ICE-based Idea Prioritization (Evidence-Guided)

Goal

Ingest an idea description and current state, score it on Impact, Confidence, and Ease, and compute the ICE Score = Impact × Confidence × Ease to propose an execution priority. All evaluations must follow explicit criteria and rely only on stated evidence—no inference or guesswork.

When to Use

• To quickly order an idea backlog and select items for exploration and experiments

• When you have at least some explicit evidence (interviews/data/tests) and a rough team effort estimate

• Before drafting experiment plans for a leaf opportunity in an Opportunity-Solution Tree

Input

• Idea title and description: goal, target metric, scope, and working hypothesis

• Idea analysis and current state: data/user/market/test evidence, execution hypothesis, risks, and effort estimate

• Optional:

• Target metric and expected change rate (%) or range

• Estimated effort (person-weeks)

Output

• Format: Markdown (.md )

• Location: initiatives/[initiative]/solutions/

• Filename: ice-[YYYY-MM-DD]-[slugified-idea-title].md

Scoring Model

1. Impact Mapping

Target Metric Change (%) Impact

50% 10

35 - 49.9% 9

25 - 34.9% 8

18 - 24.9% 7

12 - 17.9% 6

7 - 11.9% 5

4 - 6.9% 4

2 - 3.9% 3

0.5 - 1.9% 2

0.1 - 0.4% 1

≤ 0% 0

• Missing data handling: If no explicit percentage is provided, first ask the user for an estimate. If the user cannot provide one, apply default +1.5% improvement (Impact 2) and add a warning in the output: ⚠️ DEFAULT VALUE: Impact uses assumed +1.5% improvement due to missing data .

2. Ease Mapping (Estimated Effort: person-weeks)

Duration Ease

< 1 week 10

1–2 weeks 9

3–4 weeks 8

5–6 weeks 7

6–7 weeks 6

8–9 weeks 5

10–12 weeks 4

13–16 weeks 3

17–25 weeks 2

≥ 26 weeks 1

3. Evidence Types (Count only evidence directly tied to Impact)

Evidence Type Description

Test Results A/B tests, longitudinal user studies, beta experiments, large MVPs with quantitative validation

User-based Evidence Product usage data, 20+ user interviews, usability studies, MVP results/feedback

Market Data Surveys, smoke tests, "table stakes" in the competitive set

Empirical Evidence Few data points, sales requests, 1–3 interested customers, one competitor has the feature

Estimates & Plans Internal model-based estimates, feasibility review with Eng/Design, schedule/business model analysis

Opinions of Others Executives/colleagues/experts/investors opinions

Directional Fit Alignment with company vision/strategy, tech/market trends, external research, macro trends

Self-belief Personal intuition/gut feel/experience

• Caution: Use only explicitly stated evidence in the input. No inference.

• Statements like "intuitively", "personally I think", "my gut says" → classify as Self-belief.

• Without explicit quantitative backing, do not accept as Market Data or Estimates & Plans.

4. Confidence Calculation

• Principle: Include only evidence that directly supports Impact.

• Per-type contribution = MIN(Weight × count, Max)

• Group caps (sum upper bounds):

• Self-belief + Directional Fit ≤ 0.1

• Opinions of Others + Estimates & Plans ≤ 0.5

• Market Data + User-based Evidence ≤ 3.0

Evidence Type Weight Max

Self-belief 0.01 0.1

Directional Fit 0.05 0.1

Opinions of Others 0.10 0.5

Estimates & Plans 0.30 0.5

Empirical Evidence 0.50 1.0

Market Data 1.0 3.0

User-based Evidence 2.0 3.0

Test Results 3.0 5.0

• Keyword hints (examples): "test/experiment/AB", "user request/behavioral data", "market/competitor/table stakes", "estimate/modeling", "intuition/gut/personally".

