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:

Performance Index Scale:

Top 3 Overperformances:

Top 3 Underperformances:

Step 2: Insights Extraction

Systematically extract learnings across four dimensions.

Win Analysis (What Worked):

Loss Analysis (What Did Not Work):

Customer Feedback Synthesis:

Internal Feedback Synthesis:

Step 3: Root-Cause Mapping

For each material underperformance (Index < 0.80), perform a structured 5-Whys analysis.

5-Whys Template:

Root-Cause Classification:

Root-Cause Summary:

Step 4: Improvement Scoring and Prioritization

Score each potential improvement on three dimensions to prioritize the next-launch playbook.

Improvement Scoring Model:

Scoring Definitions:

Priority Score Formula:

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

Priority Classification:

Step 5: Build the Next-Launch Playbook

Compile all P0 and P1 improvements into an actionable playbook.

Next-Launch Playbook Template:

Assumptions to Revalidate:

Benchmarks Updated:

Step 6: Launch Comparison (Multi-Launch Trend)

If prior launch debriefs exist, compare trends across launches.

Cross-Launch Comparison:

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)