---
title: "Opportunity Solution Tree"
description: "Creates an opportunity solution tree mapping desired outcomes to opportunities and potential solutions. Use for outcome-driven product discovery, prioritization, or communicating product strategy."
generated: true
source: scripts/generate-skill-pages.py
tags:
  - Define
  - problem-framing
---

:::note[Quick facts]
**Phase:** Define | **Version:** 2.0.0 | **Category:** problem-framing | **License:** Apache-2.0
:::

**Try it:** `/opportunity-tree "Your context here"`

An Opportunity Solution Tree (OST) is a visual framework for product discovery that connects business outcomes to customer opportunities and potential solutions. Developed by Teresa Torres, it prevents the common trap of jumping straight to solutions by ensuring every feature idea traces back to a customer need and measurable outcome.

## When to Use

- During continuous product discovery to organize learning
- When prioritizing what opportunities to pursue
- To communicate product strategy to stakeholders
- When you have too many feature ideas and need structure
- After user research to connect insights to action
- When aligning team on what outcomes matter most

## How to Use

Use the `/opportunity-tree` slash command:

```
/opportunity-tree "Your context here"
```

Or reference the skill file directly: `skills/define-opportunity-tree/SKILL.md`

## Instructions

When asked to create an opportunity solution tree, follow these steps:

1. **Define the Desired Outcome**
   Start at the top with a clear, measurable business or product outcome. This should be something you can influence through product changes. Express it quantitatively when possible (e.g., "Increase 30-day retention from 40% to 55%").

2. **Identify Opportunity Areas**
   Branch out to 3-5 opportunity areas.places where customer needs or pain points could be addressed. Opportunities are not solutions; they're customer problems, needs, or desires. Phrase them from the customer's perspective.

3. **Add Supporting Evidence**
   For each opportunity, note the evidence that supports it: user research quotes, behavioral data, support tickets, or market trends. Strong opportunities have multiple evidence sources.

4. **Brainstorm Solutions**
   For each opportunity, generate 2-4 potential solutions. Don't self-censor at this stage. Solutions can range from quick experiments to major features. Keep them specific enough to evaluate.

5. **Define Assumption Tests**
   For each promising solution, identify the riskiest assumption and design a lightweight experiment to test it. Good tests validate whether the solution will actually address the opportunity.

6. **Prioritize the Tree**
   Not all branches are equal. Mark which opportunity and solution you'll pursue first based on potential impact, confidence, and effort. The tree is a living document.you'll iterate as you learn.

7. **Visualize the Structure**
   Create a tree diagram showing the hierarchy: outcome at top, opportunities below, solutions beneath each opportunity, and experiments at the leaves.

## Output Template

# Opportunity Solution Tree: [Outcome Name]

## Desired Outcome

**Outcome Statement:** [Clear, measurable outcome]
**Current State:** [Baseline metric if available]
**Target State:** [Goal metric]
**Timeframe:** [When we want to achieve this]
**Owner:** [Who owns this outcome]

### Why This Outcome Matters
<!-- Connect to business strategy and user value -->

[Explanation of strategic importance]

---

## Visual Tree

```
                    [OUTCOME]
                    [Statement]
                        |
        ┌───────────────┼───────────────┐
        |               |               |
   [Opportunity 1] [Opportunity 2] [Opportunity 3]
        |               |               |
    ┌───┴───┐       ┌───┴───┐       ┌───┴───┐
    |       |       |       |       |       |
[Sol A] [Sol B] [Sol C] [Sol D] [Sol E] [Sol F]
    |       |       |       |       |       |
[Test]  [Test]  [Test]  [Test]  [Test]  [Test]
```

---

## Opportunity Branches

### Opportunity 1: [Customer need or problem]

**Description:** [What is the opportunity from customer's perspective]
**Impact Potential:** High/Medium/Low
**Confidence:** High/Medium/Low

**Evidence:**
- [Source 1]: "[Quote or data point]"
- [Source 2]: "[Quote or data point]"
- [Source 3]: "[Quote or data point]"