5. ICE Score and Priority Interpretation

• Formula: ICE = Impact × Confidence × Ease

• Interpretation:

• ≥ 250: Consider immediate execution (high expected ROI)

• 150–249: Promising; recommend additional precision testing

• 100–149: Proceed with mitigations or phase-two testing

• < 100: On hold or needs strengthening

Process

• Input validation

• Verify target metric, expected change (%), evidence text, and estimated effort (person-weeks)

• If missing, ask clarifying questions about metric/change, effort, and evidence type/source

• Impact scoring

• Map % change to table; if missing, apply default Impact 2

• Ease scoring

• Map person-weeks to table; if uncertain, use conservative lower ease

• Evidence extraction and classification

• Count only Impact-related evidence from the input

• Tally per type and apply group caps

• Confidence calculation

• Sum per-type contributions → apply group caps → final Confidence (0–10)

• ICE computation and bucket

• Compute ICE = I × C × E; assign interpretation bucket

• Report generation

• Include score table, calculation rationale, cap applications, risks/assumptions, and recommended next steps

Output Format

ICE Evaluation — [Idea Title]

Overview

• Idea: [Title]
• One-line Summary: [Brief description]
• Target Metric: [Metric name]
• Assumptions/Scope: [Key assumptions]

Score Summary

• Impact: [I] (basis: [expected % change or default rule])
• Ease: [E] (basis: [person-weeks])
• Confidence: [C]
  • Details:
    • Self-belief: 0.01 × [n] → [x] (max 0.1, Group A ≤ 0.1)
    • Directional Fit: 0.05 × [n] → [x]
    • Opinions of Others: 0.10 × [n] → [x] (Group B ≤ 0.5)
    • Estimates & Plans: 0.30 × [n] → [x]
    • Empirical Evidence: 0.50 × [n] → [x] (max 1.0)
    • Market Data: 1.0 × [n] → [x] (Group C ≤ 3.0)
    • User-based Evidence: 2.0 × [n] → [x]
    • Test Results: 3.0 × [n] → [x] (max 5.0)
  • Group caps applied:
    • Group A (Self-belief + Directional Fit): [sum] → [capped]
    • Group B (Opinions + Estimates): [sum] → [capped]
    • Group C (Market + User): [sum] → [capped]
  • Final Confidence: [C]

ICE Calculation

• ICE = [I] × [C] × [E] = [Score]
• Priority Guidance: [Bucket label]

Input Summary

• Expected Metric Change: [value/none → default 1.5% applied]
• Estimated Effort (person-weeks): [value/uncertain]
• Evidence Excerpts:
  • [Excerpt 1 — classified as: user/market/test/...]
  • [Excerpt 2 — classified as: ...]

Risks / Assumptions

• [Key risk]
• [Key uncertainty]
• [Critical assumption]

Recommended Next Steps

• [Tests/data collection/research/prototype]
• Confidence Improvement Plan: [Which evidence to strengthen]

Notes

• ICE is a fast comparison/sorting tool; final decisions must also consider strategy, market, and resources.

Example

• Idea: "AI-based revenue anomaly detection dashboard"

• Expected change: 26% → Impact 8

• Effort: 5 weeks → Ease 7

• Confidence input:

• User-based Evidence (3) → 2.0 × 3 = 6.0 → Group C cap (≤ 3.0) → 3.0

• Estimates & Plans (2) → 0.3 × 2 = 0.6 → Group B cap (≤ 0.5) → 0.5

• Total: 3.0 + 0.5 = 3.5 → Final C = 3.5

• ICE = 8 × 3.5 × 7 = 196 → "Promising; recommend additional precision testing"

Customization (Team Tuning)

• Adjust Impact bands to your target metric sensitivity

• Adjust Ease bands to team speed/role mix

• Extend evidence keywords to your domain language, while preserving the "explicit evidence only" rule

• Recalibrate bucket thresholds per quarterly capacity/roadmap density

Guardrails

• Do not invent/infer evidence or over-credit weak signals

• Exclude evidence not directly tied to Impact from Confidence

• When uncertain, apply conservative caps and document in "Risks/Assumptions"

• Prevent duplicate counting of the same source/content

Error Handling

• Missing % change/effort: apply default rules (Impact 2) or ask clarifying questions

• Insufficient evidence: request the needed evidence types with examples

• Conflicting info: note conflicts and dependent assumptions; use conservative scoring

Interaction Model

• Collect & validate inputs → 2) Score I/E → 3) Classify/count evidence → 4) Compute Confidence (with caps) → 5) Compute ICE & assign bucket → 6) Generate report → 7) Resolve gaps/uncertainties and update