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Post-Launch Analysis Framework
Analyze launched features to understand performance, user feedback, and identify improvements.
Use Case
Analyzing launched features, understanding performance, or identifying improvement opportunities after launch.
Prompt
Create a post-launch analysis for [feature/product].
Launch Context:
- Feature/product: [name]
- Launch date: [date]
- Time since launch: [duration]
- Launch scope: [describe]
Provide:
1. Launch Summary
- What was launched
- Launch scope and rollout
- Key objectives
- Success criteria
2. Quantitative Analysis
- Usage metrics
- Engagement metrics
- Conversion metrics
- Performance metrics
- Comparison to baseline
- Comparison to targets
3. Qualitative Analysis
- User feedback themes
- Support ticket analysis
- User interview insights
- Sentiment analysis
4. What Worked Well
- Successful aspects
- Positive user feedback
- Met or exceeded goals
- Unexpected wins
5. What Didn't Work
- Issues identified
- Negative feedback
- Missed goals
- Unexpected problems
6. User Behavior Analysis
- How users are using the feature
- Adoption patterns
- Usage frequency
- User segments using it
7. Problem Identification
- Friction points
- Confusion areas
- Technical issues
- UX issues
8. Improvement Opportunities
- Quick wins
- Short-term improvements
- Long-term enhancements
- Prioritized recommendations
9. Business Impact
- Revenue impact (if applicable)
- Cost impact
- User satisfaction impact
- Strategic alignment
10. Next Steps
- Immediate fixes needed
- Iteration plan
- Further research needed
- Follow-up actions
Format as a comprehensive post-launch analysis with actionable recommendations.How to use
- 1Replace [feature/product], [name], [date], [duration], and [describe] with your specific launch details
- 2Add context before the prompt: Describe your launch and goals. Example: "Feature: Checkout flow. Launch date: Jan 1, 2026. Time since launch: 2 weeks. Launch scope: Phased rollout to 50% of users."
- 3If you have analytics data: Paste metrics and user behavior data. Say "Analytics data: [paste data]"
- 4If you have user feedback: Paste feedback, support tickets, or survey responses. Say "User feedback: [paste feedback]"
- 5Paste the modified prompt into your preferred AI tool, like ChatGPT or Claude
- 6Review the post-launch analysis: Check quantitative analysis, qualitative analysis, what worked/didn't work, and improvement opportunities
- 7Prioritize improvements: Focus on quick wins first, then short-term improvements, then long-term enhancements
- 8Ask for specifics: Request "Focus on UX issues" or "Prioritize improvements by user impact"
- 9Export to your tool: Copy the analysis and recommendations to Notion, Confluence, or your product documentation
- 10Use for iteration: Apply the improvement recommendations to plan your next iteration
Pro Tips
- • Include baseline metrics: Mention pre-launch metrics (e.g., "Pre-launch conversion rate: 30%") so AI can compare to post-launch
- • Specify time period: Mention "2 weeks since launch" so AI provides appropriate analysis timeframe
- • Request prioritization: Ask "Prioritize improvements by impact and effort" for actionable recommendations
- • For multiple features: Analyze one feature at a time for focused analysis: "Analyze checkout flow only"
- • Save as template: Reuse the post-launch analysis structure for future feature launches
Tags
post-launchanalysismetricsfeedbackiteration
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