Opportunity Solution Tree (OST)

What It Is

The Opportunity Solution Tree is a visual framework that connects a single product outcome to the customer opportunities that could drive it, the solutions that could address each opportunity, and the experiments that test each solution's riskiest assumptions. It was introduced by Teresa Torres in Continuous Discovery Habits as the backbone of a structured, ongoing discovery practice.

When to Use It

• When a product team has a clear outcome metric but is unsure which customer problem to solve next.
• When you need to prevent "solution-first" thinking by forcing opportunity exploration before ideation.
• During weekly discovery cadences to keep the team's option space visible and evolving.
• When stakeholders request a feature and you need to trace it back to a real customer need.

How It Works

1. Set the Outcome — Choose one measurable product outcome the team is responsible for (e.g., increase trial-to-paid conversion by 5%).
2. Map Opportunities — Through customer interviews and data, identify the needs, pain points, and desires that, if addressed, would move the outcome. Organize them in a hierarchy (broad themes break into specific sub-opportunities).
3. Select a Target Opportunity — Assess and prioritize opportunities based on frequency, severity, and strategic fit. Pick one to focus on.
4. Generate Solutions — Ideate at least three distinct solutions for the target opportunity. Resist converging on the first idea.
5. Identify Assumptions — For each solution, list the assumptions that must be true for it to work (desirability, viability, feasibility, usability).
6. Test Assumptions — Design small, fast experiments to test the riskiest assumptions before committing engineering effort.

Key Principles

• One outcome at a time. Each tree has exactly one outcome at the root. Multiple outcomes require separate trees.
• Opportunities are customer needs, not your ideas. They come from research, not brainstorming.
• Compare across the tree, not just down it. Always keep multiple opportunities and multiple solutions visible so you can pivot without starting over.
• Small tests, fast learning. Assumption tests should take days, not sprints. Use prototypes, data pulls, or one-question surveys.
• The tree is alive. Update it weekly as you learn. Add, prune, and re-prioritize continuously.

Common Mistakes

• Jumping from outcome straight to solutions. Skipping the opportunity layer means you are guessing at the problem. The middle layer is the most important part of the tree.
• Only one solution per opportunity. If you only generate one idea, you have nothing to compare against and no fallback. Always generate at least three.
• Treating the tree as a one-time artifact. An OST drawn once and never updated is just a project plan with extra shapes. Its value comes from weekly revision.

Source

Teresa Torres, Continuous Discovery Habits (2021), Chapters 4-7. The OST is introduced in Chapter 4 and revisited throughout the book as the integrating structure for all discovery activities.