Decision Matrix

Decision Matrix

Overview

A decision matrix scores each option on each criterion, making subjective factors visible and comparable. It includes weighted criteria, sensitivity analysis, and clear recommendations.

Quick example:

Option A wins despite not being fastest or cheapest because quality matters most (45% weight).

Workflow

Copy this checklist and track your progress:

Decision Matrix Progress:
- [ ] Step 1: Frame the decision and list alternatives
- [ ] Step 2: Identify and weight criteria
- [ ] Step 3: Score each alternative on each criterion
- [ ] Step 4: Calculate weighted scores and analyze results
- [ ] Step 5: Validate quality and deliver recommendation

Step 1: Frame the decision and list alternatives

Ask user for decision context (what are we choosing and why), list of alternatives (specific named options, not generic categories), constraints or dealbreakers (must-have requirements), and stakeholders (who needs to agree). Understanding must-haves helps filter options before scoring. See Framing Questions for clarification prompts.

Step 2: Identify and weight criteria

Collaborate with user to identify criteria (what factors matter for this decision), determine weights (which criteria matter most, as percentages summing to 100%), and validate coverage (do criteria capture all important trade-offs). If user is unsure about weighting → Use resources/template.md for weighting techniques. See Criterion Types for common patterns.

Step 3: Score each alternative on each criterion

For each option, score on each criterion using consistent scale (typically 1-10 where 10 = best). Ask user for scores or research objective data (cost, speed metrics) where available. Document assumptions and data sources. For complex scoring → See resources/methodology.md for calibration techniques.

Step 4: Calculate weighted scores and analyze results

Calculate weighted score for each option (sum of criterion score × weight). Rank options by total score. Identify close calls (options within 5% of each other). Check for sensitivity (would changing one weight flip the decision). See Sensitivity Analysis for interpretation guidance.

Step 5: Validate quality and deliver recommendation

Self-assess using resources/evaluators/rubric_decision_matrix.json (minimum score ≥ 3.5). Present decision-matrix.md file with clear recommendation, highlight key trade-offs revealed by analysis, note sensitivity to assumptions, and suggest next steps (gather more data on close calls, validate with stakeholders).

Framing Questions

To clarify the decision:

• What specific decision are we making? (Choose X from Y alternatives)
• What happens if we don't decide or choose wrong?
• When do we need to decide by?
• Can we choose multiple options or only one?

To identify alternatives:

• What are all the named options we're considering?
• Are there other alternatives we're ruling out immediately? Why?
• What's the "do nothing" or status quo option?

To surface must-haves:

• Are there absolute dealbreakers? (Budget cap, timeline requirement, compliance need)
• Which constraints are flexible vs rigid?

Criterion Types

Common categories for criteria (adapt to your decision):

Financial Criteria:

• Upfront cost, ongoing cost, ROI, payback period, budget impact
• Typical weight: 20-40% (higher for cost-sensitive decisions)

Performance Criteria:

• Speed, quality, reliability, scalability, capacity, throughput
• Typical weight: 30-50% (higher for technical decisions)

Risk Criteria:

• Implementation risk, reversibility, vendor lock-in, technical debt, compliance risk
• Typical weight: 10-25% (higher for enterprise/regulated environments)

Strategic Criteria:

• Alignment with goals, future flexibility, competitive advantage, market positioning
• Typical weight: 15-30% (higher for long-term decisions)

Operational Criteria:

• Ease of use, maintenance burden, training required, integration complexity
• Typical weight: 10-20% (higher for internal tools)

Stakeholder Criteria:

• Team preference, user satisfaction, executive alignment, customer impact
• Typical weight: 5-15% (higher for change management contexts)

Weighting Approaches

Method 1: Direct Allocation (simplest) Stakeholders assign percentages totaling 100%. Quick but can be arbitrary.

Method 2: Pairwise Comparison (more rigorous) Compare each criterion pair: "Is cost more important than speed?" Build ranking, then assign weights.

