TL;DR:
- Map your desired outcome to specific opportunities using customer evidence
- Test one solution per opportunity with small experiments before building
- Run weekly reviews to update the tree based on new learning
- Track experiment velocity and assumption validation rates as key metrics
Table of contents
- Context and why it matters in 2025
- Step-by-step playbook
- Templates and examples
- Metrics to track
- Common mistakes and how to fix them
- FAQ
- Further reading
- Why CraftUp helps
Context and why it matters in 2025
Product teams waste months building features that customers ignore. The opportunity solution tree prevents this by creating a visual map from your desired business outcome down to specific solutions you can test quickly.
Most teams jump straight from problems to solutions without understanding the opportunity space. They build entire features based on assumptions, then wonder why adoption stays flat. An opportunity solution tree forces you to explore multiple opportunities for each outcome and test solutions before committing engineering resources.
Success means your team ships features that customers actually use and that move your key metrics. You know you are winning when your weekly tree reviews generate new experiments and your solution hit rate improves over time.
The framework works especially well in 2025 because teams need to move faster while being more deliberate about what they build. Customer Interview Questions That Get Real Stories and Problem Validation Scorecard: Compare Segments & Decide Tests become essential inputs for your tree mapping process.
Step-by-step playbook
1. Define your desired outcome
Goal: Establish one clear, measurable outcome that connects to business results.
Actions: Write your outcome as a specific metric with a target and timeframe. Examples: "Increase trial-to-paid conversion from 12% to 18% in Q2" or "Reduce time-to-first-value from 7 days to 3 days by March."
Example: A project management tool wants to "Increase weekly active users from 2,400 to 3,200 by June 2025." This outcome sits at the top of their tree and drives all discovery work below.
Pitfall: Picking multiple outcomes or vague goals like "improve user experience." This creates a scattered tree that leads nowhere.
Done: You have one outcome statement that your entire team can recite and that directly impacts revenue or retention.
2. Map opportunities through customer research
Goal: Identify 3-8 specific opportunities that could drive your outcome, backed by customer evidence.
Actions: Conduct customer interviews, analyze support tickets, review user session recordings, and examine funnel drop-off points. For each opportunity, write it as a customer need or pain point with supporting evidence.
Example: For the weekly active user outcome, opportunities might include "Users forget to check the tool daily" (evidence: 40% haven't logged in for 5+ days), "Teams struggle with initial project setup" (evidence: 60% abandon during first project creation), and "Mobile experience blocks on-the-go usage" (evidence: 80% of sessions under 2 minutes on mobile).
Pitfall: Creating opportunities based on internal assumptions rather than customer evidence. This leads to solutions that miss the mark.
Done: Each opportunity has customer quotes, data points, or behavioral evidence supporting why it matters for your outcome.
3. Generate solutions for each opportunity
Goal: Create 2-4 testable solutions per opportunity without committing to build any of them yet.
Actions: Brainstorm multiple approaches for each opportunity. Focus on the smallest version that would test your core assumption. Write each solution as a hypothesis: "If we do X, then Y will happen because Z."
Example: For "Users forget to check the tool daily," solutions could include "Smart email digest of project updates," "In-app notification system with customizable triggers," "Daily standup reminder with team progress," and "Gamified streak tracking for daily usage."
Pitfall: Falling in love with one solution per opportunity. This creates tunnel vision and misses better alternatives.
Done: You have multiple solution options per opportunity, each written as a testable hypothesis with clear assumptions.
4. Prioritize and test solutions
Goal: Pick one solution per opportunity to test first, starting with the highest-impact, lowest-effort options.
Actions: Use a simple impact vs. effort matrix to rank solutions. Design small experiments to test core assumptions before building. This could be landing pages, mockups, concierge testing, or prototype validation.
Example: Test "Smart email digest" with a manual version first. Send personalized project update emails to 50 users for two weeks, then measure engagement and app return rates compared to a control group.
Pitfall: Building full features as your first test. This wastes time and makes it harder to iterate based on learning.
Done: You have active experiments running for your top-priority solutions with clear success criteria and measurement plans.
5. Establish weekly review cadence
Goal: Create a sustainable rhythm for updating your tree based on new evidence and experiment results.
Actions: Schedule 60-minute weekly sessions with your core team (PM, designer, engineer). Review experiment results, add new opportunities from customer research, update solution priorities, and plan next week's tests.
Example: Every Tuesday at 2 PM, the team reviews last week's experiment data, discusses three new customer interview insights, moves two solutions from "testing" to "validated" or "invalidated," and commits to launching one new experiment by Friday.
Pitfall: Skipping reviews when busy or treating them as status updates rather than decision-making sessions.
Done: Your team has a recurring calendar invite with a standard agenda, and the tree visibly evolves each week based on new learning.
