impact-estimation

name	impact-estimation
description	Use when comparing multiple features or initiatives to determine prioritization order. Covers RICE scoring with quantified reach, impact, confidence, and effort dimensions, tier ranking, and success metric definition. Do not use for MVP scope definition (use mvp-scoping) or analytics instrumentation planning (use analytics-design).
metadata	{"author":"dtsong"}

Impact Estimation

Purpose

Apply RICE scoring to prioritize features and initiatives based on quantified reach, impact, confidence, and effort, replacing gut-feel prioritization with a repeatable framework.

Scope Constraints

Produces prioritization scores and tier rankings based on provided estimates and data. Does not generate usage data, validate assumptions empirically, or make final prioritization decisions. RICE outputs are advisory and require stakeholder review before commitment.

Inputs

• List of features or initiatives to prioritize
• User base size and segmentation (for Reach estimation)
• Available team capacity (for Effort calibration)
• Business goals and success metrics
• Any existing data (usage analytics, user research, market data)

Input Sanitization

No user-provided values are used in commands or file paths. All inputs are treated as read-only analysis targets.

Procedure

Progress Checklist

• Step 1: RICE criteria defined
• Step 2: Features scored
• Step 3: RICE scores calculated
• Step 4: Features ranked
• Step 5: Quick wins vs strategic bets identified
• Step 6: Success metrics defined

Step 1: Define RICE Criteria

Calibrate the scoring dimensions for this project:

• Reach: How many users or customers will this affect per quarter? Use real numbers where possible (e.g., "500 active users" not "many users"). For internal tools, count affected team members.
• Impact: How much will each affected user benefit?
  • 3 = Massive (transforms their workflow, solves a critical pain point)
  • 2 = High (significant improvement, removes notable friction)
  • 1 = Medium (noticeable improvement, nice to have)
  • 0.5 = Low (minor improvement, slight convenience)
  • 0.25 = Minimal (barely noticeable, edge case benefit)
• Confidence: How sure are we about Reach and Impact estimates?
  • 100% = High confidence (backed by data, user research, or direct requests)
  • 80% = Medium confidence (strong signals but some assumptions)
  • 50% = Low confidence (educated guess, limited data)
  • 20% = Moonshot (speculative, unvalidated assumption)
• Effort: Person-months of work (including design, development, testing, deployment). Use 0.5 as minimum for small tasks.

Step 2: Score Each Feature on All 4 Dimensions

For each feature/initiative, provide:

• Reach number with source/rationale
• Impact score with justification
• Confidence percentage with evidence basis
• Effort estimate with scope description

Be honest about confidence — inflated confidence undermines the entire framework.

Step 3: Calculate RICE Score

Formula: RICE = (Reach x Impact x Confidence) / Effort

• Higher scores indicate higher priority
• Calculate for every feature to enable direct comparison
• Show the math for transparency

Step 4: Rank Features by RICE Score

• Sort all features by RICE score descending
• Group into tiers:
  • Tier 1: Top quartile — prioritize immediately
  • Tier 2: Second quartile — plan for next cycle
  • Tier 3: Third quartile — consider if capacity allows
  • Tier 4: Bottom quartile — deprioritize or reconsider

Step 5: Identify Quick Wins vs Strategic Bets

Classify by effort and score:

• Quick Wins: High RICE score + Low effort (< 1 person-month). Do these first.
• Strategic Bets: High RICE score + High effort (> 2 person-months). Plan carefully, consider phasing.
• Low-Hanging Fruit: Medium RICE score + Very low effort (< 0.5 person-month). Fill gaps in sprints.
• Money Pits: Low RICE score + High effort. Avoid or fundamentally rethink.

Step 6: Define Success Metrics and KPIs

For each prioritized feature, define:

• Primary metric: The one number that indicates success
• Leading indicators: Early signals that predict the primary metric
• Guardrail metrics: Things that should NOT get worse (e.g., performance, error rate)
• Measurement method: How and when you'll measure
• Target: Specific number or threshold for success

Compaction resilience: If context was lost during a long session, re-read the Inputs section to reconstruct the feature list and scoring context, check the Progress Checklist for completed steps, then resume from the earliest incomplete step.

Output Format

RICE Scoring Table

Feature	Reach	Impact	Confidence	Effort	RICE Score	Tier
Feature A	1000	3	80%	2	1200	1
Feature B	500	2	100%	0.5	2000	1
Feature C	200	1	50%	3	33	3
...	...	...	...	...	...	...

Priority-Ranked Feature List

1. Feature B (RICE: 2000) — Quick Win
2. Feature A (RICE: 1200) — Strategic Bet
3. ...

Quick Wins vs Strategic Bets

Category	Features	Combined Effort	Expected Impact
Quick Wins	Feature B, ...	... person-months	...
Strategic Bets	Feature A, ...	... person-months	...
Low-Hanging Fruit	...	...	...
Money Pits	Feature C, ...	...	Deprioritize

Success Metrics per Feature

Feature	Primary Metric	Target	Leading Indicator	Guardrail
Feature A	...	...	...	...
Feature B	...	...	...	...

Handoff

• Hand off to mvp-scoping if prioritized features need MoSCoW classification and phased roadmap planning.
• Hand off to analytics-design if success metrics require telemetry instrumentation or A/B test framework design.

Quality Checks

• Reach estimates use real numbers (not vague qualifiers)
• Impact scores include justification for each rating
• Confidence percentages are honest (not all 80%)
• Effort estimates account for design, dev, testing, and deployment
• RICE math is shown and correct
• Features are ranked and tiered by score
• Quick wins vs strategic bets are clearly classified
• Success metrics are defined with specific targets

Evolution Notes

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