| name | opportunity-solution-tree |
| description | Structure product discovery with Teresa Torres' Opportunity Solution Tree - outcome, opportunities, solutions, experiments |
| argument-hint | [desired outcome] |
Opportunity Solution Tree (OST)
A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
Domain Context
The Opportunity Solution Tree (Teresa Torres, Continuous Discovery Habits) is the backbone of modern product discovery. It prevents teams from jumping to solutions by forcing them to first map the opportunity space.
Structure (4 levels):
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Desired Outcome (top) - The measurable business or product outcome the team is pursuing. Should be a single, clear metric (e.g., "increase 7-day retention to 40%"). This comes from your OKRs or product strategy.
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Opportunities (second level) - Customer needs, pain points, or desires discovered through research. These are problems worth solving - not features. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance * (1 - Satisfaction) (Dan Olsen, The Lean Product Playbook). Normalize Importance and Satisfaction to 0-1.
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Solutions (third level) - Possible ways to address each opportunity. Generate multiple solutions per opportunity - don't commit to the first idea. The Product Trio (PM + Designer + Engineer) should ideate together. "Best ideas often come from engineers."
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Experiments (bottom) - Fast, cheap tests to validate whether a solution actually addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility risks). Prefer experiments with "skin-in-the-game" (Alberto Savoia) over opinion-based validation.
Key principles:
- One outcome at a time. Don't try to solve everything. Focus the tree on a single desired outcome.
- Opportunities, not features. "Never allow customers to design solutions. Prioritize opportunities (problems), not features."
- Compare and contrast. Always generate at least 3 solutions per opportunity before choosing. Avoid the "first idea" trap.
- Discovery is not linear. Loop back if experiments fail. Kill solutions that don't validate. Explore new branches.
- Continuous, not periodic. Update the tree weekly as you learn from interviews, analytics, and experiments.
Instructions
You are helping a product team build an Opportunity Solution Tree for $ARGUMENTS.
Input Requirements
- A desired outcome or business metric to improve
- Customer research data (interviews, surveys, analytics, feedback)
- Optionally: existing opportunities or solution ideas to organize
Process
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Define the desired outcome - Confirm or help articulate a single, measurable outcome at the top of the tree.
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Map opportunities - From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
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Prioritize opportunities - Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
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Generate solutions - For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
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Design experiments - For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
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Visualize the tree - Present the full OST in a clear hierarchical format.
Think step by step. Save as markdown if substantial.
Further Reading
