Build an Opportunity Solution Tree, a structured map linking one measurable product outcome to customer needs, possible solutions, and experiments. Use research findings to prioritize problems and define hypotheses, metrics, and success thresholds for testing ideas.
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Build an Opportunity Solution Tree (OST) to structure product discovery — map a desired outcome to opportunities, solutions, and experiments. Based on Teresa Torres' Continuous Discovery Habits. Use when structuring discovery work, mapping opportunities to solutions, or deciding what to build next.
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A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.
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):
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.
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.
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."
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:
You are helping a product team build an Opportunity Solution Tree for $ARGUMENTS.
Define the desired outcome — Confirm or help articulate a single, measurable outcome at the top of the tree.
Map opportunities — From provided research, identify 3-7 customer opportunities (needs/pains). Group related opportunities. Frame each from the customer's perspective.
Prioritize opportunities — Use Opportunity Score or qualitative assessment to rank. Focus on the top 2-3.
Generate solutions — For each prioritized opportunity, brainstorm 3+ solutions from PM, Designer, and Engineer perspectives.
Design experiments — For the most promising solutions, suggest 1-2 fast experiments. Specify: hypothesis, method, metric, success threshold.
Visualize the tree — Present the full OST in a clear hierarchical format.
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Analyze A/B test results to decide whether to launch a change, extend testing, stop, or investigate further. Check sample size, statistical significance, uncertainty ranges, and effects on other important metrics, then summarize the recommendation and next steps.
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Organize customer feature requests into themes and rank the top three against product goals, customer value, effort, and risk. For each recommendation, explain the underlying need, possible alternatives, uncertain assumptions, and simple ways to test them.
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Compare growth options across existing and new products and markets using the Ansoff Matrix, a four-part planning framework. Assess opportunities, resources, competition, and risks, then develop market launch plans and a phased roadmap for priority options.
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Choose the first customer segment for a product launch by comparing urgent needs, willingness to pay, achievable market share, and referral potential. Get scored recommendations, a priority segment, a 90-day acquisition plan, and expansion options.
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Plan low-effort experiments to check assumptions about an existing product before building a feature. Compare prototypes, A/B tests, and other methods, with a clear assumption, procedure, metric, and success threshold for each experiment.
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Turn a new product idea into a testable hypothesis and two or three simple experiments. Define target customers, expected actions, measurement methods, and success thresholds to assess demand through behavior such as sign-ups, pre-orders, or paid commitments.
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