Method 3: Must-Have vs Nice-to-Have (filters first) Separate absolute requirements (pass/fail) from weighted criteria. Only evaluate options that pass must-haves.

Method 4: Stakeholder Averaging (group decisions) Each stakeholder assigns weights independently, then average. Reveals divergence in priorities.

See resources/methodology.md for detailed facilitation techniques.

Sensitivity Analysis

After calculating scores, check robustness:

1. Close calls: Options within 5-10% of winner → Need more data or second opinion 2. Dominant criteria: One criterion driving entire decision → Is weight too high? 3. Weight sensitivity: Would swapping two criterion weights flip the winner? → Decision is fragile 4. Score sensitivity: Would adjusting one score by ±1 point flip the winner? → Decision is sensitive to that data point

Red flags:

• Winner changes with small weight adjustments → Need stakeholder alignment on priorities
• One option wins every criterion → Matrix is overkill, choice is obvious
• Scores are mostly guesses → Gather more data before deciding

Common Patterns

Technology Selection:

• Criteria: Cost, performance, ecosystem maturity, team familiarity, vendor support
• Weight: Performance and maturity typically 50%+

Vendor Evaluation:

• Criteria: Price, features, integration, support, reputation, contract terms
• Weight: Features and integration typically 40-50%

Strategic Choices:

• Criteria: Market opportunity, resource requirements, risk, alignment, timing
• Weight: Market opportunity and alignment typically 50%+

Hiring Decisions:

• Criteria: Experience, culture fit, growth potential, compensation expectations, availability
• Weight: Experience and culture fit typically 50%+

Feature Prioritization:

• Criteria: User impact, effort, strategic value, risk, dependencies
• Weight: User impact and strategic value typically 50%+

When NOT to Use This Skill

Skip decision matrix if:

• Only one viable option (no real alternatives to compare)
• Decision is binary yes/no with single criterion (use simpler analysis)
• Options differ on only one dimension (just compare that dimension)
• Decision is urgent and stakes are low (analysis overhead not worth it)
• Criteria are impossible to define objectively (purely emotional/aesthetic choice)
• You already know the answer (using matrix to justify pre-made decision is waste)

Use instead:

• Single criterion → Simple ranking or threshold check
• Binary decision → Pro/con list or expected value calculation
• Highly uncertain → Scenario planning or decision tree
• Purely subjective → Gut check or user preference vote

Quick Reference

Process: 1. Frame decision → List alternatives 2. Identify criteria → Assign weights (sum to 100%) 3. Score each option on each criterion (1-10 scale) 4. Calculate weighted scores → Rank options 5. Check sensitivity → Deliver recommendation

Resources:

• resources/template.md - Structured matrix format and weighting techniques
• resources/methodology.md - Advanced techniques (group facilitation, calibration, sensitivity analysis)
• resources/evaluators/rubric_decision_matrix.json - Quality checklist before delivering

Deliverable: decision-matrix.md file with table, rationale, and recommendation

Decision Matrix: Advanced Methodology

Workflow

Copy this checklist for complex decision scenarios:

Advanced Decision Matrix Progress:
- [ ] Step 1: Diagnose decision complexity
- [ ] Step 2: Apply advanced weighting techniques
- [ ] Step 3: Calibrate and normalize scores
- [ ] Step 4: Perform rigorous sensitivity analysis
- [ ] Step 5: Facilitate group convergence

Step 1: Diagnose decision complexity - Identify complexity factors (stakeholder disagreement, high uncertainty, strategic importance). See 1. Decision Complexity Assessment.

Step 2: Apply advanced weighting techniques - Use AHP or other rigorous methods for contentious decisions. See 2. Advanced Weighting Methods.

Step 3: Calibrate and normalize scores - Handle different scoring approaches and normalize across scorers. See 3. Score Calibration & Normalization.

