name: rice-scoring description: "Prioritize features/projects by Reach, Impact, Confidence, and Effort into a single score." version: 1.0.0 platforms: [linux, macos, windows] metadata: hermes: tags: [rice, prioritization, product, scoring, reach, impact, confidence, effort] related_skills: [moscow, kano-model, the-eisenhower-matrix]

RICE Scoring

Overview

RICE is a quantitative prioritization framework that computes a single comparable score for each feature or initiative. It prevents gut-feel ranking by forcing explicit estimates across four dimensions: how many people are affected, how much it helps them, how sure you are, and how much work it takes.

RICE Score = (Reach × Impact × Confidence) / Effort

Higher score = higher priority. Items are ranked by score descending.

The Four Dimensions

Reach — "How many people, in what time window?"

Count the number of users or customers affected per time period (typically per quarter). Use real data: DAU, MAU, conversion funnel counts, support ticket volume. Do NOT estimate in percentages here — use absolute numbers.

• Example: 2,400 users/quarter who go through the checkout flow

Impact — "How much does this move the needle per person?"

Rate the impact on the individual user when they encounter the feature. Use a fixed scale:

Confidence — "How sure are we about Reach and Impact?"

Express as a percentage reflecting evidence quality:

Never exceed 100%. Round to 100/80/50 — false precision is noise.

Effort — "How many person-months does this take?"

Estimate total engineering + design + PM time in person-months. Minimum value: 0.5 (half a person-month). Do NOT use story points — convert to time.

• 1 engineer for 2 weeks = 0.5 person-months
• 2 engineers + 1 designer for 1 month = 3 person-months

How to Apply

Step 1 — List all candidates

Write out every feature, project, or initiative under consideration. Aim to score at least 5–10 items so the ranking is meaningful.

Step 2 — Estimate each dimension independently

For each item, assign Reach, Impact, Confidence, and Effort without looking at the final score yet. Involve the team — engineering owns Effort, product/data owns Reach, design/research informs Impact and Confidence.

Step 3 — Calculate the score

Score = (Reach × Impact × Confidence%) / Effort

Example: Reach=2400, Impact=2, Confidence=80%, Effort=3 Score = (2400 × 2 × 0.80) / 3 = 1280

Step 4 — Rank and sanity-check

Sort by score descending. Ask: does this order match intuition? If not, find the mismatch — either the intuition is wrong or an estimate is off. Adjust estimates with justification, not to make the answer "look right."

Step 5 — Communicate the tradeoffs

Share the scoring table with stakeholders. The score is a conversation starter, not a decree. Flag items where Confidence is low — they may need a spike or experiment before committing.

Output Format

╔══════════════════════════════════════════════════════════════════════════════════════════════╗
║  RICE SCORING  ►  [product area or initiative set]          TIME WINDOW: [quarter/month]    ║
╠══════════════════════════════════════════════════════════════════════════════════════════════╣
║                                                                                              ║
║    Score  =  ( Reach  ×  Impact  ×  Confidence% )  ÷  Effort        Higher = Higher Pri    ║
║                   ▲          ▲            ▲                ▲                                 ║
║              users/period  0.25–3       50–100%       person-months                         ║
║                                                                                              ║
╚══════════════════════════════════════════════════════════════════════════════════════════════╝
 
┌──────────────────────────────┬───────────┬────────┬────────────┬────────┬────────────┐
│  Item                        │   Reach   │ Impact │ Confidence │ Effort │ RICE Score │
├──────────────────────────────┼───────────┼────────┼────────────┼────────┼────────────┤
│  [Feature A]                 │  [#,###]  │  [#.#] │   [##%]    │  [#.#] │   [#,###]  │
├──────────────────────────────┼───────────┼────────┼────────────┼────────┼────────────┤
│  [Feature B]                 │  [#,###]  │  [#.#] │   [##%]    │  [#.#] │   [#,###]  │
├──────────────────────────────┼───────────┼────────┼────────────┼────────┼────────────┤
│  [Feature C]                 │  [#,###]  │  [#.#] │   [##%]    │  [#.#] │   [#,###]  │
├──────────────────────────────┼───────────┼────────┼────────────┼────────┼────────────┤
│  [Feature D]                 │  [#,###]  │  [#.#] │   [##%]    │  [#.#] │   [#,###]  │
└──────────────────────────────┴───────────┴────────┴────────────┴────────┴────────────┘
 
  RANKING  (sorted highest → lowest)
  ┌────┬─────────────────────────┬──────────────┬──────────────────────────────────────────┐
  │ #  │  Item                   │  RICE Score  │  Rationale                               │
  ├────┼─────────────────────────┼──────────────┼──────────────────────────────────────────┤
  │ 1  │  [Feature X]            │   [#,###]    │  [one-line rationale]                    │
  │ 2  │  [Feature Y]            │   [#,###]    │  [one-line rationale]                    │
  │ 3  │  [Feature Z]            │   [#,###]    │  [one-line rationale]                    │
  └────┴─────────────────────────┴──────────────┴──────────────────────────────────────────┘
 
  ┌─── LOW CONFIDENCE FLAGS ──────────────────────┐  ┌─── RECOMMENDATION ───────────────────────────┐
  │  ● [Item]: needs [research / data / spike]    │  │  ► Act next on [top 1–2 items]               │
  │    before committing                          │  │    because [score + strategic rationale]     │
  └───────────────────────────────────────────────┘  └──────────────────────────────────────────────┘

The scoring table is the source of truth — sort it by RICE Score descending before populating the Ranking section. Flag any item with Confidence ≤ 50% in Low Confidence Flags; these need a research spike or experiment before the work is committed.

Common Mistakes

• Using percentages for Reach. "20% of users" is meaningless without an absolute base. Always convert to a real count per time period.
• Inflating Confidence to avoid hard conversations. If you haven't done the research, 50% is honest. Marking everything 80% defeats the framework.
• Rounding Effort to 1 when it's really 0.5. Small items get artificially deprioritized. Use 0.5 for sub-month work.
• Ignoring strategic bets. A high-confidence, low-Reach compliance item may rank low but be non-negotiable. Flag these separately — RICE ranks by user value, not legal necessity.
• Running RICE in a room without data. The framework exposes gaps in your data, not just priorities. Missing Reach data is a signal to instrument your funnel, not to guess.

Footer

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