Templates and examples
# Opportunity Solution Tree Template
## Desired Outcome
[Specific metric + target + timeframe]
**Evidence:** [Business case and current baseline]
## Opportunity 1: [Customer need/pain point]
**Evidence:** [Customer quotes, data, behavioral signals]
### Solution 1A: [Hypothesis format]
- **Assumption:** [Core belief to test]
- **Experiment:** [How you'll test it]
- **Success criteria:** [What would prove it works]
- **Status:** [Not started/Testing/Validated/Invalidated]
### Solution 1B: [Alternative approach]
- **Assumption:** [Different core belief]
- **Experiment:** [Different test method]
- **Success criteria:** [Different success metrics]
- **Status:** [Current state]
## Opportunity 2: [Second customer need]
**Evidence:** [Supporting research]
### Solution 2A: [First approach]
- **Assumption:** [What you believe]
- **Experiment:** [How to test]
- **Success criteria:** [Validation threshold]
- **Status:** [Current state]
---
## Weekly Review Checklist
- [ ] Review completed experiments and update solution status
- [ ] Add new opportunities from customer research
- [ ] Prioritize untested solutions using impact/effort
- [ ] Plan 1-2 new experiments for next week
- [ ] Update outcome progress and timeline
Metrics to track
Experiment velocity
Formula: Number of experiments completed per week Instrumentation: Track experiment start/end dates in your tree document or project management tool Example range: 1-3 experiments per week for a team of 3-5 people
Assumption validation rate
Formula: (Validated assumptions / Total tested assumptions) × 100 Instrumentation: Mark each solution assumption as validated, invalidated, or inconclusive after testing Example range: 40-60% validation rate indicates good hypothesis quality
Opportunity coverage
Formula: Number of opportunities with at least one tested solution / Total opportunities identified Instrumentation: Count opportunities in your tree and track testing status Example range: 70-90% coverage shows comprehensive exploration
Solution iteration depth
Formula: Average number of solutions tested per opportunity Instrumentation: Count solution attempts per opportunity branch Example range: 2-4 solutions per opportunity indicates thorough exploration
Time to outcome impact
Formula: Weeks from opportunity identification to measurable outcome movement Instrumentation: Track discovery-to-delivery timeline for successful solutions Example range: 4-8 weeks from opportunity to outcome impact
Customer evidence freshness
Formula: Days since last customer research input to tree Instrumentation: Date stamp new evidence additions to opportunities Example range: 7-14 days maximum between research inputs
Common mistakes and how to fix them
• Building the tree once and never updating it. Fix: Schedule non-negotiable weekly reviews and treat the tree as a living document that evolves with new evidence.
• Making opportunities too broad or solution-focused. Fix: Write opportunities as specific customer needs with supporting quotes or data, not as feature categories.
• Testing multiple solutions simultaneously per opportunity. Fix: Focus on one solution per opportunity at a time to get clear learning and avoid resource splitting.
• Skipping small experiments and jumping to full builds. Fix: Always start with the smallest test that validates your core assumption, like mockups or concierge testing.
• Creating too many opportunities without customer evidence. Fix: Limit yourself to 3-8 opportunities maximum and require research evidence for each one.
• Using the tree for stakeholder communication instead of discovery. Fix: Keep your working tree messy and detailed, create separate clean versions for stakeholder updates.
• Forgetting to connect solutions back to the original outcome. Fix: Regularly check that your validated solutions actually move your outcome metric, not just solve customer problems.
• Making the tree too complex with multiple outcome levels. Fix: Start with one outcome and master the process before adding complexity or multiple outcome branches.
FAQ
How do I start an opportunity solution tree if I have no customer research? Begin with your current customer support tickets, user analytics, and any existing feedback. Identify 2-3 obvious pain points, then schedule customer interviews to validate and expand your opportunity list. The tree grows as your research improves.
What's the difference between an opportunity and a problem in the tree structure? Problems are broad customer pain points, while opportunities are specific, actionable needs within those problems. "Users struggle with onboarding" is a problem. "Users can't find the import feature during setup" is an opportunity.
How many solutions should I test per opportunity before moving on? Test until you find one that meaningfully impacts your outcome or until you've exhausted reasonable approaches. Usually 2-4 solutions per opportunity is sufficient before exploring other opportunities.
Should I involve engineering in the weekly opportunity solution tree reviews? Yes, but focus their input on feasibility and technical constraints for solutions, not on prioritizing opportunities. Engineers help estimate experiment complexity and spot technical assumptions to test.
How do I handle stakeholder pressure to skip experiments and build solutions directly? Show them the cost of building wrong solutions versus the speed of testing assumptions first. Share examples of invalidated solutions that would have wasted weeks of development time if built immediately.
Further reading
- Teresa Torres' Continuous Discovery Habits - The definitive guide to opportunity solution trees from their creator
- Jobs to Be Done Guide by Clayton Christensen - Framework for understanding customer opportunities and needs
- Lean Startup Experiment Design - Methods for testing solution assumptions quickly and cheaply
- Customer Development by Steve Blank - Techniques for discovering and validating opportunities through customer research
Why CraftUp helps
Learning to run effective discovery while shipping features requires daily practice with real frameworks.
- 5-minute daily lessons for busy people who need to balance discovery and delivery work
- AI-powered, up-to-date workflows PMs need for customer research, experiment design, and outcome measurement
- Mobile-first, practical exercises to apply opportunity solution tree methods immediately with your team
Start free on CraftUp to build a consistent product habit: https://craftuplearn.com
