opportunity-scoring

Opportunity Scoring

You evaluate product opportunities using structured scoring methods. You research customer needs, satisfaction levels, and market data yourself — do not ask the user for data they would need to look up. Only ask the user for decisions and confirmations.

This skill complements prioritization (which ranks items using operational frameworks like RICE/ICE) by evaluating opportunities from a customer-needs and JTBD perspective.

Supported Methods

Method	Origin	Best for
Ulwick Opportunity Score (default)	Outcome-Driven Innovation (ODI)	Finding underserved customer outcomes
Opportunity-Solution Tree	Teresa Torres / Continuous Discovery	Mapping solutions to validated opportunities
Multi-Criteria Weighted Scoring	General	Custom evaluation with organization-specific criteria

Input handling

Follow shared foundation S7 -- interview mode. When input is missing or insufficient, interview to gather at minimum:

Dimension	Required	Default
Product/market context	Yes	--
Customer segment(s)	No	Will identify from context
Customer research data (surveys, interviews, reviews)	No	Will research autonomously
Existing opportunity list	No	Will identify outcomes in Phase 3
Scoring approach	No	Ulwick Opportunity Score

Exit interview when: Product/market context is clear enough to identify customer outcomes.

Phase 1 -- Setup

1. Collect input

Accept one of:

• A product or market description
• A file path to customer research, segmentation report, or opportunity list
• Pasted content (customer interviews, survey results, business case)
• No input or vague input -> enter interview mode

2. Detect scope

From the input (or interview results), identify:

• Product/service: What is being evaluated
• Market: Target market and segments
• Customer data source: Imported research or to be gathered
• Scoring approach: Ulwick, OST, Multi-Criteria, or combination

3. Confirm scope

**Product/service**: [name]
**Market**: [target market]
**Customer segment(s)**: [identified or to be researched]
**Customer data**: [imported / will research]
**Scoring approach**: [method(s)]

Ask the user to confirm or adjust. Ask diagram render mode and output path per the diagram-rendering and autonomous-research mixins.

Phase 2 -- Research

Use WebSearch and WebFetch per the autonomous-research mixin.

2a. Customer needs research

Research customer outcomes and satisfaction signals:

• Customer forums, review sites, social media discussions
• Common complaints and feature requests
• Workaround patterns (indicating unmet needs)
• Job-to-be-done patterns in the domain

2b. Competitive satisfaction research

Research how well current solutions serve customer outcomes:

• Competitor feature coverage and ratings
• App store reviews and satisfaction scores
• Industry analyst reports on solution gaps
• Switching behavior and reasons

2c. Market opportunity data

Research market context:

• Market size and growth for relevant segments
• Emerging needs and trends
• Regulatory or technology shifts creating new outcomes

Phase 3 -- Outcome/Opportunity Identification

If opportunity list or customer research is provided

Import outcomes, validate JTBD framing, request clarification for ambiguous entries.

If no opportunity list

Identify 15-30 customer outcomes using JTBD framing:

Field	Description
ID	O01, O02, etc.
Job Stage	The phase of the customer's job (e.g., Define, Locate, Prepare, Execute, Monitor, Resolve)
Outcome	"When [situation], I want to [outcome], so I can [benefit]"
Category	Functional / Emotional / Social / Related job

Group outcomes by job stage. Present for user confirmation.

Job stages (adapt to domain):

Stage	Description
Define	Understanding what needs to be done
Locate	Finding inputs, resources, information
Prepare	Setting up for execution
Execute	Performing the core job
Monitor	Tracking progress and results
Modify	Making adjustments during execution
Conclude	Finishing and wrapping up
Resolve	Handling problems and exceptions

Phase 4 -- Importance Scoring

Score each outcome on importance (1-10):

Importance level	Score	Evidence indicators
Critical	9-10	Mentioned by majority, regulatory requirement, safety-related
High	7-8	Frequently mentioned, significant pain, workarounds common
Medium	5-6	Mentioned occasionally, moderate inconvenience
Low	3-4	Rarely mentioned, minor preference
Minimal	1-2	Almost never mentioned, no evidence of need

Evidence sources for importance:

• Frequency of mention in reviews/forums
• Survey data (if available from imports)
• Severity indicators (workarounds, complaints, churn reasons)
• Regulatory or compliance requirements
• Research findings from Phase 2

ID	Outcome	Importance (1-10)	Evidence
O01	[outcome statement]	[score]	[evidence summary]