#### Solutions

**Solution 1A: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

**Solution 1B: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

---

### Opportunity 2: [Customer need or problem]

**Description:** [What is the opportunity from customer's perspective]
**Impact Potential:** High/Medium/Low
**Confidence:** High/Medium/Low

**Evidence:**
- [Source 1]: "[Quote or data point]"
- [Source 2]: "[Quote or data point]"
- [Source 3]: "[Quote or data point]"

#### Solutions

**Solution 2A: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

**Solution 2B: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

---

### Opportunity 3: [Customer need or problem]

**Description:** [What is the opportunity from customer's perspective]
**Impact Potential:** High/Medium/Low
**Confidence:** High/Medium/Low

**Evidence:**
- [Source 1]: "[Quote or data point]"
- [Source 2]: "[Quote or data point]"
- [Source 3]: "[Quote or data point]"

#### Solutions

**Solution 3A: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

**Solution 3B: [Solution name]**
- Description: [What we would build]
- Effort: [T-shirt size: S/M/L/XL]
- Riskiest Assumption: [What must be true]
- Assumption Test: [How to validate quickly]

---

## Prioritization

### Current Focus

**Priority Opportunity:** [Which opportunity we're pursuing first]
**Priority Solution:** [Which solution we're testing first]
**Rationale:** [Why this path over others]

### Opportunity Ranking

| Rank | Opportunity | Impact | Confidence | Effort | Score |
|------|-------------|--------|------------|--------|-------|
| 1 | [Name] | H/M/L | H/M/L | H/M/L | [Calculated] |
| 2 | [Name] | H/M/L | H/M/L | H/M/L | [Calculated] |
| 3 | [Name] | H/M/L | H/M/L | H/M/L | [Calculated] |

### Parking Lot
<!-- Good ideas not pursuing now -->

- [Idea]: [Why not now]
- [Idea]: [Why not now]

---

## Experiments Backlog

| Solution | Assumption | Test Method | Success Criteria | Status |
|----------|------------|-------------|------------------|--------|
| [Name] | [Assumption] | [Method] | [Criteria] | Planned/Running/Complete |
| [Name] | [Assumption] | [Method] | [Criteria] | Planned/Running/Complete |
| [Name] | [Assumption] | [Method] | [Criteria] | Planned/Running/Complete |

---

## Learning Log

<!-- Update as you run experiments -->

| Date | Experiment | Result | Learning | Impact on Tree |
|------|------------|--------|----------|----------------|
| [Date] | [What we tested] | [Pass/Fail] | [What we learned] | [How tree changed] |

---

## Next Steps

- [ ] [Immediate action 1]
- [ ] [Immediate action 2]
- [ ] [Immediate action 3]

---

*This is a living document. Update as you learn from experiments and customer feedback.*

## Example Output

<details>
<summary>Opportunity Solution Tree: Improve 30-Day User Retention</summary>

# Opportunity Solution Tree: Improve 30-Day User Retention

## Desired Outcome

**Outcome Statement:** Increase 30-day user retention rate
**Current State:** 40% of new users return after 30 days
**Target State:** 55% of new users return after 30 days
**Timeframe:** Q2 2026
**Owner:** Maya Johnson, Product Manager

### Why This Outcome Matters

User retention is our primary growth lever. Our acquisition costs are high ($45 CAC), so improving retention directly impacts unit economics. Users who make it past 30 days have 85% likelihood of converting to paid, making early retention critical to revenue growth. Leadership has identified this as our #1 product priority for the quarter.