Step 4: Perform rigorous sensitivity analysis - Test decision robustness with Monte Carlo or scenario analysis. See 4. Advanced Sensitivity Analysis.

Step 5: Facilitate group convergence - Use Delphi method or consensus-building techniques. See 5. Group Decision Facilitation.

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1. Decision Complexity Assessment

Complexity Indicators

Low Complexity (use basic template):

• Clear stakeholder alignment on priorities
• Objective criteria with available data
• Low stakes (reversible decision)
• 3-5 alternatives

Medium Complexity (use enhanced techniques):

• Moderate stakeholder disagreement
• Mix of objective and subjective criteria
• Moderate stakes (partially reversible)
• 5-8 alternatives

High Complexity (use full methodology):

• Significant stakeholder disagreement on priorities
• Mostly subjective criteria or high uncertainty
• High stakes (irreversible or strategic decision)
• >8 alternatives or multi-phase decision
• Regulatory or compliance implications

Complexity Scoring

Complexity Score = Sum of factors

• 5-7 points: Use basic template
• 8-11 points: Use enhanced techniques (sections 2-3)
• 12-15 points: Use full methodology (all sections)

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2. Advanced Weighting Methods

Analytic Hierarchy Process (AHP)

When to use: High-stakes decisions with contentious priorities, need rigorous justification

Process:

1. Create pairwise comparison matrix: For each pair, rate 1-9 (1=equal, 3=slightly more important, 5=moderately, 7=strongly, 9=extremely) 2. Calculate weights: Normalize columns, average rows 3. Check consistency: CR < 0.10 acceptable (use online AHP calculator: bpmsg.com/ahp/ahp-calc.php)

Example: Comparing Cost, Performance, Risk, Ease pairwise yields weights: Performance 55%, Risk 20%, Cost 15%, Ease 10%

Advantage: Rigorous, forces logical consistency in pairwise judgments.

Swing Weighting

When to use: Need to justify weights based on value difference, not just importance

Process:

1. Baseline: Imagine all criteria at worst level 2. Swing: For each criterion, ask "What value does moving from worst to best create?" 3. Rank swings: Which swing creates most value? 4. Assign points: Give highest swing 100 points, others relative to it 5. Convert to weights: Normalize points to percentages

Example:

Total points: 220 → Weights: 100/220=45%, 60/220=27%, 50/220=23%, 10/220=5%

Advantage: Focuses on marginal value, not abstract importance. Reveals if criteria with wide option variance should be weighted higher.

Multi-Voting (Group Weighting)

When to use: Group of 5-15 stakeholders needs to converge on weights

Process:

1. Round 1 - Individual allocation: Each person assigns 100 points across criteria 2. Reveal distribution: Show average and variance for each criterion 3. Discuss outliers: Why did some assign 40% to Cost while others assigned 10%? 4. Round 2 - Revised allocation: Re-allocate with new information 5. Converge: Repeat until variance is acceptable or use average

Example:

Convergence achieved when variance <±5% for all criteria.

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3. Score Calibration & Normalization

Handling Different Scorer Tendencies

Problem: Some scorers are "hard graders" (6-7 range), others are "easy graders" (8-9 range). This skews results.

Solution: Z-score normalization

Step 1: Calculate each scorer's mean and standard deviation

Scorer A: Gave scores [8, 9, 7, 8] → Mean=8, SD=0.8 Scorer B: Gave scores [5, 6, 4, 6] → Mean=5.25, SD=0.8

Step 2: Normalize each score

Z-score = (Raw Score - Scorer Mean) / Scorer SD

Step 3: Re-scale to 1-10

Normalized Score = 5.5 + (Z-score × 1.5)

Result: Scorers are calibrated to same scale, eliminating grading bias.

Dealing with Missing Data

Scenario: Some alternatives can't be scored on all criteria (e.g., vendor A won't share cost until later).

Approach 1: Conditional matrix

Score available criteria only, note which are missing. Once data arrives, re-run matrix.

