence tiers: ideas backed by past experiments or strong quantitative data get high confidence; ideas backed by user research or analogous results get medium; ideas resting on opinion get low. Documenting the evidence for each score keeps it from drifting into gut feel.

COMMON WRONG ANSWERS Forgetting Confidence entirely, which lets wildly optimistic untested ideas dominate. Treating Confidence as a pure feeling with no rubric. Conflating Confidence with Impact, double-penalizing. Using inconsistent units across ideas so scores are not comparable. Treating RICE output as an exact ranking rather than a structured starting point for discussion.

LIKELY FOLLOW-UPS How do you avoid gaming the scores? Require evidence notes and peer review. What if Effort is highly uncertain? Use ranges or re-estimate after discovery. When does RICE break down? For strategic bets where reach is small but optionality is large.

ONE CONCRETE EXAMPLE Two ideas compete. A homepage banner reaches 100k users with estimated medium impact but rests only on a hunch, so Confidence is 50 percent. A checkout fix reaches 20k users with similar impact but is backed by a prior winning experiment, earning 100 percent confidence and low effort. After applying the formula, the evidence-backed checkout fix scores higher per unit of effort and is prioritized, exactly the discipline Confidence is meant to enforce.