Impact Estimation

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.

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)

Process

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

Output Format

RICE Scoring Table

Priority-Ranked Feature List

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

Quick Wins vs Strategic Bets

Success Metrics per Feature

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