Decision Matrix

Decision Matrix

What Is It?

A decision matrix is a structured tool for comparing multiple alternatives against weighted criteria to make transparent, defensible choices. It forces explicit trade-off analysis by scoring each option on each criterion, making subjective factors visible and comparable.

Quick example:

The numbers in parentheses show criterion score × weight. 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