Launch Debrief

Launch Debrief (MIRROR Protocol)

A structured post-launch retrospective engine that transforms raw launch data into quantified learnings, root-cause analyses, and improvement playbooks. MIRROR ensures every launch makes future launches better by extracting actionable insights from both successes and failures through systematic analysis rather than anecdotal recall.

When to Use

• Conducting a post-launch retrospective (ideally at T+30 and T+90)
• Analyzing why a launch over- or underperformed expectations
• Building an institutional knowledge base of launch learnings
• Creating improvement playbooks for the next launch cycle
• Presenting launch results to leadership with root-cause analysis
• Comparing actual results against pre-launch projections
• Identifying systemic issues across multiple launches

What You'll Need

Critical inputs (ask if not provided):

• Launch name, date, and type (GA, beta, feature, expansion)
• Pre-launch targets for all VITAL metrics (from launch-pulse)
• Actual performance data for all tracked metrics
• Launch readiness scores from gate reviews (from launch-command)
• Budget allocation and actual spend (from budget-allocator)
• Channel performance data by channel (from demand-engine)

Nice-to-have:

• Customer feedback (NPS, surveys, support tickets, social mentions)
• Internal team feedback (retro notes, Slack threads, post-mortems)
• Competitive activity during launch window (from battle-scanner)
• Sales feedback on messaging and enablement effectiveness
• Win/loss analysis data from CRM
• Previous launch debrief reports for trend analysis

Process

Step 1: Metrics Review -- Actual vs Target vs Baseline

For each VITAL metric, calculate the Performance Index and classify the result.

Performance Index Table:

| VITAL Layer | Metric | Baseline | Target | Actual | Perf. Index | Classification | |-------------|--------|----------|--------|--------|-------------|---------------| | Volume | Website Traffic | | | | Actual/Target | | | Volume | Impressions | | | | | | | Volume | Social Reach | | | | | | | Intent | MQLs | | | | | | | Intent | Demo Requests | | | | | | | Intent | Trial Signups | | | | | | | Traction | SQLs | | | | | | | Traction | Pipeline Created | | | | | | | Traction | Win Rate | | | | | | | Adoption | Activation Rate | | | | | | | Adoption | Time to Value | | | | | | | Adoption | DAU/WAU | | | | | | | Loyalty | NPS | | | | | | | Loyalty | 30-Day Retention | | | | | | | Loyalty | Referral Rate | | | | | |

Performance Index Scale:

| Index | Classification | Color | Meaning | |-------|---------------|-------|---------| | >= 1.20 | Significant Overperformance | Blue | Exceeded target by 20%+, investigate why | | 1.00 - 1.19 | On Target | Green | Met or exceeded target | | 0.80 - 0.99 | Slight Underperformance | Yellow | Close to target, minor optimization needed | | 0.60 - 0.79 | Material Underperformance | Orange | Significant gap, root-cause analysis required | | 30% lift | | Ease (inverse) | 30% | Requires org change, 6+ months | Cross-team effort, 1-3 months | Single team, <1 month | | Confidence | 30% | Hypothesis only, no data | Some supporting data | Strong evidence, proven elsewhere |

Priority Score Formula:

Priority = (Impact x 0.4) + (Ease x 0.3) + (Confidence x 0.3)

Priority Classification:

| Score Range | Priority | Action | |------------|---------|--------| | 8.0 - 10.0 | P0: Implement immediately | Must-do for next launch, assign owner this week | | 6.0 - 7.9 | P1: Implement next cycle | Plan for next launch, assign owner within 2 weeks | | 4.0 - 5.9 | P2: Backlog | Good ideas, queue for future improvement | | < 4.0 | P3: Monitor | Low confidence or low impact, revisit if new data |

Step 5: Build the Next-Launch Playbook

Compile all P0 and P1 improvements into an actionable playbook.

Next-Launch Playbook Template:

| # | Improvement | Priority | Owner | Deadline | Dependencies | Success Metric | Status | |---|------------|---------|-------|----------|-------------|---------------|--------| | 1 | | P0 | | | | | Not Started | | 2 | | P0 | | | | | | | 3 | | P1 | | | | | | | 4 | | P1 | | | | | | | 5 | | P1 | | | | | |

Assumptions to Revalidate:

| # | Assumption from This Launch | Was It Valid? | Updated Assumption | Validation Method | |---|---------------------------|-------------|-------------------|------------------| | 1 | | Yes/No/Partial | | | | 2 | | | | | | 3 | | | | |

Benchmarks Updated:

| Metric | Previous Benchmark | Actual This Launch | New Benchmark | Notes | |--------|-------------------|-------------------|--------------|-------| | | | | | | | | | | | |

Step 6: Launch Comparison (Multi-Launch Trend)

If prior launch debriefs exist, compare trends across launches.

Cross-Launch Comparison:

| Dimension | Launch N-2 | Launch N-1 | This Launch | Trend | Notes | |-----------|-----------|-----------|------------|-------|-------| | Overall LRI at gate G4 | | | | | | | Pipeline created (T+30) | | | | | | | Activation rate (T+30) | | | | | | | NPS (T+30) | | | | | | | Budget efficiency (ROI) | | | | | | | Debrief improvement adoption | | | | | |

Output

Save to outputs/launch-debrief/

Deliverables:

1. Launch Scorecard -- Performance Index for every VITAL metric with actual vs target vs baseline, top 3 over/underperformances, and overall launch grade (A through F based on weighted Performance Index)
2. Insights Report -- Win analysis, loss analysis, customer feedback synthesis, and internal feedback synthesis with evidence-backed findings across messaging, channels, content, timing, and audience
3. Root-Cause Analysis -- 5-Whys analysis for each material underperformance, classified by error type (Strategy/Execution/Assumption/External/Timing), with controllability and fix-difficulty assessments
4. Next-Launch Playbook -- Prioritized improvement list (P0 through P3) using the Impact x Ease x Confidence scoring model, with owners, deadlines, dependencies, and updated benchmarks

Chain Connections

• Receives from: launch-pulse (actual metrics data), launch-command (gate scores, launch plan), budget-allocator (spend actuals), demand-engine (channel performance), battle-scanner (competitive context)
• Feeds back into: All future launch cycles -- updated benchmarks flow to launch-pulse, process improvements flow to launch-command, messaging learnings flow to position-lock, channel learnings flow to demand-engine
• Enhanced by: growth-loop (post-launch retention data), signal-radar (market context during launch window)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: varunk130
• Source: varunk130/ai-gtm-skill-library
• License: MIT

Install and usage instructions live in the source repository linked above.