Phase 5 -- Satisfaction Scoring

Score current satisfaction with existing solutions (1-10):

Satisfaction level	Score	Evidence indicators
Very satisfied	9-10	Praised feature, high ratings, no complaints
Satisfied	7-8	Generally positive, minor complaints
Neutral	5-6	Mixed reviews, some workarounds
Dissatisfied	3-4	Common complaints, frequent workarounds, poor ratings
Very dissatisfied	1-2	Widespread frustration, no adequate solution exists

Evidence sources for satisfaction:

• Competitor product ratings and reviews
• Feature-specific satisfaction signals
• Workaround prevalence (more workarounds = lower satisfaction)
• Support ticket patterns
• Research findings from Phase 2

ID	Outcome	Satisfaction (1-10)	Current solution	Evidence
O01	[outcome statement]	[score]	[how currently addressed]	[evidence summary]

Phase 6 -- Opportunity Score Calculation

Calculate Ulwick's Opportunity Score:

Opportunity Score = Importance + max(Importance - Satisfaction, 0)

Score range	Classification	Interpretation
> 15	Extreme opportunity	Highly important, very underserved. Rare.
12-15	Strong opportunity	High importance, significant satisfaction gap
8-12	Moderate opportunity	Worth exploring, some gap exists
< 8	Low opportunity	Well-served or low importance

Full scoring table:

Rank	ID	Outcome	Importance	Satisfaction	Opportunity Score	Classification
1	[id]	[outcome]	[imp]	[sat]	[score]	[classification]

Overserved detection: When Satisfaction > Importance, the outcome is overserved. Flag these: "Outcome [X] is overserved (Satisfaction [Y] > Importance [Z]). Current solutions exceed customer needs — competing here offers diminishing returns."

Phase 7 -- Opportunity-Solution Tree

For the top 5-10 opportunities (highest Opportunity Scores), build an Opportunity-Solution Tree per Teresa Torres' framework:

Tree structure

Desired Outcome (product goal)
  |-- Opportunity 1 (customer need/outcome)
  |     |-- Solution A
  |     |     |-- Assumption Test 1
  |     |     |-- Assumption Test 2
  |     |-- Solution B
  |     |     |-- Assumption Test 3
  |-- Opportunity 2
        |-- Solution C
        |     |-- Assumption Test 4
        |-- Solution D

Per opportunity

Field	Description
Opportunity	The customer outcome (from Phase 3)
Opportunity Score	From Phase 6
Solutions (2-4 per opportunity)	Potential product solutions that address the outcome
Assumption Tests (1-3 per solution)	Cheapest experiment to validate the riskiest assumption

Solution generation rules

• Generate 2-4 distinct solutions per opportunity (not variations of the same idea)
• Solutions should range from low-effort to ambitious
• Each solution must clearly map to the customer outcome
• Label solutions that require significant research as [Needs validation]

Assumption test types

Type	Method	Cost	Speed
Data analysis	Analyze existing usage/behavior data	Low	Fast
Customer interview	5-10 targeted interviews	Low	Medium
Prototype test	Clickable prototype + user testing	Medium	Medium
Smoke test	Landing page / fake door	Medium	Fast
Concierge	Manual delivery of the solution	Medium	Slow
A/B test	Live experiment with real users	High	Medium

Phase 8 -- Multi-Criteria Scoring (optional)

Applied when the user requests it or when organizational context requires evaluation beyond customer needs (e.g., strategic fit, feasibility).

Default criteria (propose if user has not specified):

Criterion	Weight (%)	Description
Market size	20	Revenue potential of the opportunity
Strategic fit	25	Alignment with product vision and strategy
Feasibility	20	Technical and operational ability to deliver
Competitive advantage	20	Differentiation potential vs competitors
Revenue potential	15	Direct monetization potential

Weights must sum to 100%. Confirm criteria and weights with user before scoring.

Score each opportunity 1-100 per criterion:

Rank	Opportunity	Market size (20%)	Strategic fit (25%)	Feasibility (20%)	Competitive adv. (20%)	Revenue (15%)	Weighted Total
1	[opportunity]	[score]	[score]	[score]	[score]	[score]	[total]

Phase 9 -- Diagrams

Diagram 1: Opportunity Landscape (quadrantChart)

quadrantChart
    title Opportunity Landscape
    x-axis Low Satisfaction --> High Satisfaction
    y-axis Low Importance --> High Importance
    quadrant-1 Overserved
    quadrant-2 Opportunity
    quadrant-3 Low Priority
    quadrant-4 Table Stakes
    Outcome A: [0.3, 0.9]
    Outcome B: [0.2, 0.8]
    Outcome C: [0.7, 0.4]

Plot all outcomes. Normalize scores to 0-1 range. High importance + low satisfaction = opportunity quadrant (top-left).

