Create and Prioritize Opportunities (Continuous Discovery Habits)

Goal

To extract opportunities from interview snapshots and synthesis results based on customer needs, pain points, and desires,
organize, review, and prioritize them according to the Opportunity Solution Tree structure,
and ultimately propose target opportunities for exploration.

When to Use

After completing interview snapshots using Create Interview Snapshots
After synthesizing multiple snapshots using Synthesize Interview Snapshots
When you need to identify and prioritize customer problems worth solving
Before generating solutions to ensure focus on the right opportunities

Input

Primary Source: Interview snapshots from user-interviews/snapshots/
Secondary Source: Synthesis documents from user-interviews/synthesis/
Context Source: All strategic materials from company-level-context/ (Product Vision, Product Strategy, OKRs, Goals, Team Structure, other company strategy documents, etc.)
Minimum Requirements: At least 3-5 interview snapshots or 1 synthesis document

Output

Format: Markdown (.md)
Location: opportunities/[topic]/
Filename: opportunities-[topic]-v[version].md

Semantic Naming Guidelines:

topic: kebab-case topic name extracted from content analysis (e.g., newsletter-creation, user-onboarding)
version: auto-incrementing version number (v1, v2, v3...)
Example: opportunities-newsletter-creation-v1.md

Version Management:

Check existing files with same topic pattern before creating new opportunities
Auto-increment version number (v1 → v2 → v3...)
Never overwrite existing opportunity files
Preserve all opportunity versions for comparison
No date dependency required

AI Instructions for Opportunity Creation

When Receiving Interview Data

Input Quality Validation: Verify that snapshots or synthesis meet minimum standards
Research Gaps Identification: Record missing information that could strengthen opportunity identification
Additional Data Request: Request more interviews if sample size is insufficient
Context Source Utilization: Automatically scan and utilize all strategic materials from company-level-context/ for opportunity prioritization decisions
Topic Extraction: Extract main topic from content analysis for semantic filename
File Existence Check: MANDATORY - Check for existing files with pattern opportunities-[topic]-v*.md before creating new file
Version Management: MANDATORY - Auto-increment version number based on existing files. Never overwrite existing files

Opportunity Extraction Guidelines

Focus on Customer Needs, Pain Points, Desires: Follow Teresa Torres' opportunity solution tree principles by focusing on customer needs, pain points, and desires
Problem-Focused Statements: Use problem-focused statement format: "I want to ~ but ~ makes it difficult"
Avoid Solution Language: Reconstruct feature suggestions or solution requests as underlying needs
Identify Root Causes: Don't stay at surface-level complaints, identify root causes
Preserve Customer Language: Use exact customer quotes and customer terminology
Document Evidence: Link each opportunity to specific examples and quotes for evidence documentation

Opportunity Statement Formatting Rules

Correct Opportunity Statement Format:

Need-based: "I want [desired outcome] but [barrier/problem] makes it difficult"
Pain point-based: "I feel frustrated in [situation] because of [specific problem]"
Desire-based: "I wish I could [desired experience] but [current constraint] makes it impossible"

Incorrect Opportunity Statement (Avoid These):

❌ Feature suggestions: "Highlight/VOD instant recommendation feature"
❌ Solution requests: "We need chapter functionality"
❌ Technical solutions: "Automatic handoff functionality"

Correct Examples:

✅ "I can't find content worth watching"
✅ "I've run out of episodes of my favorite show"
✅ "I don't know how to search for specific programs"
✅ "The program I was watching is no longer available"

Quality Standards

Customer Problem Focus: Each opportunity clearly describes customer needs, pain points, and desires
Problem Statement Format: Use problem-focused statement format: "I want ~ but ~ makes it difficult"
Evidence Strength: Supported by multiple participants or strong behavioral evidence
Actionable Scope: Problems that can be addressed through product changes
Customer-Centric Language: Expressed in customer language, not company terms
Solution-Free: Focus on underlying customer needs, not feature suggestions or solution requests

Semantic File Naming Guidelines

Topic Extraction: Analyze interview content to identify main topic/theme
Filename Format: Use semantic naming pattern opportunities-[topic]-v[version].md
Version Management: Auto-increment version number based on existing files
Folder Organization: Create topic-specific subfolders for better organization
No Date Dependency: Remove all date-based filename requirements

Topic Extraction Process:

Analyze interview snapshots or synthesis for common themes and keywords
Identify the most frequent and relevant topic/theme
Convert to kebab-case format (e.g., "Newsletter Creation" → "newsletter-creation")
Ensure topic uniqueness across different opportunity files
Use topic as primary identifier instead of date

