Go/No-Go Scoring

The Go/No-Go Scoring system synthesizes multiple decision factors into a single composite score with clear action recommendations. This structured approach replaces subjective decision-making with data-driven methodology while preserving room for strategic judgment.

Overview

Every opportunity receives a Decision Score from 0-100, calculated by weighting four key factors:

1. Win Probability (40% default weight): AI-calculated likelihood of success
2. Strategic Fit (30% default weight): Alignment with company priorities
3. Capability Match (20% default weight): Strength of requirement matches
4. Competitive Position (10% default weight): Market standing and differentiators

The composite score maps to one of four decision outcomes, each with specific pursuit recommendations and resource allocation guidance.

The Four Decision Outcomes

1. Strong Go (80-100 points)

Recommendation: Full pursuit with top resources

Characteristics:

• High win probability (typically 65%+)
• Strong strategic alignment
• Excellent capability match
• Favorable competitive position

Typical Action Plan:

• Assign A-team personnel (best proposal writers, technical leads)
• Allocate full budget for proposal development
• Engage executive sponsors for customer relationships
• Invest in competitive intelligence and win strategy
• Consider teaming only for strategic reasons (not capability gaps)
• Plan for robust technical approach and innovative solutions
• Budget for customer engagement (pre-proposal meetings, demos, site visits)

Resource Commitment: 100% of normal proposal budget and timeline

Success Metrics: 60-70% win rate expected on Strong Go opportunities

Example Strong Go Opportunity:

Opportunity: Cloud Infrastructure Modernization ($2.5M, 3-year)
Win Probability: 72% (past performance with customer, strong technology match)
Strategic Fit: 88% (target market expansion, strategic account)
Capability Match: 92% (exceeds on cloud, DevOps, security; meets all others)
Competitive Position: 78% (known differentiator in automation, weak incumbency)

Decision Score: (72 × 0.4) + (88 × 0.3) + (92 × 0.2) + (78 × 0.1)
             = 28.8 + 26.4 + 18.4 + 7.8
             = 81.4 → STRONG GO

Action: Full pursuit, A-team assigned, executive engagement planned

2. Qualified Go (60-79 points)

Recommendation: Conditional pursuit with risk mitigation

Characteristics:

• Moderate win probability (typically 40-65%)
• Good strategic alignment OR strong capability match
• Some gaps requiring mitigation
• Competitive pressure present

Typical Action Plan:

• Assign mixed team (experienced leads, developing staff for growth)
• Allocate 60-80% of full proposal budget
• Identify and mitigate specific risks:
  • Capability gaps: Teaming, subcontracting, or key hires
  • Competitive threats: Differentiation strategy, pricing approach
  • Customer relationship: Proactive engagement to clarify needs
  • Strategic uncertainty: Define success criteria and exit points
• Set clear go/no-go checkpoint mid-proposal
• Plan for efficient proposal process (leverage templates, past proposals)
• Focus on strengths; don't over-invest in weak areas

Resource Commitment: 60-80% of normal proposal budget and timeline

Success Metrics: 35-45% win rate expected on Qualified Go opportunities

Conditional Pursuit Strategies:

Example Qualified Go Opportunity:

Opportunity: Data Analytics Platform ($800K, 18-month)
Win Probability: 52% (moderate past performance, new customer relationship)
Strategic Fit: 75% (good market alignment, moderate revenue impact)
Capability Match: 68% (strong on analytics, gaps in specific ML algorithms)
Competitive Position: 55% (competitor has relevant case study)

Decision Score: (52 × 0.4) + (75 × 0.3) + (68 × 0.2) + (55 × 0.1)
             = 20.8 + 22.5 + 13.6 + 5.5
             = 62.4 → QUALIFIED GO

Action: Pursue with B-team, identify ML teaming partner, competitive pricing strategy
Risk Mitigation: Engage customer to clarify ML requirements, validate our approach
Exit Criteria: No-bid if teaming partner can't commit or customer feedback is negative

3. Qualified No-Go (40-59 points)

Recommendation: Lean toward no-bid; pursue only with strategic override

Characteristics:

• Low-to-moderate win probability (typically 25-40%)
• Limited strategic alignment
• Significant capability gaps or competitive disadvantages
• High opportunity cost (better pursuits available)

Typical Action Plan:

• Default to no-bid decision
• Consider pursuit only if:
  • Strategic override: Executive decision to pursue for relationship or market entry
  • Learning opportunity: New market/technology where loss is acceptable investment
  • Low-cost entry: Minimal proposal effort required (e.g., simplified acquisition)
  • No better alternatives: Capacity available and pipeline is thin
• If pursuing with override:
  • Assign C-team or junior staff for development opportunity
  • Allocate minimal budget (< 40% of normal proposal cost)
  • Use as training exercise; don't over-invest
  • Set low expectations; plan for loss
  • Extract maximum learning value from process