Approach 2: Pessimistic/Optimistic bounds

Assign worst-case and best-case scores for missing data. Run matrix twice:

• Pessimistic scenario: Missing data gets low score (e.g., 3)
• Optimistic scenario: Missing data gets high score (e.g., 8)

If same option wins both scenarios → Decision is robust. If different winners → Missing data is decision-critical, must obtain before deciding.

Non-Linear Scoring Curves

Problem: Not all criteria are linear. E.g., cost difference between $10K and $20K matters more than $110K vs $120K.

Solution: Apply utility curves

Diminishing returns curve (Cost, Time):

• Score = 10 × (1 - e^(-k × Cost Improvement))
• k = sensitivity parameter (higher k = faster diminishing returns)

Threshold curve (Must meet minimum):

• Score = 0 if below threshold
• Score = 1-10 linear above threshold

Example: Load time criterion with 2-second threshold:

• Option A: 1.5s → Score = 10 (below threshold = great)
• Option B: 3s → Score = 5 (above threshold, linear penalty)
• Option C: 5s → Score = 1 (way above threshold)

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4. Advanced Sensitivity Analysis

Monte Carlo Sensitivity

When to use: High uncertainty in scores, want to understand probability distribution of outcomes

Process:

1. Define uncertainty ranges for each score

• Option A Cost score: 6 ± 2 (could be 4-8)
• Option A Performance: 9 ± 0.5 (could be 8.5-9.5)

2. Run simulations (1000+ iterations):

• Randomly sample scores within uncertainty ranges
• Calculate weighted total for each option
• Record winner

3. Analyze results:

• Option A wins: 650/1000 = 65% probability
• Option B wins: 300/1000 = 30% probability
• Option C wins: 50/1000 = 5% probability

Interpretation:

• >80% win rate: High confidence in decision
• 50-80% win rate: Moderate confidence, option is likely but not certain
• <50% win rate: Low confidence, gather more data or consider decision is close call

Tools: Excel (=RANDBETWEEN or =NORM.INV), Python (numpy.random), R (rnorm)

Scenario Analysis

When to use: Future is uncertain, decisions need to be robust across scenarios

Process:

1. Define scenarios (typically 3-4):

• Best case: Favorable market conditions
• Base case: Expected conditions
• Worst case: Unfavorable conditions
• Black swan: Unlikely but high-impact event

2. Adjust criterion weights or scores per scenario:

3. Run matrix for each scenario, identify winner

4. Evaluate robustness:

• Dominant option: Wins in all scenarios → Robust choice
• Scenario-dependent: Different winners → Need to assess scenario likelihood
• Mixed: Wins in base + one other → Moderately robust

Threshold Analysis

Question: At what weight does the decision flip?

Process:

1. Vary one criterion weight from 0% to 100% (keeping others proportional) 2. Plot total scores for all options vs. weight 3. Identify crossover point where lines intersect (decision flips)

Example:

When Performance weight < 25% → Option B wins (cost-optimized) When Performance weight > 25% → Option A wins (performance-optimized)

Insight: Current weight is 40% for Performance. Decision is robust unless Performance drops below 25% importance.

Practical use: Communicate to stakeholders: "Even if we reduce Performance priority to 25% (vs current 40%), Option A still wins. Decision is robust."

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5. Group Decision Facilitation

Delphi Method (Asynchronous Consensus)

When to use: Experts geographically distributed, want to avoid groupthink, need convergence without meetings

Process:

Round 1:

• Each expert scores options independently (no discussion)
• Facilitator compiles scores, calculates median and range

Round 2:

• Share Round 1 results (anonymous)
• Experts see median scores and outliers
• Ask experts to re-score, especially if they were outliers (optional: provide reasoning)

Round 3:

• Share Round 2 results
• Experts make final adjustments
• Converge on consensus scores (median or mean)

Convergence criteria: Standard deviation of scores <1.5 points per criterion

Example:

Advantage: Avoids dominance by loudest voice, reduces groupthink, allows reflection time.

