rice-prioritization

name	rice-prioritization
description	Worked RICE scoring playbook for evidence-based backlog prioritization — covers calibrating each factor, avoiding common scoring errors, and using the output to run a defensible prioritization meeting.

RICE Prioritization

When to Use This

You have 5+ competing initiatives and need a transparent, arguable ranking that isn't driven by whoever speaks loudest. RICE produces a single comparable score so trade-offs are explicit. Use it at quarterly planning or when the backlog needs a defensible cut.

The Formula

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

Calibrating Each Factor

Reach (users or events per time period)

• Use a consistent time window across all items — usually per quarter.
• Source from actual data: DAU/MAU, segment size from your analytics tool, support ticket volume.
• Estimate the users who will encounter the feature, not total user base.

Example	Reach
Affects all checkout users (10,000/qtr)	10,000
Affects users on mobile only (2,000/qtr)	2,000
Internal tool for 15 ops staff	15

Impact (per user/event)

Use a fixed scale — never raw numbers:

Impact level	Score
Massive (core job done)	3
High	2
Medium	1
Low	0.5
Minimal	0.25

Force-rank impact before scoring: if everything is "High," your scale is broken.

Confidence (how certain are you about the estimates)

Evidence level	Confidence
Strong data + user research	100%
Partial data or limited interviews	80%
Hunch / analogous market data	50%
Pure guess	20%

Confidence is the error-correction term. Use it — the team's natural optimism bias means 80% should be the ceiling for most items without hard data.

Effort (person-months)

Use the same unit for all items. Person-months is more stable than story points across items. Round to 0.5 increments for small work.

Worked Example

Initiative	Reach	Impact	Confidence	Effort	Score
Streamline onboarding step 2	4,000	2	80%	1	6,400
Add bulk export	500	3	100%	0.5	3,000
New dashboard widget	8,000	0.5	50%	2	1,000
API rate-limit increase	200	3	80%	0.5	960

Onboarding wins — high reach, meaningful impact, reasonable confidence, low effort.

Running the Prioritization Meeting

1. Score individually first — have each participant score before the meeting; reveal together to surface disagreements.
2. Debate outlier gaps — if estimates diverge by > 2× on any factor, that factor needs more information, not more negotiation.
3. Flag forced items — contractual commitments, compliance deadlines, and strategic bets that won't move regardless of score. Put them in a separate list; don't let them pollute the scored ranking.
4. The output is a ranked list with explicit assumptions — publish scores with the reasoning, not just the rank order, so future teams can re-score with updated data.

When RICE Breaks Down

• When items aren't comparable in type — scoring a regulatory compliance item against a growth feature doesn't work; use separate tracks.
• When the team can't agree on reach — invest in instrumentation first; scoring without data produces confident nonsense.
• When the horizon is too short — RICE is a quarterly/roadmap tool; for a two-day sprint, just pick the highest-value unblocked item.

Pitfalls

• Inflating Confidence to avoid uncomfortable trade-offs — 100% confidence means you have data, not that you feel strongly.
• Scoring Effort in story points while comparing across teams with different velocity — normalize to a common unit.
• Treating the score as final without flagging dependencies — a high-score item blocked by another item still can't be started first.
• Skipping the "debate outlier gaps" step — the value of RICE is the forced calibration conversation, not the number itself.

See Also

• ../../agents/product-metrics-analyst.md — evidence-based prioritization and funnel analysis
• ../../agents/product-strategist.md — roadmap as a set of bets; strategic override items