Resource Commitment: < 40% of normal proposal budget (if pursued at all)

Success Metrics: 15-25% win rate on Qualified No-Go pursuits

Strategic Override Justifications:

Example Qualified No-Go Opportunity:

Opportunity: Legacy System Maintenance ($400K, 12-month)
Win Probability: 30% (no past performance with customer, entrenched incumbent)
Strategic Fit: 45% (low strategic value, commodity work, minimal capability development)
Capability Match: 58% (can do the work, but not differentiated)
Competitive Position: 35% (incumbent has 8-year history, strong customer relationships)

Decision Score: (30 × 0.4) + (45 × 0.3) + (58 × 0.2) + (35 × 0.1)
             = 12 + 13.5 + 11.6 + 3.5
             = 40.6 → QUALIFIED NO-GO

Action: NO-BID recommended
Rationale: Low win probability against strong incumbent, limited strategic value, better opportunities in pipeline
Potential Override: Only if customer proactively requests our participation and commits to fair evaluation

4. Strong No-Go (0-39 points)

Recommendation: Clear no-bid decision

Characteristics:

• Very low win probability (typically < 25%)
• Poor strategic alignment
• Critical capability gaps (especially mandatory requirements)
• Severe competitive disadvantages (strong incumbent, disqualifying factors)

Typical Action Plan:

• No-bid decision - do not pursue
• Document reason for decision (learning for future opportunities)
• Optional: Submit capability statement or white paper (no-cost marketing)
• Optional: Attend pre-proposal conference for market intelligence (if minimal cost)
• Track for future re-compete if strategic value is high
• Focus resources on higher-probability opportunities

Resource Commitment: Zero proposal budget; no-bid

Success Metrics: Should not pursue; win rate N/A

Common Strong No-Go Scenarios:

Example Strong No-Go Opportunity:

Opportunity: Advanced Satellite Communications System ($15M, 5-year)
Win Probability: 18% (no satellite experience, no relevant past performance)
Strategic Fit: 35% (interesting market but massive capability gap)
Capability Match: 22% (missing 3 of 5 mandatory requirements)
Competitive Position: 25% (major aerospace primes are incumbents)

Decision Score: (18 × 0.4) + (35 × 0.3) + (22 × 0.2) + (25 × 0.1)
             = 7.2 + 10.5 + 4.4 + 2.5
             = 24.6 → STRONG NO-GO

Action: NO-BID
Rationale: Missing critical mandatory requirements (satellite communications protocols, space-rated hardware experience, security clearances). Aerospace primes have overwhelming competitive advantage. Pursuit would be zero-probability waste of resources.
Alternative: Track for future; consider acquiring satellite communications firm if market entry is strategic priority.

Scoring Formula and Weights

Default Scoring Formula

Decision Score = (Win Probability × 40%) +
                (Strategic Fit × 30%) +
                (Capability Match × 20%) +
                (Competitive Position × 10%)

Each factor is scored 0-100, then weighted and summed to produce the composite Decision Score.

Configurable Weights

Organizations can adjust weights to reflect decision philosophy:

Customizing Decision Thresholds

Organizations can also adjust the score ranges for each outcome:

Default Thresholds:

{
  strongGo: { min: 80, max: 100 },
  qualifiedGo: { min: 60, max: 79 },
  qualifiedNoGo: { min: 40, max: 59 },
  strongNoGo: { min: 0, max: 39 }
}

Conservative Threshold Adjustment (higher bar for pursuit):

{
  strongGo: { min: 85, max: 100 },      // Narrower Strong Go band
  qualifiedGo: { min: 70, max: 84 },    // Higher floor for conditional pursuit
  qualifiedNoGo: { min: 50, max: 69 },  // Expanded no-go zone
  strongNoGo: { min: 0, max: 49 }
}

Aggressive Threshold Adjustment (lower bar for pursuit):

{
  strongGo: { min: 75, max: 100 },      // Broader Strong Go band
  qualifiedGo: { min: 55, max: 74 },    // Lower floor for conditional pursuit
  qualifiedNoGo: { min: 35, max: 54 },  // Narrower no-go zone
  strongNoGo: { min: 0, max: 34 }
}

Decision Ring Visualization

The system presents the Decision Score in a visual Decision Ring that provides at-a-glance understanding:

Ring Components

Outer Ring: Shows the 0-100 score scale with color-coded zones

• Strong Go: Green zone (80-100)
• Qualified Go: Yellow zone (60-79)
• Qualified No-Go: Orange zone (40-59)
• Strong No-Go: Red zone (0-39)