Nominal Group Technique (Structured Meeting)

When to use: In-person or virtual meeting, need structured discussion to surface disagreements

Process:

1. Silent generation (10 min): Each person scores options independently 2. Round-robin sharing (20 min): Each person shares one score and rationale (no debate yet) 3. Discussion (30 min): Debate differences, especially outliers 4. Re-vote (5 min): Independent re-scoring after hearing perspectives 5. Aggregation: Calculate final scores (mean or median)

Facilitation tips:

• Enforce "no interruptions" during round-robin
• Time-box discussion to avoid analysis paralysis
• Focus debate on criteria with widest score variance

Handling Persistent Disagreement

Scenario: After multiple rounds, stakeholders still disagree on weights or scores.

Options:

1. Separate matrices by stakeholder group:

Run matrix for Engineering priorities, Sales priorities, Executive priorities separately. Present all three results. Highlight where recommendations align vs. differ.

2. Escalate to decision-maker:

Present divergence transparently: "Engineering weights Performance at 60%, Sales weights Cost at 50%. Under Engineering weights, Option A wins. Under Sales weights, Option B wins. Recommendation: [Decision-maker] must adjudicate priority trade-off."

3. Multi-criteria satisficing:

Instead of optimizing weighted sum, find option that meets minimum thresholds on all criteria. This avoids weighting debate.

Example: Option must score ≥7 on Performance AND ≤$50K cost AND ≥6 on Ease of Use. Find options that satisfy all constraints.

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6. Matrix Variations & Extensions

Weighted Pros/Cons Matrix

Hybrid: Add "Key Pros/Cons/Dealbreakers" columns to matrix for qualitative context alongside quantitative scores.

Multi-Phase Decision Matrix

Phase 1: High-level filter (simple criteria) → shortlist top 3 Phase 2: Deep-dive (detailed criteria) → select winner Avoids analysis paralysis by not deep-diving on all options upfront.

Risk-Adjusted Matrix

For uncertain scores, use expected value: (Optimistic + 4×Most Likely + Pessimistic) / 6 Accounts for score uncertainty in final weighted total.

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7. Common Failure Modes & Recovery

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8. When to Abandon the Matrix

Despite best efforts, sometimes a decision matrix is not the right tool:

Abandon if:

1. Purely emotional decision: Choosing baby name, selecting wedding venue (no "right" answer)

• Use instead: Gut feel, user preference vote

2. Single dominant criterion: Only cost matters, everything else is noise

• Use instead: Simple cost comparison table

3. Decision already made: Political realities mean decision is predetermined

• Use instead: Document decision rationale (not fake analysis)

4. Future is too uncertain: Can't meaningfully score because context will change dramatically

• Use instead: Scenario planning, real options analysis, reversible pilot

5. Stakeholders distrust process: Matrix seen as "math washing" to impose decision

• Use instead: Deliberative dialog, voting, or delegated authority

Recognize when structured analysis adds value vs. when it's theater. Decision matrices work best when:

• Multiple alternatives genuinely exist
• Trade-offs are real and must be balanced
• Stakeholders benefit from transparency
• Data is available or can be gathered
• Decision is reversible if matrix misleads

If these don't hold, consider alternative decision frameworks.

Decision Matrix Template

Workflow

Copy this checklist and track your progress:

Decision Matrix Progress:
- [ ] Step 1: Frame the decision
- [ ] Step 2: Identify criteria and assign weights
- [ ] Step 3: Score alternatives
- [ ] Step 4: Calculate and analyze results
- [ ] Step 5: Validate and deliver

Step 1: Frame the decision - Clarify decision context, list alternatives, identify must-haves. See Decision Framing.

Step 2: Identify criteria and assign weights - Determine what factors matter, assign percentage weights. See Criteria Identification and Weighting Techniques.

