Analyze Feature Request
Evaluate this feature request using PM frameworks: $ARGUMENTS
Your Task
Analyze the feature request above to determine its viability, impact, and strategic fit. Use the frameworks below to produce a data-driven recommendation on whether to pursue this opportunity.
Steps
-
Gather Context
- Parse the feature request "$ARGUMENTS" for key details
- Search for related user research in Dovetail
- Check Coda for any existing PRDs or roadmap items
- Review any relevant technical documentation
-
Apply Product Frameworks
- Use RICE scoring (Reach, Impact, Confidence, Effort)
- Apply Kano Model to categorize feature type (Basic/Performance/Delighter)
- Apply Jobs-to-be-Done framework to understand user needs
- Consider product lifecycle stage for context-appropriate prioritization
- Evaluate against current product strategy and OKRs
- Apply 80/20 rule: Is this in the vital 20% that drives value?
- Consider competitive landscape
Reference: See product-frameworks skill for detailed guidance on:
- Prioritization frameworks (RICE, Kano, Pareto)
- Lifecycle-aware prioritization strategies
-
Analyze Impact
- Estimate user reach (how many users affected)
- Assess impact on user value (1-3 scale: minimal, moderate, massive)
- Evaluate business impact (revenue, retention, acquisition)
- Consider strategic alignment with product vision
-
Assess Feasibility
- Technical complexity and dependencies
- Resource requirements (engineering, design, PM time)
- Timeline estimates
- Risk factors and constraints
-
Generate Recommendation
- Present RICE score with breakdown
- Recommend priority level (P0-P3)
- Suggest next steps (build, research more, defer, decline)
- Identify key assumptions to validate
Output Format
Present the analysis in a structured format:
Feature Summary
- Request: [Brief description]
- Requested by: [Source]
- Strategic alignment: [How it fits strategy]
RICE Score Analysis
- Reach: [Number of users/quarter]
- Impact: [0.25-3 scale: 3=Massive, 2=High, 1=Medium, 0.5=Low, 0.25=Minimal]
- Confidence: [Percentage: 100%=High, 80%=Medium, 50%=Low]
- Effort: [Person-months estimate]
- Final RICE Score: [Calculated: (Reach × Impact × Confidence) / Effort]
Kano Model Classification
- Category: [Basic/Performance/Delighter]
- Rationale: [Why this classification]
- Implication: [What this means for prioritization]
Lifecycle Context
- Product stage: [Introduction/Growth/Maturity/Decline]
- Stage-appropriate priority: [How lifecycle affects this feature's priority]
Supporting Evidence
- User research insights from Dovetail
- Competitive analysis
- Usage data or analytics
- Strategic objectives
Recommendation
- Priority: [P0/P1/P2/P3]
- Decision: [Build/Research/Defer/Decline]
- Rationale: [Key reasons for recommendation]
- Next steps: [Actionable items]
- Risks: [Key risks and mitigations]
Examples
Example 1: User-Requested Feature
User: "We've had 10 customers request bulk export functionality"
[Command analyzes]:
- Searches Dovetail for export-related feedback
- Checks Coda for roadmap and PRD status
- Calculates RICE score
- Provides prioritization recommendation
Example 2: Competitive Feature
User: "Competitor X just launched real-time collaboration. Should we build this?"
[Command analyzes]:
- Reviews strategic positioning
- Assesses user demand from research
- Evaluates effort vs. impact
- Recommends whether to pursue
Best Practices
- Ground analysis in user research data, not just opinions
- Be transparent about confidence levels and assumptions
- Consider opportunity cost (what else could we build)
- Include both quantitative and qualitative factors
- Make clear, actionable recommendations
- Document key assumptions for future reference
Related Commands
Framework References
For deeper understanding of the frameworks used: