Pm Case Study

---
name: pm-case-study
description: "Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned."
argument-hint: "[product launch, feature, or company decision]"
---

# PM Case Study Skill

Generate a detailed PM case study from a real AI product launch, pivot, or strategic decision — reconstructing the PM thinking behind it.

## When to Use
- User asks "Write a case study on [AI product launch/decision]"
- User wants to understand PM decisions behind a real product
- User says `/pm-case-study` followed by a topic
- Great for: ChatGPT launch, Claude's Constitutional AI, Gemini's multimodal strategy, GitHub Copilot pricing, Perplexity's search bet, Midjourney's Discord-first strategy, etc.

## Framework: PM Case Study (8 Sections)

### Section 1: Executive Summary
- **What happened**: One paragraph summary of the product decision/launch
- **When**: Timeline of key events
- **Who**: Key people and teams involved
- **Outcome**: How it played out (success, failure, mixed)

### Section 2: Context & Background
- **Company situation**: Where was the company at this point? Stage, funding, competitive position.
- **Market context**: What was happening in the broader market?
- **Technical context**: What capabilities existed? What was newly possible?
- **User context**: What were users doing before this product? What pain existed?

### Section 3: The Decision
- **What was decided**: Specific product/strategy decision
- **Alternatives considered**: What other paths were likely on the table?
- **Key trade-offs**: What did they give up by choosing this path?
- **Stakeholder dynamics**: Who likely championed this? Who likely opposed it?

### Section 4: Execution Analysis
- **Go-to-market strategy**: How was it launched? Distribution channel?
- **Phasing**: Was it a big bang launch or phased rollout?
- **Pricing**: How was it priced? Why that model?
- **Technical execution**: What was the technical approach? Shortcuts taken?

### Section 5: What Went Right
- Identify 3-5 specific decisions that contributed to success
- For each: What was the decision, why it mattered, what would have happened otherwise
- Be specific — reference actual features, timelines, or metrics where available

### Section 6: What Went Wrong (or Could Have Been Better)
- Identify 2-3 mistakes, misses, or areas for improvement
- For each: What happened, what the impact was, what could have been done differently
- Be fair — hindsight bias is easy, focus on what was knowable at the time

### Section 7: Metrics & Outcomes
- **Growth metrics**: Users, revenue, market share (use real numbers where available)
- **Product metrics**: Engagement, retention, satisfaction
- **Strategic outcomes**: Market position, competitive response, ecosystem effects
- **Unexpected outcomes**: Things that happened that nobody predicted

### Section 8: Key Takeaways
Extract 3-5 lessons for product managers:
- **Lesson**: Clear statement of the principle
- **Application**: How to apply this in product sense/strategy decisions
- **Example question**: A product question where this lesson is directly relevant

## Case Study Categories

### Product Launches
- ChatGPT's launch (Nov 2022) — fastest growing consumer app ever
- Claude's positioning as the "safe" alternative
- Perplexity's answer engine vs. Google Search
- Midjourney's Discord-native strategy
- Cursor's bet on AI-native IDE

### Strategic Pivots
- An AI lab's shift from nonprofit to capped-profit
- A safety lab's pivot from pure research to product company
- A big tech company's emergency response to ChatGPT
- An open-source LLM strategy from a major tech company

### Feature Decisions
- ChatGPT Plugins → GPTs → the pivot to actions/agents
- GitHub Copilot's pricing model ($10/month individual)
- Claude's Artifacts feature
- Gemini's multimodal-first approach
- NotebookLM's audio overview feature

### Pricing & Business Model
- LLM API pricing evolution (the race to the bottom)
- ChatGPT Plus ($20/month) → Team → Enterprise tiers
- The free tier strategy across AI companies
- Usage-based vs. seat-based pricing in AI

## Output Format
Write as a business school case study — structured, analytical, and with clear takeaways. Use real data where available, clearly mark estimates or speculation. Aim for ~2500 words.

## Research-First Workflow (CRITICAL)
This skill requires real data:
1. **Research extensively** — Do 10-15 web searches for: launch details, user growth data, pricing history, company blog posts, founder interviews, analyst reports, and competitor responses.
2. **Cite everything** — Include `[linked source](url)` inline for all factual claims.
3. **Date awareness** — Note what was known at the time of the decision vs. what we know now.
4. **Display** the complete case study.

## What Good Looks Like
- Demonstrates deep knowledge of the AI product landscape
- Shows you can analyze real product decisions with nuance
- Provides concrete examples and data points for product discussions
- Builds pattern recognition across multiple product launches
- Reveals your product judgment when you evaluate decisions