Step 3: Score alternatives - Rate each option on each criterion (1-10 scale). See Scoring Guidance.

Step 4: Calculate and analyze results - Compute weighted scores, rank options, check sensitivity. See Matrix Calculation and Interpretation.

Step 5: Validate and deliver - Quality check against Quality Checklist, deliver with recommendation.

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Decision Framing

Input Questions

Ask user to clarify:

1. Decision context:

• What are we deciding? (Be specific: "Choose CRM platform" not "improve sales")
• Why now? (Triggering event, deadline, opportunity)
• What happens if we don't decide or choose wrong?

2. Alternatives:

• What are ALL the options we're considering? (Get exhaustive list)
• Include "do nothing" or status quo as an option if relevant
• Are these mutually exclusive or can we combine them?

3. Must-have requirements (filters):

• Are there absolute dealbreakers? (Budget cap, compliance requirement, technical constraint)
• Which options fail must-haves and can be eliminated immediately?
• Distinguish between "must have" (filter) and "nice to have" (criterion)

4. Stakeholders:

• Who needs to agree with this decision?
• Who will be affected by it?
• Do different stakeholders have different priorities?

Framing Template

## Decision Context
- **Decision:** [Specific choice to be made]
- **Timeline:** [When decision needed by]
- **Stakeholders:** [Who needs to agree]
- **Consequences of wrong choice:** [What we risk]

## Alternatives
1. [Option A name]
2. [Option B name]
3. [Option C name]
4. [Option D name - if applicable]
5. [Do nothing / Status quo - if applicable]

## Must-Have Requirements (Pass/Fail)
- [ ] [Requirement 1] - All options must meet this
- [ ] [Requirement 2] - Eliminates options that don't pass
- [ ] [Requirement 3] - Non-negotiable constraint

**Options eliminated:** [List any that fail must-haves]
**Remaining options:** [List that pass filters]

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Criteria Identification

Process

Step 1: Brainstorm factors

Ask: "What makes one option better than another?"

Common categories:

• Cost: Upfront, ongoing, total cost of ownership
• Performance: Speed, quality, reliability, scalability
• Risk: Implementation risk, reversibility, vendor lock-in
• Strategic: Alignment with goals, competitive advantage, future flexibility
• Operational: Ease of use, maintenance, training, support
• Stakeholder: Team preference, customer impact, executive buy-in

Step 2: Validate criteria

Each criterion should be:

• [ ] Measurable or scorable (can assign 1-10 rating)
• [ ] Differentiating (options vary on this dimension)
• [ ] Relevant (actually matters for this decision)
• [ ] Independent (not redundant with other criteria)

Remove:

• Criteria where all options score the same (no differentiation)
• Duplicate criteria that measure same thing
• Criteria that should be must-haves (pass/fail, not scored)

Step 3: Keep list manageable

• Ideal: 4-7 criteria (enough to capture trade-offs, not overwhelming)
• Minimum: 3 criteria (otherwise too simplistic)
• Maximum: 10 criteria (beyond this, hard to weight meaningfully)

If you have >10 criteria, group related ones into categories with sub-criteria.

Criteria Template

## Evaluation Criteria

| # | Criterion | Definition | How We'll Measure |
|---|-----------|------------|-------------------|
| 1 | [Name] | [What this measures] | [Data source or scoring approach] |
| 2 | [Name] | [What this measures] | [Data source or scoring approach] |
| 3 | [Name] | [What this measures] | [Data source or scoring approach] |
| 4 | [Name] | [What this measures] | [Data source or scoring approach] |
| 5 | [Name] | [What this measures] | [Data source or scoring approach] |

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Weighting Techniques

Technique 1: Direct Allocation (Fastest)

Solo decision or aligned stakeholders. Assign percentages summing to 100%. Start with most important (30-50%), avoid weights <5%, round to 5% increments.

