Opportunity solution tree

Opportunity Solution Tree

The connective tissue between a business outcome and the experiments that move it.

What this skill is

A workflow for building an Opportunity Solution Tree (OST) - the discovery framework popularized by Teresa Torres that anchors discovery work to a clear outcome, surfaces opportunities (unmet customer needs, pain points, desires), generates multiple solution candidates, and identifies the assumption tests that need to run before committing. Produces a living artifact a product trio can update weekly.

What it solves

• Discovery work that wanders without a measurable outcome
• Premature commitment to one solution without exploring alternatives
• Building features before validating the underlying opportunity
• Confusion about which assumption to test next
• Roadmaps that conflate outputs (features shipped) with outcomes (customer behavior changed)

When to invoke

• Starting a new discovery cycle for a product area
• Restructuring a quarterly plan that's grown feature-heavy
• Deciding what to research next when multiple opportunities compete
• Onboarding a new product trio to a shared discovery model
• Reviewing why a shipped feature didn't move the outcome

Phase 1: Define the outcome at the top

The root of every tree is a single, measurable outcome - a customer behavior the business cares about.

Outcome statement format:

Increase [metric] from [baseline] to [target] by [date].

Rules for a good outcome:

• It is a behavior, not a feature ("more activations" not "ship onboarding v2")
• It is measurable today (you can see the baseline)
• It has a target - vague aspirations hide trade-offs
• It is owned by the product trio for the cycle

If the outcome is a business Key Performance Indicator (KPI) such as revenue or retention, restate it as the leading customer behavior that drives it.

Phase 2: Map opportunities

Opportunities are customer needs, pain points, and desires - never solutions. Source them from:

• Customer interviews (continuous discovery weekly cadence)
• Support tickets, Net Promoter Score (NPS) verbatims
• Sales call recordings
• Behavioral analytics (funnel drop-offs)
• Internal observation (customer success escalations, churn calls)

For each opportunity, write:

• A short title in the customer's voice
• Source citations (which interview, which ticket)
• The job stage it sits in (using the job map from the Jobs-to-be-Done (JTBD) skill)

Structure the opportunities into a tree:

• Parent opportunities are broad (e.g., "It's hard to know if I'm making progress")
• Child opportunities are specific instances (e.g., "I lose track of where I left off when I switch devices")

A tree with only parents is too abstract to act on. A tree with only children misses the synthesis.

Phase 3: Prioritize opportunities

Score each opportunity on:

• Reach - how many target customers experience this?
• Severity - how painful when it happens?
• Strategic alignment - does solving it support the outcome at the top?
• Confidence - how sure are we, based on evidence?

Use a 2-by-2 matrix of opportunity size × strategic alignment. Pick one opportunity per cycle for the trio to focus on. Multiple parallel opportunities dilute discovery.

Phase 4: Generate solution candidates

For the chosen opportunity, brainstorm at least 3-5 solution candidates. The first solution is almost never the best.

Solution-generation prompts:

• Magic wand: if there were no constraints, what would solve this?
• Borrow from another industry: how does X industry solve a similar pain?
• Inverse: what's the smallest change that could help?
• Service-not-software: what if a human did it?
• Existing-flow: what change to a current flow would do it?

For each candidate, write a 1-paragraph description and a sketch (Loom video, Figma frame, or rough wireframe).

Phase 5: Identify assumption tests

For each solution candidate, list the assumptions that must be true for it to work. Use the four risk categories described by Marty Cagan:

Risk	Question
Value	Will customers use it?
Usability	Can they figure out how to use it?
Feasibility	Can we build it with available technology and time?
Business viability	Does it work for legal, sales, support, and finance?

For each assumption, design the cheapest possible test:

• Survey, prototype test, fake-door experiment, concierge Minimum Viable Product (MVP), A/B test, technical spike
• Define the success and kill criteria before running the test

Run the test. Update the tree weekly.

Phase 6: Tree maintenance

The OST is a living artifact, not a one-time deliverable:

• Weekly trio meeting reviews the tree
• New customer signals add or refine opportunities
• Failed assumption tests prune solution branches
• Successful tests advance solutions toward delivery
• Outcome at the top is revisited each cycle

Output

• One outcome at the root of the tree
• 5-15 opportunities organized parent → child
• One prioritized opportunity for this cycle with reasoning
• 3-5 solution candidates per chosen opportunity
• Assumption-test plan for each candidate with success criteria
• Living artifact (Mural, Miro, FigJam) with edit history

Operating rules

Always

• Anchor the tree on a measurable customer outcome
• Write opportunities in the customer's voice
• Generate at least 3 solution candidates before committing
• Define kill criteria before running an assumption test
• Update the tree weekly with the trio

Never

• Put solutions in the opportunity layer
• Pursue more than one opportunity per trio per cycle
• Ship without testing the highest-risk assumption
• Treat the tree as a static deliverable
• Confuse a feature output with a behavior outcome