Opportunity Solution Tree (OST)

Structures product discovery by connecting a desired outcome → customer opportunities → solutions → experiments. Prevents jumping to solutions before validating the problem space.

Based on Teresa Torres, Continuous Discovery Habits (2021).

The 4-level structure

                    ┌─────────────────────┐
                    │   DESIRED OUTCOME   │  ← single measurable metric
                    └──────────┬──────────┘
               ┌───────────────┼────────────────┐
        ┌──────┴─────┐  ┌──────┴─────┐  ┌──────┴─────┐
        │Opportunity │  │Opportunity │  │Opportunity │  ← customer pain/need
        │     A      │  │     B      │  │     C      │
        └──────┬─────┘  └──────┬─────┘  └────────────┘
        ┌──────┴───┐    ┌──────┴───┐
    ┌───┴──┐ ┌───┴──┐ ┌───┴──┐ ┌───┴──┐
    │Sol 1 │ │Sol 2 │ │Sol 3 │ │Sol 4 │  ← possible solutions
    └───┬──┘ └──────┘ └───┬──┘ └──────┘
  ┌────┴────┐         ┌───┴────┐
  │ Exp 1   │         │ Exp 2  │          ← fast experiments
  └─────────┘         └────────┘

Key principles:

• One desired outcome at a time — don't try to solve everything
• Opportunities are customer problems/needs, never solutions
• Generate ≥3 solutions per opportunity before choosing one
• Experiments are the cheapest way to validate an assumption
• The tree is a living document — update weekly as you learn

How to build an OST

Step 1 — Define the desired outcome

Confirm or help the user articulate one measurable outcome at the top of the tree.

Good outcomes:

• "Increase 7-day retention from 20% to 35%"
• "Reduce time-to-first-value from 3 days to 1 day"
• "Increase conversion from free to paid from 2% to 5%"

Bad outcomes (reject these):

• "Build a better onboarding" — that's a solution
• "Improve the product" — unmeasurable
• "Launch feature X" — that's an output

If the user can't state a metric: ask "What would need to be true for you to consider this effort a success?"

Step 2 — Map opportunities from research

From customer interviews, analytics, support tickets, or NPS feedback, identify 3–7 customer opportunities (pain points, unmet needs, desires).

Frame each from the customer's perspective:

• ✅ "I struggle to understand which plan is right for me"
• ✅ "I can't find past purchases quickly"
• ✅ "I feel anxious about whether my data is safe"
• ❌ "Users need a better search" — that's a solution

Prioritise using Opportunity Score (Dan Olsen, The Lean Product Playbook):

Opportunity Score = Importance × (1 − Satisfaction)

Survey customers: rate each need on Importance (0–1) and current Satisfaction (0–1).

• High Importance + Low Satisfaction = highest score = best opportunity
• Plot on Importance vs Satisfaction chart — upper-left quadrant is the sweet spot

Step 3 — Generate solutions (diverge before converging)

For each top-priority opportunity, brainstorm ≥3 solutions from three angles:

• PM perspective: What UX/product change addresses this?
• Designer perspective: What interaction or visual change?
• Engineer perspective: What technical approach? (often the most creative)

Rules:

• Don't commit to the first idea — compare and contrast
• "Best ideas often come from engineers" — include technical solutions
• Solutions should be independent (different solutions for the same opportunity)

Step 4 — Design experiments

For the most promising solutions, design 1–2 fast experiments:

Assumption categories (prioritise in this order):

1. Value: Will users want this? (most important to test first)
2. Usability: Can users figure it out?
3. Feasibility: Can we build it?
4. Viability: Does the business case work?

Cheap experiment types:

• Existing product: A/B test, fake door, prototype, user interview, data analysis
• New product: XYZ hypothesis ("At least X% of Y will do Z"), landing page, concierge MVP

Step 5 — Visualise and document

Write docs/discovery/OST-<outcome-slug>.md:

# Opportunity Solution Tree: <Outcome>

**Desired outcome**: <metric> from <current> to <target> by <date>
**Last updated**: <date>

## Opportunity map

| # | Opportunity | Importance | Satisfaction | Opportunity Score | Priority |
|---|------------|-----------|-------------|-------------------|---------|
| A | <customer need> | 0.8 | 0.3 | 0.56 | 1st |
| B | <customer need> | 0.7 | 0.6 | 0.28 | 3rd |
| C | <customer need> | 0.6 | 0.2 | 0.48 | 2nd |

## Solutions for top opportunities

### Opportunity A: <name>
| Solution | Description | Experiment |
|---------|-------------|-----------|
| Sol A1 | <description> | <experiment> |
| Sol A2 | <description> | <experiment> |
| Sol A3 | <description> | <experiment> |

## Active experiments

| Experiment | Assumption | Status | Result |
|-----------|-----------|--------|--------|
| <name> | <assumption> | Running / Done | <result or pending> |

## Learning log

- <date>: Discovered <insight> from <source>. Killed <solution> / promoted <opportunity>.

Integration with /spec prd

Once an opportunity is validated and a solution is chosen: → Run /spec prd with the validated opportunity as the problem statement → The OST's Opportunity Score data feeds directly into PRD §3 (Success Metrics) and §4 (Target Users)

Anti-patterns

❌ Opportunity = solution in disguise: "Users need a search bar" is a solution. "Users can't find past purchases" is an opportunity.

❌ Skipping divergence: Picking the first solution for each opportunity. Always generate ≥3 before choosing.

❌ Experiments that take >1 week: If it takes longer than a week to learn, it's not an experiment — it's a feature.

❌ Updating the tree once: OST is a continuous practice. Update weekly as you learn.

❌ Too many outcomes: One outcome per tree. If you have multiple outcomes, run multiple trees or pick the highest priority.