---

## Visual Tree

```
                        [OUTCOME]
            Increase 30-day retention 40% → 55%
                           |
        ┌──────────────────┼──────────────────┐
        |                  |                  |
   [Opportunity 1]    [Opportunity 2]    [Opportunity 3]
   Users don't        Users can't find   Users forget
   understand value   features they      to come back
   in first session   need               |
        |                  |         ┌────┴────┐
    ┌───┴───┐          ┌───┴───┐     |         |
    |       |          |       |  [Email]  [Mobile
[Wizard] [Video]   [Search] [AI        Push]
    |       |          |    Assist]
[Test:   [Test:    [Test:   [Test:
Survey]  A/B]      Usage]   Prototype]
```

---

## Opportunity Branches

### Opportunity 1: Users don't understand the product's value in their first session

**Description:** New users sign up but don't experience an "aha moment" in their first session. They leave without understanding what the product can do for them.
**Impact Potential:** High
**Confidence:** High

**Evidence:**
- User Research (P3): "I signed up but wasn't sure what I was supposed to do first. I just clicked around."
- Data: 65% of churned users never completed their first task
- Support Tickets: "How do I get started?" is #2 most common question
- User Research (P7): "The product looks powerful but I didn't have time to figure it out."

#### Solutions

**Solution 1A: Interactive Onboarding Wizard**
- Description: A 3-step guided wizard that walks users through creating their first project, adding a task, and seeing the dashboard.guaranteeing they experience core value
- Effort: M
- Riskiest Assumption: Users will complete a multi-step wizard rather than skipping it
- Assumption Test: Prototype test with 5 users measuring completion rate and time-to-value

**Solution 1B: Personalized Video Walkthrough**
- Description: A 90-second contextual video (based on use case selected at signup) showing the product solving their specific problem
- Effort: M
- Riskiest Assumption: Users will watch a video rather than exploring on their own
- Assumption Test: A/B test video vs. no video, measure video completion and 7-day retention

**Solution 1C: Template Gallery with Guided Setup**
- Description: Offer pre-built templates (marketing calendar, sprint board, personal tasks) that users can clone, reducing setup friction
- Effort: S
- Riskiest Assumption: Templates match what users actually want to do
- Assumption Test: Survey 20 recent signups about their intended use case; compare to template library

---

### Opportunity 2: Users can't find the features they need when they need them

**Description:** Users come back with intent but can't locate features, get frustrated, and leave. The product has grown complex and discovery is poor.
**Impact Potential:** Medium
**Confidence:** Medium

**Evidence:**
- User Research (P2): "I know you have time tracking somewhere but I spent 10 minutes looking for it."
- Data: Search usage is low (8% of sessions) despite 50+ features
- Heatmaps: Users click on navigation items seemingly at random
- Support Tickets: "Where is X?" tickets increased 40% after last feature release

#### Solutions

**Solution 2A: Enhanced Search with Natural Language**
- Description: Upgrade search to understand natural language queries ("track time on this task") and show relevant features, not just content
- Effort: L
- Riskiest Assumption: Users will try search if we make it more prominent
- Assumption Test: Make search bar 2x larger and track usage change before investing in NLP

**Solution 2B: AI Feature Assistant**
- Description: A chatbot-style assistant that users can ask "how do I..." questions and get guided help
- Effort: XL
- Riskiest Assumption: Users will engage with a chatbot for navigation
- Assumption Test: Wizard of Oz test with 10 users.manually respond to chat queries and measure satisfaction

**Solution 2C: Contextual Feature Tips**
- Description: Surface relevant features based on what the user is doing (e.g., if editing a task, suggest time tracking)
- Effort: M
- Riskiest Assumption: Contextual tips won't feel intrusive or annoying
- Assumption Test: A/B test tips on/off for one feature area; monitor dismissal rate and feature adoption

---

### Opportunity 3: Users forget to come back.nothing prompts their return

**Description:** Users have initial intent but without habits formed or external triggers, they simply forget the product exists until they need it again (if ever).
**Impact Potential:** High
**Confidence:** High

**Evidence:**
- Data: 70% of churned users never returned after day 3
- User Research (P5): "It's a good product, I just forgot about it. I have a lot going on."
- Cohort Analysis: Users who engage within 72 hours have 3x retention
- Benchmark: Competitors send 4-5 emails in first week; we send 1