Version Management Process:

MANDATORY STEP 1: Check existing files with pattern opportunities-[topic]-v*.md
MANDATORY STEP 2: Find the highest version number for the same topic
MANDATORY STEP 3: Auto-increment version number (v1 → v2 → v3...)
MANDATORY STEP 4: Generate new filename with incremented version
MANDATORY STEP 5: Verify no file with new filename exists before creation
CRITICAL: Never overwrite existing files - always create new version
No manual version tracking required

Smart Topic Detection:

Content Analysis: Extract main themes from interview snapshots or synthesis
Keyword Frequency: Use most frequent relevant keywords as topic
Kebab-Case Format: Convert spaces to hyphens, lowercase (e.g., "User Onboarding" → "user-onboarding")
Uniqueness Check: Ensure topic doesn't conflict with existing files
Fallback: Use generic topic name if extraction fails

Process

0) File Management (MANDATORY PRE-STEP)

Extract topic from interview data or synthesis content
Check existing files with pattern opportunities-[topic]-v*.md in target directory
Find highest version number for same topic (e.g., if v1, v2 exist, next is v3)
Generate new filename with incremented version: opportunities-[topic]-v[version].md
Verify no file exists with new filename before proceeding
CRITICAL: Never overwrite existing files - always create new version

0.5) Context Source Analysis (MANDATORY PRE-STEP)

Automatically scan all strategic materials in company-level-context/ directory
Categorize by file type: Product Vision, Product Strategy, OKRs, Goals, Team Structure, other strategy documents
Extract key strategic elements: vision, goals, priorities, constraints, resource status, etc.
Set opportunity evaluation criteria: alignment with company strategy, resource availability, business impact, etc.
Prepare context utilization: provide strategic context for priority decisions

1. Extract Opportunities

Extract customer stories, needs, pain points, and desires from interview snapshots and synthesis results.
Organize all items as Opportunities.
- Need-based: "I want [desired outcome] but [barrier/problem] makes it difficult"
- Pain point-based: "I feel frustrated in [situation] because of [specific problem]"
- Desire-based: "I wish I could [desired experience] but [current constraint] makes it impossible"
- Do not include solution requests as opportunities; instead, reconstruct them as underlying needs.
Link evidence to each opportunity. (quotes, frequency, customer segments, etc.)
Exclude feature suggestions or solution requests and focus only on actual customer problems

2. Map Opportunities

Place all opportunities in the Opportunity Solution Tree structure (Outcome → Parent → Child → Leaf).
Use parent-child relationships and sibling relationships to break opportunities into specific, independent units.
Verify that each branch aligns with specific moments in the customer experience (Experience Map).
Reconstruct or merge opportunities that are duplicates, ambiguous, or disguised solutions.

3. User Review #1 — Review Opportunity Candidates

Present the opportunity candidate table to the user.
Each row: ID | Parent | Opportunity Statement | EvidenceCount | Quotes
Questions:
- "Are there any opportunities to merge/split/delete?"
- "Would you like to modify the name of any opportunities?"
PAUSE and incorporate user feedback.

4. Assess Opportunities

Evaluate by comparing and contrasting each sibling set.
Evaluation criteria through four lenses:

Opportunity Sizing — How many customers and how frequently does this occur?
Market Factors — How important is this from a competitive/market trend perspective? (table stakes vs differentiator)
Company Factors — How well does this align with our organizational strategy and capabilities?
- Context Source Utilization: Reference specific company-level-context/ files and strategic elements considered
- Strategic Alignment: How opportunity aligns with company vision, goals, OKRs
- Resource Considerations: Team capabilities, resource availability from context
Customer Factors — How important is this to customers? What is their satisfaction with existing alternatives?

Do not use scoring.
Instead, record subjective and data-driven discussions after comparison/contrast.

5. User Review #2 — Review Priorities

Provide priority comparison results in a summary table:
Sibling Set | Candidates | Key Factors | Discussion Notes | Tentative Winner
Questions:
- "Do you agree with this selection, or would you like to explore a different opportunity first?"
PAUSE and incorporate user feedback.

6. Propose Target Opportunity

Mark one of the tree's leaf nodes as a Target Opportunity (Proposal).
This is merely a suitable candidate for the team to begin exploration, not a final decision.
This proposal has a reversible (two-way door) nature and can be changed at any time during exploration based on new learning.

