Opportunity Solution Trees

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):

Desired Outcome (top) — The measurable business or product outcome you're 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. Frame them from the customer's perspective: "I struggle to..." or "I wish I could..." Prioritize using Opportunity Score: Importance × (1 − Satisfaction) (Dan Olsen).
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.
Experiments (bottom) — Fast, cheap tests to validate whether a solution addresses the opportunity. Use assumption testing (Value, Usability, Viability, Feasibility).

Key principles:

One outcome at a time — don't try to solve everything
Opportunities, not features — never let customers design solutions
Compare and contrast — generate at least 3 solutions per opportunity
Discovery is not linear — loop back if experiments fail
Continuous, not periodic — update the tree weekly

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

What It Is

Use the Opportunity Solution Tree (OST) to connect a business outcome to the customer opportunities that drive it, then compare solutions and tests. The tree forces you to separate needs from ideas and keeps discovery tied to delivery.

When to Use It

Structure discovery around customer opportunities
Tie customer needs to measurable outcomes
Compare multiple solutions for the same opportunity
Keep continuous discovery aligned with the roadmap
Create a shared view of priorities with stakeholders

When Not to Use It

You are not doing customer research
The solution is already decided
The work is a commodity requirement with no real options
You only need a quick one-off decision

Core Structure

Outcome: the business result you are responsible for achieving
Opportunities: unmet customer needs, pains, or desires
Solutions: multiple ideas that address one opportunity
Experiments: tests that validate the riskiest assumptions

Process

Follow this step-by-step process to build and use an Opportunity Solution Tree:

Step 1: Define the Desired Outcome

Confirm or help articulate a single, measurable outcome at the top of the tree
Make it specific (e.g., "increase 7-day retention to 40%" not "improve retention")
Tie it to your OKRs or product strategy

Step 2: Map Opportunities

From customer research, identify 3-7 customer opportunities (needs/pains/desires)
Frame each from the customer's perspective ("I struggle to...", "I wish I could...")
Group related opportunities into themes
Avoid solutions disguised as opportunities (e.g., "needs a dashboard" → "struggles to understand performance")

Step 3: Prioritize Opportunities

Use Opportunity Score (Importance × [1 − Satisfaction]) or qualitative assessment
Focus on the top 2-3 opportunities
Consider: impact on outcome, frequency, intensity of pain

Step 4: Generate Solutions

For each prioritized opportunity, brainstorm 3+ solutions
Include perspectives from PM, Designer, and Engineer (Product Trio)
Resist the "first idea" trap — compare and contrast before choosing

Step 5: Design Experiments

For the most promising solutions, suggest 1-2 fast experiments
Specify: hypothesis, method, metric, success threshold
Prefer experiments with "skin in the game" (Alberto Savoia) over opinion-based validation

Step 6: Visualize the Tree

Present the full OST in a clear hierarchical format
Use indentation, bullets, or a visual tool
Make it easy for stakeholders to scan and understand

Step 7: Iterate Weekly

Review the tree every week as you learn from interviews, analytics, experiments
Kill solutions that don't validate
Explore new branches as you discover new opportunities