**Evidence:**

#### Solutions

**Solution 3A: Email Re-engagement Sequence**
- Description: A 5-email sequence over 14 days that highlights unused features and incomplete tasks, bringing users back with specific reasons
- Effort: S
- Riskiest Assumption: Users will open and act on emails rather than ignore or unsubscribe
- Assumption Test: A/B test new sequence vs. current single email; measure open rate, click rate, and return rate

**Solution 3B: Mobile Push Notifications**
- Description: Smart push notifications for task deadlines, team activity, and usage prompts
- Effort: M
- Riskiest Assumption: Users have mobile app installed and notifications enabled
- Assumption Test: Check current mobile app install rate (currently 23%); if low, this opportunity is capped

**Solution 3C: Weekly Digest Email**
- Description: A personalized weekly summary showing what happened in their workspace and what needs attention
- Effort: S
- Riskiest Assumption: Users have enough activity to make digest interesting
- Assumption Test: Mock up digest for 10 real users and assess if there's enough content to be useful

---

## Prioritization

### Current Focus

**Priority Opportunity:** Opportunity 1 - Users don't understand value in first session
**Priority Solution:** Solution 1A - Interactive Onboarding Wizard
**Rationale:** Highest confidence (strong evidence), addresses earliest stage of user journey (biggest impact window), medium effort (achievable this quarter). If users never understand value, no amount of re-engagement will save them.

### Opportunity Ranking

| Rank | Opportunity | Impact | Confidence | Effort | Score |
|------|-------------|--------|------------|--------|-------|
| 1 | First session value clarity | High | High | Medium | 8/10 |
| 2 | Users forget to return | High | High | Small | 7/10 |
| 3 | Feature discoverability | Medium | Medium | Large | 5/10 |

### Parking Lot

- **Gamification elements:** Interesting but unproven in B2B; needs more research before prioritizing
- **Community features:** High effort, unclear impact; revisit after core retention improves
- **Slack integration reminders:** Good idea but requires Slack app rebuild; defer to Q3

---

## Experiments Backlog

| Solution | Assumption | Test Method | Success Criteria | Status |
|----------|------------|-------------|------------------|--------|
| Onboarding Wizard | Users will complete wizard | Prototype test (5 users) | >70% completion | Planned |
| Onboarding Wizard | Wizard improves time-to-value | A/B test (2 weeks) | >20% reduction in time to first task | Planned |
| Email Sequence | Users open/click emails | A/B test (2 weeks) | >25% open rate, >5% click rate | Planned |
| Template Gallery | Templates match use cases | Survey (20 users) | >60% match rate | Planned |

---

## Learning Log

| Date | Experiment | Result | Learning | Impact on Tree |
|------|------------|--------|----------|----------------|
| TBD | Wizard prototype | TBD | TBD | TBD |

---

## Next Steps

- [ ] Design onboarding wizard prototype (Design, this week)
- [ ] Schedule 5 usability tests for wizard (Research, next week)
- [ ] Draft email re-engagement sequence copy (Marketing, this week)
- [ ] Pull data on mobile app install rate to size Opp 3B (Data, this week)
- [ ] Review tree with engineering to validate effort estimates (PM, this week)

---

*This is a living document. Update as you learn from experiments and customer feedback.*

</details>

## Real-World Examples

See this skill applied to three different product contexts:

<details>
<summary>Storevine (B2B): Storevine B2B ecommerce platform . churn reduction opportunity tree for Campaigns Q2 sprint planning</summary>

**Prompt:**

```
/opportunity-tree

Project: Campaigns . and broader platform churn reduction strategy
Stage: Pre-Q2 sprint planning . framing the opportunity space before
scope lock

Desired outcome: Reduce annual merchant churn from 22% [fictional] to
14% [fictional] by Q4 2026

Opportunities I've identified from discovery:
1. Merchants can't run email re-engagement without a separate external
   tool (Q4 exit survey: 22% churn [fictional]; 8 merchant interviews)
2. Merchants can't see what's driving revenue across their store
   (interview finding: "there's no way to see what's working" . P8)
3. Merchants are paying for Storevine + external tools and beginning to
   question whether the platform subscription is worth it

Prior work:
- Competitive analysis (Feb 2026): email is the most-cited capability gap
- Interview synthesis (Jan 2026): non-adopter segment and Klaviyo lock-in
- Stakeholder summary: Campaigns PRD moving to engineering kickoff soon

Need: full opportunity-solution tree with visual, solution options per
branch, prioritization, and experiments backlog for Q2 planning.
```

