launch-retrospective

name	launch-retrospective
description	Use after a product launch to review results - structured analysis of metrics, what worked, what didn't, and concrete next actions

Launch Retrospective

Overview

Structured review of what happened after launch. Produces honest analysis with concrete next actions, not vanity metrics.

Announce at start: "I'm using the launch-retrospective skill to review the launch."

Process

Step 1: Context gathering

• Read all existing marketing docs to understand the original plan
• Ask the output mode question (minimal vs full)

Step 2: Four areas (one at a time)

Metrics review

• Ask what data is available (don't demand specific tools or dashboards)
• Common metrics: signups, traffic, conversion rates, email open/click rates, social engagement, revenue, press mentions
• Present what was measured vs what was planned to measure
• Flag gaps in measurement

What worked

• Which channels outperformed expectations?
• Which messages resonated most?
• What surprised you?
• Probe for specifics: "social did well" → "which posts? what engagement numbers?"

What didn't work

• Which bets didn't pay off?
• What got skipped from the plan and why?
• What was the gap between plan and execution?
• No blame - honest assessment only

Lessons and next actions

• Each lesson gets a specific, actionable next step
• Not "do better at social" but "short-form video outperformed static posts 3:1 - shift 40% of content budget to video next quarter"
• Prioritize: what has the highest impact if changed?

Step 3: Write output

• Write docs/marketing/launch-retrospective.md
• Full mode adds: metrics summary as JSON, updated persona notes with real learnings, revised channel priority ranking, next-quarter action checklist

Step 4: Handoff

• For v2 or next product: suggest cycling back to globalcoder-marketing:market-research or globalcoder-marketing:positioning-strategy with learnings as input

Key Principles

• Honest over optimistic - a retro that says everything went great is useless
• Specifics over generalities - push for numbers, examples, evidence
• Lessons need actions - every insight must have a "so what do we do about it?"
• Work with available data - don't gatekeep on perfect analytics, use what exists