Opportunity Assessment

What is an Opportunity Solution Tree (OST)?

A visual framework (Teresa Torres) that connects a desired outcome to the opportunities (problems/needs) that could drive it, the solutions that could address each opportunity, and the experiments that test each solution.

Structure

1. Outcome (top) — a measurable result the team is accountable for, e.g. "increase activation rate."
2. Opportunities (branch) — problems/needs/pain points discovered through research that connect to the outcome.
3. Solutions (branch) — possible ways to address a specific opportunity.
4. Experiments (leaves) — small tests to validate a solution before full build.

Why It Helps

It prevents teams from jumping straight from outcome to solution, skipping the critical opportunity-mapping step. It also makes trade-offs visible — you can see all the competing opportunities and solutions at once instead of debating ideas in isolation.

Using It in Practice

Continuous discovery teams update their OST weekly as new research comes in, pruning opportunities that turn out to be small and adding newly discovered ones.

Why Size Opportunities?

Not all opportunities are equal — sizing estimates the potential impact before investing in a solution, preventing teams from over-investing in a niche problem.

Sizing Inputs

• Reach — how many users/customers are affected?
• Frequency — how often does this problem occur for them?
• Severity — how painful is it when it happens?
• Revenue/strategic impact — tie to a business metric where possible (retention, conversion, expansion revenue).

A Simple Sizing Formula

Potential Impact ≈ Reach × Frequency × Severity, scored on relative scales (e.g. 1–5) when hard numbers aren't available — the goal is a defensible relative ranking, not false precision.

Data Sources for Sizing

Analytics (how many users hit this flow/error), support ticket volume, survey prevalence data, and sales/CS anecdotes weighted by how often they recur.

Assessing Risk Per Opportunity

Before committing to a solution, assess the four risks (value, usability, feasibility, business viability — Module 1) specifically for the opportunity at hand.

A Simple Risk Framework

For each candidate opportunity/solution pair, ask:

• What's our biggest assumption here, and how confident are we?
• What's the cost if we're wrong — cheap to recover, or expensive?
• What's the fastest, cheapest way to test the riskiest assumption first?

Risk vs. Reward Matrix

Plot opportunities on two axes — potential impact (from sizing) and confidence/risk level — to visually separate "big bet, validate fast" opportunities from "safe, incremental" ones, guiding both prioritization and how much experimentation rigor each deserves.

Avoiding Risk Blindness

Teams under deadline pressure often skip risk assessment for opportunities that "feel obvious." These are often exactly where costly invalidated assumptions hide — familiarity isn't evidence.

Why Prioritization Matters

Teams have far more opportunities than capacity to address them. A clear, shared prioritization method prevents decisions from being driven by whoever argues loudest or has the most seniority.

Common Prioritization Frameworks

• RICE — Reach, Impact, Confidence, Effort: score each and divide to get a relative priority score.
• Value vs. Effort matrix — simple 2x2 plot to spot "quick wins" (high value, low effort).
• Weighted scoring — define your own criteria (strategic fit, user impact, revenue, risk) with weights matched to current business priorities.

Combining Frameworks with Judgment

Frameworks create a structured conversation, not a final automatic answer — use the score as a starting point for debate, informed by sizing and risk assessment, not a substitute for it.

Communicating Priorities

Show the "why" behind prioritization (the OST, the sizing data, the risk level) to stakeholders, not just the final ranked list — this builds trust and reduces re-litigating decisions later.