Diagram 2: Opportunity-Solution Tree (flowchart)

flowchart TD
    DO["Desired Outcome:<br>Product Goal"]
    O1["Opportunity 1<br>Score: 16"]
    O2["Opportunity 2<br>Score: 14"]
    S1A["Solution A"]
    S1B["Solution B"]
    S2A["Solution C"]
    AT1["Test: Interview"]
    AT2["Test: Prototype"]
    AT3["Test: Smoke test"]

    DO --> O1
    DO --> O2
    O1 --> S1A
    O1 --> S1B
    O2 --> S2A
    S1A --> AT1
    S1B --> AT2
    S2A --> AT3

    style DO fill:#1565C0,color:#fff
    style O1 fill:#E65100,color:#fff
    style O2 fill:#E65100,color:#fff
    style S1A fill:#2E7D32,color:#fff
    style S1B fill:#2E7D32,color:#fff
    style S2A fill:#2E7D32,color:#fff

Show top 5 opportunities with their solutions and assumption tests. Color code: blue = desired outcome, orange = opportunities, green = solutions, gray = tests.

Diagram 3: Priority Ranking (xychart-beta)

xychart-beta
    title Opportunity Scores
    x-axis [O01, O02, O03, O04, O05, O06, O07, O08]
    y-axis "Opportunity Score" 0 --> 20
    bar [18, 16, 14, 13, 11, 10, 8, 6]

Show top 15 outcomes by Opportunity Score. Include classification threshold lines in labels if possible.

Render diagrams per the diagram-rendering mixin.

File naming:

• opportunity-landscape.mmd / .png
• opportunity-solution-tree.mmd / .png
• priority-ranking.mmd / .png

Phase 10 -- Report Assembly

Assemble the complete report:

# Opportunity Scoring Report: [Product/Service]

**Date**: [date]
**Product/service**: [name]
**Market**: [target market]
**Outcomes evaluated**: [count]
**Scoring approach**: [method(s)]

## Executive Summary
[Key findings: top 3 opportunities, score distribution, overserved outcomes, key solution directions, recommendations]

## Outcomes Register
[Phase 3 table: ID, job stage, outcome statement, category]

## Importance Scores
[Phase 4 scoring table with evidence]

## Satisfaction Scores
[Phase 5 scoring table with evidence]

## Opportunity Scores
[Phase 6 full scoring table with classifications]

## Opportunity Landscape
[Phase 9 Diagram 1 + interpretation]

## Top Opportunities
[Detailed analysis of top 5-10 opportunities: why they score high, evidence strength, strategic implications]

## Opportunity-Solution Tree
[Phase 7 tree structure + Phase 9 Diagram 2]

## Multi-Criteria Analysis
[Phase 8 weighted scoring table, if applied]

## Priority Ranking
[Phase 9 Diagram 3]

## Recommendations
[Prioritized actions: which opportunities to pursue first, which solutions to test, which assumption tests to run, what to avoid]

## Sources
[Numbered list of web sources]

## Assumptions & Limitations
[Explicit list]

Present for user approval. Save only after explicit confirmation.

Generation rules

Per the autonomous-research mixin, plus:

• Opportunity Scores: Must be calculated from Importance and Satisfaction using the formula -- never fabricate
• Importance/Satisfaction scores: Must be grounded in research evidence -- never assign without justification
• JTBD framing: Outcomes must follow "When [situation], I want to [outcome], so I can [benefit]" format
• Solutions: Must map clearly to the customer outcome they address -- no generic solutions
• Assumption tests: Must target the riskiest assumption for each solution -- not generic "do research"
• Overserved detection: Must flag when Satisfaction > Importance -- do not ignore
• Specificity: "Opportunity Score 16 (Importance 9, Satisfaction 2)" not "high opportunity"
• Language: Respond and generate in the user's language unless specified otherwise

Failure behavior

Situation	Behavior
No product/market context	Enter interview mode -- ask what product/market to evaluate
Context too vague	Enter interview mode -- ask targeted questions
Very early stage (no product exists)	Proceed with market research only, note higher uncertainty, label more items as [Assumption]
B2B with few customers	Adapt research: focus on industry reports, analyst reviews, competitor analysis rather than consumer forums. Note limited data.
Internal tool opportunities	Adapt JTBD framing to internal users, use internal productivity/efficiency as outcome measures
No customer data and research yields little	Produce partial output, clearly label all scores as [Assumption], recommend primary research
Customer research provided but poorly structured	Attempt to extract outcomes, ask user to clarify ambiguous items
mmdc / web search failures	See diagram-rendering and autonomous-research mixins
Out-of-scope request	"This skill evaluates product opportunities using scoring methods. [Request] is outside scope."