Example: Cost 30%, Performance 25%, Ease of use 20%, Risk 15%, Team preference 10% = 100%

Technique 2: Pairwise Comparison (Most Rigorous)

Difficult to weight directly or need justification. Compare each pair ("Is A more important than B?"), tally wins, convert to percentages.

Example: Cost vs Performance → Performance wins. After all pairs, Performance has 4 wins (40%), Cost has 2 wins (20%), etc.

Technique 3: Stakeholder Averaging (Group Decisions)

Multiple stakeholders with different priorities. Each assigns weights independently, then average. Large variance reveals disagreement → discuss before proceeding.

Example: If stakeholders assign Cost weights of 40%, 20%, 30% → Average is 30%, but variance suggests need for alignment discussion.

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Scoring Guidance

Scoring Scale

Use 1-10 scale (better granularity than 1-5):

• 10: Exceptional, best-in-class
• 8-9: Very good, exceeds requirements
• 6-7: Good, meets requirements
• 4-5: Acceptable, meets minimum
• 2-3: Poor, below requirements
• 1: Fails, unacceptable

Consistency tips:

• Define what 10 means for each criterion before scoring
• Score all options on one criterion at a time (easier to compare)
• Use half-points (7.5) if needed for precision

Scoring Process

For objective criteria (cost, speed, measurable metrics):

1. Get actual data (quotes, benchmarks, measurements) 2. Convert to 1-10 scale using formula:

• Lower is better (cost, time): Score = 10 × (Best value / This value)
• Higher is better (performance, capacity): Score = 10 × (This value / Best value)

Example (Cost - lower is better):

• Option A: $50K → Score = 10 × ($30K / $50K) = 6.0
• Option B: $30K → Score = 10 × ($30K / $30K) = 10.0
• Option C: $40K → Score = 10 × ($30K / $40K) = 7.5

For subjective criteria (ease of use, team preference):

1. Define what 10, 7, and 4 look like for this criterion 2. Score relative to those anchors 3. Document reasoning/assumptions

Example (Ease of Use):

• 10 = No training needed, intuitive UI, users productive day 1
• 7 = 1-week training, moderate learning curve
• 4 = Significant training (1 month), complex UI

Calibration questions:

• Would I bet money on this score being accurate?
• Is this score relative to alternatives or absolute?
• What would change this score by ±2 points?

Scoring Template

## Scoring Matrix

| Option | Criterion 1 (Weight%) | Criterion 2 (Weight%) | Criterion 3 (Weight%) | Criterion 4 (Weight%) |
|--------|-----------------------|-----------------------|-----------------------|-----------------------|
| Option A | [Score] | [Score] | [Score] | [Score] |
| Option B | [Score] | [Score] | [Score] | [Score] |
| Option C | [Score] | [Score] | [Score] | [Score] |

**Data sources and assumptions:**
- Criterion 1: [Where scores came from, what assumptions]
- Criterion 2: [Where scores came from, what assumptions]
- Criterion 3: [Where scores came from, what assumptions]
- Criterion 4: [Where scores came from, what assumptions]

---

Matrix Calculation

Calculation Process

For each option: 1. Multiply criterion score by criterion weight 2. Sum all weighted scores 3. This is the option's total score

Formula: Total Score = Σ (Criterion Score × Criterion Weight)

Example:

Winner: Option A (7.7)

Final Matrix Template

## Decision Matrix Results

| Option | [Criterion 1] ([W1]%) | [Criterion 2] ([W2]%) | [Criterion 3] ([W3]%) | [Criterion 4] ([W4]%) | **Weighted Total** | **Rank** |
|--------|----------------------|----------------------|----------------------|----------------------|-------------------|----------|
| [Option A] | [S] ([S×W1]) | [S] ([S×W2]) | [S] ([S×W3]) | [S] ([S×W4]) | **[Total]** | [Rank] |
| [Option B] | [S] ([S×W1]) | [S] ([S×W2]) | [S] ([S×W3]) | [S] ([S×W4]) | **[Total]** | [Rank] |
| [Option C] | [S] ([S×W1]) | [S] ([S×W2]) | [S] ([S×W3]) | [S] ([S×W4]) | **[Total]** | [Rank] |

