Prioritising with RICE and MoSCoW

01Why use a scoring method

Teams always have more ideas than time. Without a method, the loudest opinion or the newest idea wins. A prioritisation framework makes the reasoning visible so people can discuss assumptions rather than preferences.

02RICE

RICE, popularised by Intercom, scores each item with four factors and combines them:

Score = (Reach × Impact × Confidence) ÷ Effort

• Reach: how many people the item affects in a set period.
• Impact: how much it moves the goal for each person, on a simple agreed scale.
• Confidence: how sure you are of the estimates, as a percentage.
• Effort: the work required, for example in person-weeks.

Illustrative example with made-up numbers. Item A: reach 2,000, impact 1, confidence 80%, effort 2. Score = (2000 × 1 × 0.8) ÷ 2 = 800. Item B: reach 500, impact 3, confidence 50%, effort 3. Score = (500 × 3 × 0.5) ÷ 3 = 250. Item A ranks higher even though B has a bigger impact per person, because it reaches more people, is better evidenced and costs less.

03MoSCoW

MoSCoW sorts items into four groups rather than scoring them:

• Must have

  Without it the release fails or is unusable.

• Should have

  Important, but there is a workaround.

• Could have

  Nice to have if time allows.

• Won’t have (this time)

  Agreed to be out of scope for now.

04Pitfalls and simpler options

• False precision. A score of 800 versus 750 is not a meaningful difference when inputs are rough estimates.
• Gaming confidence. Teams can inflate confidence to push a favourite idea. Tie confidence to evidence, such as research or data.
• Scoring alone. Estimate together so that different roles challenge each other.

For a small backlog, a simple impact versus effort grid is often enough: do high impact, low effort first.

05Scoring honestly

Scores are only as good as the conversation behind them. Agree on definitions before scoring so everyone uses the same scale.

1. Define the goal the score serves, such as activation or retention, and measure impact against it.
2. Agree on a small impact scale with written meanings for each level.
3. Base reach on data where possible, and write down where a number is a guess.
4. Review the ranked list and ask whether it feels wrong. If so, find which assumption is off rather than editing the score.

Treat the output as a conversation starter. Dependencies, risks and strategy still need human judgement.