---

**Output:**

# Opportunity Solution Tree: Reduce Annual Merchant Churn

</details>

<details>
<summary>Brainshelf (Consumer): Brainshelf consumer PKM app . opportunity tree for saved content re-engagement</summary>

**Prompt:**

```
/opportunity-tree

outcome: increase 7-day return rate from 18% to 25% [fictional]
by end of Q2 2026.

three opportunities from interviews + data:
1. re-engagement trigger gap . users have no external prompt to return
2. content relevance decay . old saves lose timeliness, making the
   library feel stale
3. library overwhelm . 400-item undifferentiated list creates avoidance

solutions I'm considering:
- opp 1: morning email digest (resurface), in-app notification card
- opp 2: freshness scoring, auto-archive stale items
- opp 3: intent tagging at save time, smart collections

want to prioritize opp 1 / email digest based on competitive analysis
(readwise is the only proof point for email resurfacing).
```

---

**Output:**

# Opportunity Solution Tree: Increase 7-Day Return Rate

</details>

<details>
<summary>Workbench (Enterprise): "Workbench enterprise collaboration platform: enterprise expansion via governance, SSO, and Confluence migration"</summary>

**Prompt:**

```
/opportunity-tree

Product: Workbench Blueprints (enterprise doc templates with required sections and approval gates)
Stage: Define phase, post-discovery and problem statement

Desired outcome: Expand Workbench enterprise customer base from 500 to 650 accounts within 12 months [fictional]
Current state: 500 enterprise customers [fictional]; enterprise churn in compliance segments is 18% annual [fictional]; 8 pipeline deals ($1.8M ARR) stalled on governance gaps [fictional]
Timeframe: 12 months from GA launch
Owner: Sandra C. (Head of Product)

Opportunities identified from discovery research:
1. Documentation governance gap -- no enterprise doc tool enforces template completion; 38% of docs reach approval incomplete [fictional]; enterprise teams need "templates with teeth"
2. SSO and security parity gap -- enterprise IT blocks deployment of tools without SSO/SAML, audit logs, and SOC 2; this is a pre-qualification filter, not a differentiator, but it is a hard gate
3. Confluence migration friction -- 4 of 6 interview participants had Confluence experience; all described migration as expensive and risky; Blueprints must offer a capability Confluence cannot provide to justify the migration cost

Solutions to explore per opportunity:
- Governance: required-section enforcement, native approval gates, template admin controls
- SSO/security: SAML integration, audit log export, SOC 2 Type II, data residency options
- Migration: Confluence template import, guided manual migration, migration support program

Prioritization: Governance is highest impact and highest confidence. SSO is table stakes (must-have, not differentiator). Migration is medium confidence (we believe it drives adoption but have not tested).

Stakeholders: Sandra C. (Head of Product), Karen L. (Eng Lead), Mei-Lin T. (Enterprise Sales Lead)
```

---

**Output:**

# Opportunity Solution Tree: Enterprise Customer Expansion via Blueprints

</details>

## Quality Checklist

Before finalizing, verify:

- [ ] Outcome is measurable and within product team's influence
- [ ] Opportunities are customer-centric (needs/problems, not features)
- [ ] Each opportunity has supporting evidence documented
- [ ] Multiple solutions exist per opportunity (not jumping to one)
- [ ] Assumptions are explicit and experiments designed
- [ ] Prioritization is clear (which branch to explore first)

## Output Format

Use the template in `references/TEMPLATE.md` to structure the output.