Process Flow

Continuous Discovery Workflow

Individual Interviews → Create Snapshots → Synthesize Patterns → Create Opportunities → Generate Solutions
     ↓                    ↓                    ↓                    ↓                    ↓
[Raw Data]        [Structured Stories]   [Shared Patterns]    [Problem Statements] [Product Ideas]

Input-Output Relationship

Input: Interview snapshots and/or synthesis documents
Process: Topic extraction, opportunity extraction, mapping, prioritization
Output: Prioritized opportunity solution tree with target opportunity
Next Step: Use opportunities to generate multiple solutions

Recommended Folder Structure

opportunities/
├── newsletter-creation/
│   ├── opportunities-newsletter-creation-v1.md
│   └── opportunities-newsletter-creation-v2.md
├── user-onboarding/
│   ├── opportunities-user-onboarding-v1.md
│   └── opportunities-user-onboarding-v2.md
└── payment-flow/
    └── opportunities-payment-flow-v1.md

Output Structure (opportunities-[topic]-v[version].md)

# Opportunities — [Topic Name]

**Topic:** [Extracted topic name]  
**Version:** [v1, v2, v3...]  
**Source Documents:** [List of snapshots and/or synthesis documents used]  
**Context Sources:** [List of company-level-context files referenced]  
**Total Opportunities Identified:** [Number]  

---

## Opportunity Solution Tree (Snapshot)
- Outcome: [Desired Outcome]
  - Parent Opportunity A
    - Child 1
    - Child 2 (Proposed Target Opportunity ⭐)

---

## Candidate Opportunities (Pre-Review)
| ID | Parent | Opportunity Statement | Evidence Count | Supporting Quotes | Notes |
|----|--------|----------------------|----------------|-------------------|-------|
| 1 | [Parent] | [Problem statement in customer language] | [X] participants | "[Quote 1]" | [Additional context] |

---

## Review Notes #1
- User decisions: [Merged, Removed, Renamed opportunities]
- Changes made: [Specific modifications requested]

---

## Assessment (Sibling Comparisons)
### [Sibling Set A]
- **Candidates:** [List of opportunities being compared]
- **Factors Considered:** 
  - Opportunity Sizing: [Frequency and scope]
  - Market Factors: [Competitive landscape and trends]
  - Company Factors: [Strategic alignment and capabilities]
    - **Context Reference:** [Specific company-level-context files and strategic elements considered]
    - **Strategic Alignment:** [How opportunity aligns with company vision, goals, OKRs]
    - **Resource Considerations:** [Team capabilities, resource availability from context]
  - Customer Factors: [Importance and satisfaction with alternatives]
- **Discussion:** [Key points from comparison]
- **Tentative Winner:** [Selected opportunity with rationale]

---

## Review Notes #2
- User confirmation: [Proposed target maintained or adjusted]
- Final decision: [Confirmed target opportunity]

---

## Proposed Target Opportunity
**[ID] — [Opportunity Statement]**

**Why This Opportunity:**
- [Clear rationale for selection]
- [Alignment with research findings]

**Supporting Evidence:**
- [Multiple quotes and behavioral observations]
- [Frequency across participants]
- [Impact on user experience]

**Next Steps:** 
- [ ] Explore multiple solutions (see [Generate Solutions](/generate-solutions))
- [ ] Validate opportunity with additional research if needed
- [ ] Share findings with product team

---

## Quality Indicators

### Strong Opportunities
- **Clear Customer Problem**: Not general complaints but **specific customer needs/pain points/desires**
- **Problem-Focused Statement**: Clear problem statement in "I want to ~ but ~ makes it difficult" format
- **Strong Evidence**: Multiple participants or detailed **behavior-based stories**
- **Customer Language**: Expressed in **user's own words**
- **Actionable Scope**: Problems that can be addressed through **product changes**
- **Solution-Free**: Focus on **underlying customer needs**, not feature suggestions

### Weak Opportunities
- **Vague Description**: General statements without specific context
- **Single Source**: Only mentioned by one participant
- **Solution Disguised**: Product feature request instead of problem (e.g., "highlight recommendation feature")
- **Company Language**: Internal terminology instead of customer voice
- **Feature Request**: Contains solution or feature suggestions

---

## Guardrails
- Opportunities must be expressed in **customer language** (prohibition of internal company language).
- **Feature suggestions or solution requests are not Opportunities**.  
  (e.g., "highlight recommendation feature" → **"I can't find content worth watching"** - reconstruct as underlying need)
- Emotions themselves are not Opportunities.  
  (e.g., "I'm frustrated" → **reconstruct as underlying opportunity**)
- Must use **problem-focused statement** format: "I want ~ but ~ makes it difficult"
- Prohibition of unnecessary precise scoring. Focus on **relative comparison** and **discussion-based recording**.
- All decisions are **hypothetical and reversible** and can be adjusted at any time based on continuous learning.

