opportunity-solution-tree

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# 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:

| Experiment | Assumption tested | Method | Success metric | Effort |
|------------|------------------|--------|---------------|--------|
| <experiment name> | <what belief this validates> | <A/B test / fake door / prototype / interview> | <metric + threshold> | <1d / 3d / 1w> |

**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`:

```markdown
# 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 |

From avelikiy/great_cto, MIT.

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