13 KiB
13 KiB
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.
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]
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] |
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.
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):
- Get actual data (quotes, benchmarks, measurements)
- 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):
- Define what 10, 7, and 4 look like for this criterion
- Score relative to those anchors
- 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:
- Multiply criterion score by criterion weight
- Sum all weighted scores
- This is the option's total score
Formula: Total Score = Σ (Criterion Score × Criterion Weight)
Example:
| Option | Cost (30%) | Performance (40%) | Risk (20%) | Ease (10%) | Total |
|---|---|---|---|---|---|
| Option A | 7 × 0.30 = 2.1 | 9 × 0.40 = 3.6 | 6 × 0.20 = 1.2 | 8 × 0.10 = 0.8 | 7.7 |
| Option B | 9 × 0.30 = 2.7 | 6 × 0.40 = 2.4 | 8 × 0.20 = 1.6 | 6 × 0.10 = 0.6 | 7.3 |
| Option C | 5 × 0.30 = 1.5 | 8 × 0.40 = 3.2 | 7 × 0.20 = 1.4 | 9 × 0.10 = 0.9 | 7.0 |
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)
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]"]
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
Common Pitfalls
| Pitfall | Fix |
|---|---|
| Too many criteria (>10) | Consolidate related criteria into categories |
| Redundant criteria | Combine criteria that always score the same |
| Arbitrary weights | Use pairwise comparison or stakeholder discussion |
| Scores are guesses | Gather data for objective criteria, define anchors for subjective |
| Confirmation bias | Weight criteria BEFORE scoring options |
| Ignoring sensitivity | Always check if small changes flip the result |
| False precision | Match precision to confidence level |
| Missing "do nothing" | Include status quo as an option to evaluate |