---

## Error Handling

### Insufficient Data
- **Too Few Snapshots**: Request additional interviews before opportunity creation
- **Weak Evidence**: Ask for more specific behavioral examples
- **Missing Context**: Request additional details from original interviews

### File Naming Issues
- **Topic Extraction Failure**: Ensure clear topic identification from content analysis
- **Version Conflicts**: Always check existing files before creating new opportunities
- **Incorrect Format**: Use kebab-case for topic, v[number] for version
- **Missing Topic**: Extract main theme from interview snapshots or synthesis for filename
- **Overwriting Prevention**: Never overwrite existing opportunity files, always increment version
- **File Existence Check Failure**: MANDATORY - Always verify file existence before creation
- **Version Detection Error**: If version detection fails, start with v1 and add warning note

### Quality Issues
- **Vague Problems**: Push for specific customer stories and examples
- **Solution Requests**: Help reframe as underlying customer needs
- **Weak Evidence**: Highlight areas needing stronger support

---

## Quality Assurance Checklist

### Input Validation
- [ ] Sufficient interview data available (3+ snapshots or 1 synthesis)
- [ ] Data quality meets standards from create-interview-snapshots
- [ ] Behavioral evidence is concrete and specific
- [ ] Context sources from company-level-context/ have been scanned and analyzed
- [ ] Strategic alignment factors have been identified from context documents

### Opportunity Quality
- [ ] Each opportunity clearly describes a customer problem
- [ ] Problems are expressed in customer language
- [ ] Evidence supports each opportunity
- [ ] Opportunities are actionable through product changes

### Process Completion
- [ ] All opportunities mapped to solution tree structure
- [ ] User review completed for opportunity candidates
- [ ] Prioritization assessment completed with context source considerations
- [ ] Company factors assessment includes strategic alignment analysis
- [ ] Target opportunity selected and documented
- [ ] Next steps are clear and actionable

### File Naming Validation
- [ ] **MANDATORY**: Extracted clear topic from content analysis before creating filename
- [ ] **MANDATORY**: Checked existing files with pattern `opportunities-[topic]-v*.md` before creation
- [ ] **MANDATORY**: Verified no file with new filename exists before creation
- [ ] Filename uses semantic naming format: opportunities-[topic]-v[version].md
- [ ] Version number is correctly auto-incremented for same-topic opportunities
- [ ] Topic name is descriptive and kebab-case formatted
- [ ] **CRITICAL**: No overwriting of existing opportunity files - always create new version
- [ ] Topic extraction completed through content analysis before opportunity creation
- [ ] Version management process followed step-by-step

---

## AI Implementation Checklist

### Before Creating Any Opportunity File:
1. **Context Source Analysis** ✅
   - [ ] Scan `company-level-context/` directory for all strategic documents
   - [ ] Identify and categorize files: Product Vision, Product Strategy, OKRs, Goals, Team Structure, etc.
   - [ ] Extract key strategic elements: vision, goals, priorities, constraints, resources
   - [ ] Prepare context for opportunity assessment and prioritization

2. **Topic Extraction** ✅
   - [ ] Analyze interview data or synthesis for main theme
   - [ ] Extract kebab-case topic name (e.g., "newsletter-creation")
   - [ ] Verify topic is descriptive and unique

3. **File Existence Check** ✅
   - [ ] Search for existing files: `opportunities-[topic]-v*.md`
   - [ ] List all found files with their version numbers
   - [ ] Identify highest version number

4. **Version Management** ✅
   - [ ] Calculate next version number (highest + 1)
   - [ ] Generate new filename: `opportunities-[topic]-v[version].md`
   - [ ] Verify new filename doesn't already exist

5. **File Creation** ✅
   - [ ] Create file with new filename only
   - [ ] Never overwrite existing files
   - [ ] Add version number to document header
   - [ ] Include context sources in document metadata

### Error Handling:
- **If topic extraction fails**: Use generic topic name and add warning
- **If version detection fails**: Start with v1 and add warning note
- **If file already exists**: Increment version and try again
- **If multiple topics found**: Use most relevant one and document choice
- **If context source scan fails**: Continue with opportunity creation but note missing context
- **If no strategic documents found**: Proceed with customer-focused assessment only
- **If context files are inaccessible**: Document limitation and focus on available data

---

## Related Frameworks
- [Create Interview Snapshots](/create-interview-snapshots)
- [Synthesize Interview Snapshots](/synthesize-snapshots)
- [Generate Solutions](/generate-solutions)

create-opportunities