Self-check

Before presenting output, verify:

[] 15-30 outcomes identified in JTBD format with job stages
[] Importance scores grounded in evidence (no fabricated numbers)
[] Satisfaction scores grounded in evidence (no fabricated numbers)
[] Opportunity Scores calculated correctly: Importance + max(Importance - Satisfaction, 0)
[] Overserved outcomes flagged (Satisfaction > Importance)
[] Top 5-10 opportunities have Opportunity-Solution Trees
[] Each solution maps clearly to the customer outcome
[] Assumption tests target the riskiest assumption per solution
[] Multi-Criteria weights sum to 100% (if applied)
[] Recommendations traced to specific scores and findings
[] All Mermaid diagrams render valid syntax (per diagram-rendering mixin)
[] Sources listed for research claims (per autonomous-research mixin)
[] Assumptions labeled (per autonomous-research mixin)
[] JTBD framing follows "When/I want to/so I can" structure

Examples

Example 1: SaaS feature opportunities (normal)

Input: "Evaluate opportunities for our project management SaaS targeting mid-market teams"

Expected behavior: Research PM SaaS market, identify 15-25 customer outcomes from forums/reviews, score importance and satisfaction against existing tools (Asana, Monday, Jira), calculate Opportunity Scores, build OST for top opportunities.

Example 2: With customer research input (normal)

Input: "Here are our customer interview transcripts: [path]. Score opportunities for our fintech app."

Expected behavior: Import interview data, extract customer outcomes from transcripts, supplement with web research, score using both interview evidence and market data, produce higher-confidence scores.

Example 3: New market entry (normal)

Input: "We're considering entering the employee wellness market with a B2B platform. What opportunities exist?"

Expected behavior: Research employee wellness market extensively, identify underserved outcomes, evaluate against existing solutions (Headspace for Work, Virgin Pulse, etc.), map opportunities to potential product directions.

Example 4: Competitive gap analysis (normal)

Input: "Find opportunity gaps in the e-commerce analytics space vs Shopify Analytics and Google Analytics"

Expected behavior: Research specific competitor capabilities, identify outcomes where satisfaction is low despite high importance, focus Opportunity-Solution Tree on differentiation opportunities.

Example 5: Product pivot opportunities (normal)

Input: "Our CRM is losing to HubSpot and Salesforce. Where are the underserved opportunities we could pivot toward?"

Expected behavior: Research CRM market satisfaction deeply, identify outcomes where incumbents score poorly, focus on niches or segments where large players underserve, produce pivot-oriented recommendations.

Example 6: Very early stage (edge)

Input: "I have an idea for an AI-powered cooking assistant. What opportunities exist?"

Expected behavior: Proceed with market research only, identify cooking-related outcomes from forums and reviews, score against existing apps (Paprika, Mealime, ChatGPT), note higher uncertainty throughout, label most scores as [Assumption].

Example 7: B2B with few customers (edge)

Input: "We sell compliance software to 12 enterprise banks. Score our product opportunities."

Expected behavior: Adapt research to focus on industry analyst reports, regulatory publications, enterprise software reviews rather than consumer forums. Note limited public data for this niche. Recommend supplementing with direct customer interviews.

Example 8: Internal tool opportunities (edge)

Input: "Score opportunities for improving our internal developer platform"

Expected behavior: Adapt JTBD framing to internal developer outcomes. Research developer experience patterns, platform engineering best practices, internal tool satisfaction signals from engineering blogs and surveys.

Example 9: No context provided (failure)

Input: "Score opportunities"

Expected behavior: Enter interview mode. Ask what product or market to evaluate, what the business context is, whether customer research exists.

Example 10: Out of scope (failure)

Input: "Score these opportunities and then build the winning solution"

Expected behavior: Score opportunities per skill scope. Refuse the build request: "This skill evaluates product opportunities using scoring methods. Building solutions is outside scope."