Score Indicator: Needle or marker shows exact composite score position

Center Display:

• Decision Score: Large number (e.g., "73")
• Outcome Label: Text label ("Qualified Go")
• Confidence Interval: ± range based on data quality (e.g., "±8")

Factor Breakdown: Inner segments or side panel showing component scores

• Win Probability: 68% (contributes 27.2 points at 40% weight)
• Strategic Fit: 82% (contributes 24.6 points at 30% weight)
• Capability Match: 75% (contributes 15.0 points at 20% weight)
• Competitive Position: 60% (contributes 6.0 points at 10% weight)
• Total: 72.8 → Qualified Go

Interpreting the Ring

Strong Signal Patterns:

• High confidence Strong Go: Score 85+, all factors green
• Borderline Strong Go: Score 80-82, verify assumptions before full resource commitment
• High Qualified Go: Score 75-79, close to Strong Go; focus on mitigating gaps to push over threshold
• Low Qualified Go: Score 60-65, marginal pursuit; needs clear risk mitigation plan
• High No-Go: Score 55-59, near decision boundary; validate that override isn't appropriate

Mixed Signal Patterns (investigate before finalizing decision):

• High strategic fit, low win probability: Great opportunity, but can we actually win?
• High capability match, low strategic fit: Easy win, but should we care?
• High win probability, low capability match: Something is wrong; validate capability assessment
• High score with low confidence interval: Missing data; gather more information before decision

Factor Scoring Details

Win Probability (0-100%)

Source: AI-calculated based on multiple signals (see Win Probability)

Key Inputs:

• Past performance similarity
• Customer relationship strength
• Capability match quality
• Competitive landscape
• Procurement characteristics

Confidence Level: System provides confidence interval (e.g., 60% ±15%)

Contribution to Decision Score (at 40% weight):

• 80% win probability → 32 points
• 60% win probability → 24 points
• 40% win probability → 16 points
• 20% win probability → 8 points

Strategic Fit (0-100 points)

Source: Weighted scoring against configured strategic priorities (see Strategic Fit)

Calculation:

Strategic Fit = Σ(Priority Score × Priority Weight)

Where each Priority Score is 0-100 assessment of how well opportunity advances that priority

Example:

Revenue Growth (25% weight): Scored 90 → 22.5 points
Market Expansion (20% weight): Scored 80 → 16.0 points
Capability Development (15% weight): Scored 60 → 9.0 points
Strategic Accounts (20% weight): Scored 70 → 14.0 points
Financial Returns (20% weight): Scored 85 → 17.0 points

Strategic Fit Score = 78.5

Contribution to Decision Score (at 30% weight):

• 90 strategic fit → 27 points
• 70 strategic fit → 21 points
• 50 strategic fit → 15 points
• 30 strategic fit → 9 points

Capability Match (0-100 points)

Source: Aggregated from requirement-level capability assessments

Calculation:

Capability Match = (Mandatory Requirements Score × 0.6) + (Rated Criteria Score × 0.4)

Where:
- Mandatory Requirements Score = % of mandatory requirements where capability ≥ "Meets"
- Rated Criteria Score = Weighted average of all rated requirement capability scores

Capability Scores by Match Level:

• Exceeds: 100 points (demonstrable excellence)
• Meets: 75 points (fully capable)
• Partial: 50 points (capable with gaps)
• Cannot Meet: 0 points (missing capability)

Critical Rule: Any mandatory requirement scored "Cannot Meet" reduces Capability Match to < 40, triggering Strong No-Go unless mitigation is documented.

Contribution to Decision Score (at 20% weight):

• 90 capability match → 18 points
• 70 capability match → 14 points
• 50 capability match → 10 points
• 30 capability match → 6 points

Competitive Position (0-100 points)

Source: Manual assessment of market position and competitive factors (see Competitive Intelligence)

Scoring Rubric:

Adjustment Factors:

• +10 points: You have unique differentiator validated by customer
• +5 points: Competitor has notable weakness you can exploit
• -10 points: Competitor is incumbent with strong performance record
• -5 points: Competitor has recent win in this exact domain with this customer

Contribution to Decision Score (at 10% weight):

• 80 competitive position → 8 points
• 60 competitive position → 6 points
• 40 competitive position → 4 points
• 20 competitive position → 2 points

Decision Workflow

Advanced Scoring Scenarios

Handling Borderline Scores

Scores within ±3 points of a threshold require additional scrutiny:

Example: Score of 78 (Qualified Go, but 2 points below Strong Go threshold of 80)

Investigation Questions:

• Which factor is holding back the score? (e.g., win probability is 55%, pulling down composite)
• Can we improve that factor? (e.g., customer engagement to increase win probability)
• Is the opportunity worth pushing into Strong Go? (resource availability, strategic importance)
• What's the confidence interval? (78 ±10 could actually be Strong Go territory)

Decision Options:

• Accept Qualified Go status and pursue with appropriate resource constraints
• Invest to improve weak factor (customer engagement, teaming, competitive intel) and re-score
• Treat as "high Qualified Go" with slightly elevated resources
• If very close to threshold and high confidence, round up and treat as low Strong Go

Portfolio-Level Scoring

Individual opportunity scores should be considered in portfolio context:

Resource Constraint Scenario:

• You have 3 active Strong Go pursuits consuming all proposal resources
• New opportunity scores Qualified Go (72 points)
• Decision: No-bid the Qualified Go to maintain quality on Strong Go pursuits

Pipeline Gap Scenario:

• You have only 1 active pursuit in pipeline
• Opportunity scores Qualified Go (64 points)
• Decision: Pursue as resource is available, even though it's borderline

Strategic Override Scenario:

• Opportunity scores Qualified No-Go (52 points)
• BUT: It's with your #1 strategic account and they've specifically requested your bid
• Decision: Override to Qualified Go, but with clear resource constraints and learning focus

Sensitivity Analysis

For high-value or strategic opportunities, perform sensitivity analysis:

What-If Scenarios:

• If we team with Partner X, capability match improves from 68% to 85%
  • New Decision Score: 72 → 78 (still Qualified Go, but stronger)
• If we engage customer and validate approach, win probability might increase from 55% to 68%
  • New Decision Score: 64 → 69 (remains Qualified Go, but higher confidence)
• If competitor Y decides not to bid, competitive position improves from 50 to 75
  • New Decision Score: 66 → 68 (modest improvement)

Sensitivity Insights:

• Which factor has the most leverage? (Focus mitigation efforts there)
• Can we realistically improve the score enough to change outcome tier?
• Is the improvement worth the investment? (e.g., is customer engagement likely to actually increase win probability?)

Calibration and Continuous Improvement

Quarterly Score Calibration

Every quarter, review decision score accuracy:

Learning from Overrides

Executive overrides provide valuable calibration data:

Track Override Patterns:

Override: Qualified No-Go → Go (Strategic Account Justification)
Original Score: 48 (Win Prob 35%, Strategic Fit 55%, Capability 60%, Competitive 40%)
Override Rationale: "CEO relationship with customer CIO; strategic account development priority"
Outcome: LOSS (competitor scored higher on technical; price was non-competitive)
Learning: Strategic relationship wasn't sufficient to overcome technical gaps; need better capability match for strategic overrides

Override Analysis Questions:

• What % of overrides resulted in wins vs. losses?
• Were override justifications validated by outcomes?
• Do certain executives have better override judgment than others?
• Are there systematic override patterns that should be incorporated into scoring model?

Model Refinement:

• If "strategic account" overrides win > 50%, consider increasing strategic fit weight
• If "CEO relationship" overrides win < 30%, document that relationships alone don't overcome technical/competitive gaps
• If certain override types consistently win, codify them into automated scoring

Integration with Approval Workflows

Decision scores can trigger approval workflows:

Approval Authority by Score and Value

Automated Workflow Triggers

Best Practices

Maintain Scoring Objectivity

Do:

• Score each factor independently based on evidence
• Document assumptions and data sources for each score
• Use consistent rubrics and definitions across opportunities
• Review scores with cross-functional team for bias checks
• Calibrate scores quarterly based on outcome data

Don't:

• Inflate scores to justify pursuing an opportunity you "like"
• Deflate scores to justify no-bidding an opportunity you want to avoid
• Let resource availability influence scoring (address in decision, not score)
• Score based on "gut feel" without documenting rationale
• Cherry-pick data to support desired score

Set Clear Decision Cadence

Establish standard timelines for each decision phase:

Balance Model and Judgment

The scoring model is a tool, not a mandate:

Trust the Model When:

• Strong Go or Strong No-Go (clear signals)
• Data quality is high (all factors well-validated)
• Historical calibration is strong (model has proven accuracy)
• Decision is consistent with past similar opportunities

Apply Judgment When:

• Borderline scores (within ±5 points of threshold)
• Missing or low-quality data (wide confidence intervals)
• Novel opportunity types (model has limited training data)
• Strategic factors the model doesn't capture

Document Judgment:

• When overriding model recommendation, document rationale
• Track override outcomes to validate judgment accuracy
• Review overrides quarterly to identify patterns for model refinement

FAQ

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Related Documentation:

• Bid Decision Framework
• Win Probability
• Strategic Fit
• Competitive Intelligence
• Recording Outcomes
• Performance Dashboard