launch-debrief

name	launch-debrief
description	Structured post-launch retrospective that produces quantified learnings and improvement playbooks. Use when: launch retrospective, post-launch review, what worked, launch debrief, post-mortem, lessons learned.

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
< 0.60	Critical Miss	Red	Major failure, deep investigation required

Top 3 Overperformances:

Rank	Metric	Index	Why It Worked	Replicable?
1				Yes / Partially / No
2
3

Top 3 Underperformances:

Rank	Metric	Index	Initial Hypothesis	Severity
1				Critical / High / Medium
2
3

Step 2: Insights Extraction

Systematically extract learnings across four dimensions.

Win Analysis (What Worked):

#	Category	Finding	Evidence	Impact Level	Replicable?
1	Messaging	Which messages resonated strongest?	Data point	High/Med/Low
2	Channel	Which channels outperformed?	Data point
3	Content	Which assets drove the most engagement?	Data point
4	Timing	Were there timing advantages?	Data point
5	Audience	Which segments responded best?	Data point

Loss Analysis (What Did Not Work):

#	Category	Finding	Evidence	Impact Level	Preventable?
1	Messaging	Which messages fell flat?	Data point	High/Med/Low
2	Channel	Which channels underperformed?	Data point
3	Competitive	Where did competitors win?	Data point
4	Execution	What execution gaps occurred?	Data point
5	Assumptions	Which assumptions were wrong?	Data point

Customer Feedback Synthesis:

Source	Volume	Top Positive Themes	Top Negative Themes	Surprise Insights
NPS Comments
Support Tickets
Social Mentions
Sales Conversations
User Surveys

Internal Feedback Synthesis:

Team	What Went Well	What Was Frustrating	What Would They Change
Product
Marketing
Sales
Support
Engineering

Step 3: Root-Cause Mapping

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

5-Whys Template:

Underperformance	Why 1	Why 2	Why 3	Why 4	Why 5 (Root Cause)
Metric: [name], Index: [value]

Root-Cause Classification:

Root Cause	Error Type	Definition	Example
RC1	Strategy	Wrong approach chosen	Targeted wrong segment
RC2	Execution	Right approach, poor implementation	Campaign launched late, buggy landing page
RC3	Assumption	Incorrect belief about market/customer	Assumed price sensitivity that did not exist
RC4	External	Outside factors beyond control	Competitor launched same week, economic shift
RC5	Timing	Right approach, wrong time	Feature not ready, market not primed

Root-Cause Summary:

#	Underperformance	Root Cause	Error Type	Controllable?	Fix Difficulty
1			Strategy/Execution/Assumption/External/Timing	Yes/Partial/No	Easy/Medium/Hard
2
3

Step 4: Improvement Scoring and Prioritization

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

Improvement Scoring Model:

#	Improvement	Impact (1-10)	Ease (1-10, inverse)	Confidence (1-10)	Priority Score
1
2
3
4
5
6
7
8

Scoring Definitions:

Dimension	Weight	1 (Low)	5 (Medium)	10 (High)
Impact	40%	Marginal improvement, <5% lift	Moderate improvement, 10-20% lift	Transformative, >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)