**Weights:** [Criterion 1] ([W1]%), [Criterion 2] ([W2]%), [Criterion 3] ([W3]%), [Criterion 4] ([W4]%)

**Scoring scale:** 1-10 (10 = best)

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Interpretation

Analysis Checklist

After calculating scores, analyze:

1. Clear winner vs close call

• [ ] Margin >10%: Clear winner, decision is robust
• [ ] Margin 5-10%: Moderate confidence, validate assumptions
• [ ] Margin <5%: Toss-up, need more data or stakeholder discussion

2. Dominant criterion check

• [ ] Does one criterion drive entire decision? (accounts for >50% of score difference)
• [ ] Is that appropriate or is weight too high?

3. Surprising results

• [ ] Does the winner match gut instinct?
• [ ] If not, what does the matrix reveal? (Trade-off you hadn't considered)
• [ ] Or are weights/scores wrong?

4. Sensitivity questions

• [ ] If we swapped top two criterion weights, would winner change?
• [ ] If we adjusted one score by ±1 point, would winner change?
• [ ] Which scores are most uncertain? (Could they change with more data)

Recommendation Template

## Recommendation

**Recommended Option:** [Option name] (Score: [X.X])

**Rationale:**
- [Option] scores highest overall ([X.X] vs [Y.Y] for runner-up)
- Key strengths: [What it excels at based on criterion scores]
- Acceptable trade-offs: [Where it scores lower but weight is low enough]

**Key Trade-offs:**
- **Winner:** Strong on [Criterion A, B] ([X]% of total weight)
- **Runner-up:** Strong on [Criterion C] but weaker on [Criterion A]
- **Decision driver:** [Criterion A] matters most ([X]%), where [Winner] excels

**Confidence Level:**
- [ ] **High (>10% margin):** Decision is robust to reasonable assumption changes
- [ ] **Moderate (5-10% margin):** Sensitive to [specific assumption], recommend validating
- [ ] **Low (<5% margin):** Effectively a tie, consider [additional data needed] or [stakeholder input]

**Sensitivity:**
- [Describe any sensitivity - e.g., "If Risk weight increased from 20% to 35%, Option B would win"]

**Next Steps:**
1. [Immediate action - e.g., "Get final pricing from vendor"]
2. [Validation - e.g., "Confirm technical feasibility with engineering"]
3. [Communication - e.g., "Present to steering committee by [date]"]

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Quality Checklist

Before delivering, verify:

Decision framing:

• [ ] Decision is specific and well-defined
• [ ] All viable alternatives included
• [ ] Must-haves clearly separated from nice-to-haves
• [ ] Stakeholders identified

Criteria:

• [ ] 3-10 criteria (enough to capture trade-offs, not overwhelming)
• [ ] Each criterion is measurable/scorable
• [ ] Criteria differentiate between options (not all scored the same)
• [ ] No redundancy between criteria
• [ ] Weights sum to 100%
• [ ] Weight distribution reflects true priorities

Scoring:

• [ ] Scores use consistent 1-10 scale
• [ ] Objective criteria based on data (not guesses)
• [ ] Subjective criteria have clear definitions/anchors
• [ ] Assumptions and data sources documented
• [ ] Scores are defensible (could explain to stakeholder)

Analysis:

• [ ] Weighted scores calculated correctly
• [ ] Options ranked by total score
• [ ] Sensitivity analyzed (close calls identified)
• [ ] Recommendation includes rationale and trade-offs
• [ ] Next steps identified

Communication:

• [ ] Matrix table is clear and readable
• [ ] Weights shown in column headers
• [ ] Weighted scores shown (not just raw scores)
• [ ] Recommendation stands out visually
• [ ] Assumptions and limitations noted

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Common Pitfalls