/nw-jtbd-opportunity-scoring

name: nw-jtbd-opportunity-scoring
description: JTBD opportunity scoring and prioritization - outcome statement format, opportunity algorithm, scoring interpretation, feature prioritization, and opportunity matrix template
user-invocable: false
disable-model-invocation: true

JTBD Opportunity Scoring

Use when prioritizing features, stories, or backlog items based on customer-defined outcomes. Opportunity scoring (Ulwick's ODI) replaces gut-feel prioritization with evidence-based ranking.

Outcome Statements

Every customer need expressed as a desired outcome following strict format:

[Direction] + the [metric] + [object of control] + [contextual clarifier]

• **Direction**: "Minimize" or "Maximize"
• **Metric**: time, likelihood, number, or frequency

Examples

• "Minimize the time it takes to identify the root cause of a production issue"
• "Minimize the likelihood of deploying untested code to production"
• "Maximize the likelihood that acceptance criteria cover all relevant scenarios"
• "Minimize the number of iterations needed to reach shared understanding"
• "Minimize the frequency of false positive alerts during monitoring"

Quality Checks

Outcome statement must be: **Solution-free** (no specific technology) | **Measurable** (ratable on importance/satisfaction 1-5) | **Controllable** (customer can assess improvement) | **Unambiguous** (same interpretation by all stakeholders)

Deriving Outcome Statements

Walk the 8-step job map (see `jtbd-core` skill) and generate 2-3 per step. Produces 16-24 outcome statements per job -- comprehensive view of customer needs.

| Job Map Step | Outcome Statement Pattern | |-------------|--------------------------| | Define | "Minimize the time to determine [what is needed]" | | Locate | "Minimize the likelihood of missing [required input]" | | Prepare | "Minimize the time to set up [environment/context]" | | Confirm | "Minimize the likelihood of proceeding with [invalid state]" | | Execute | "Minimize the time to complete [core action]" | | Monitor | "Minimize the likelihood of [undetected failure]" | | Modify | "Minimize the time to recover from [exception]" | | Conclude | "Minimize the likelihood of [incomplete cleanup]" |

The Opportunity Algorithm

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

Where:

• **Importance** = % of respondents rating outcome 4 or 5 on 1-5 scale
• **Satisfaction** = % of respondents rating current satisfaction 4 or 5 on 1-5 scale
• **Score range**: 0-20 (higher = greater opportunity)

How It Works

Rewards outcomes both important and unsatisfied. If satisfaction >= importance, second term is zero (appropriately served). If satisfaction < importance, gap amplifies score (underserved).

Score Interpretation

| Score Range | Category | Action | |-------------|----------|--------| | 15-20 | Extremely underserved | High-priority; invest heavily | | 12-15 | Underserved | Strong opportunity; plan for next iteration | | 10-12 | Appropriately served | Maintain; incremental improvement | | < 10 | Overserved | Simplification candidate; may be over-engineered |

Applying to Feature Prioritization

Step 1: Generate Outcome Statements

From job mapping and interview findings, compile 15-30 per major job.

Step 2: Rate Importance and Satisfaction

Gather ratings from users/stakeholders. For small teams:

• Interview 5-10 users directly
• Use internal team ratings as proxy (mark as "team estimate" vs "user data")
• Leverage support tickets, feature requests, bug reports as signals

Step 3: Calculate and Rank

Compute scores, sort descending. Top scores = highest-priority features.

Step 4: Map to Stories

Each high-scoring outcome maps to one or more stories. Score 15+ should produce at least one story in current iteration.

Step 5: Identify Overserved Areas

Scores below 10 are simplification candidates. Resources on overserved outcomes are better redirected to underserved ones.

Opportunity Scoring Matrix Template

## Opportunity Scoring: [Product/Feature Area]

| # | Outcome Statement | Imp. (%) | Sat. (%) | Score | Priority |
|---|-------------------|----------|----------|-------|----------|
| 1 | Minimize the time to [outcome A] | | | | |
| 2 | Minimize the likelihood of [outcome B] | | | | |
| 3 | Maximize the [quality] when [context C] | | | | |

### Scoring Method
- Importance: % of respondents rating 4+ on 5-point scale
- Satisfaction: % of respondents rating 4+ on 5-point scale
- Score: Importance + max(0, Importance - Satisfaction)
- Priority: Extremely Underserved (15+), Underserved (12-15),
  Appropriately Served (10-12), Overserved (<10)

### Top Opportunities (Score >= 12)
1. [Outcome] -- Score: [X] -- Story: [link or title]
2. [Outcome] -- Score: [X] -- Story: [link or title]

### Overserved Areas (Score < 10)
1. [Outcome] -- Score: [X] -- Simplification opportunity: [description]

### Data Quality Notes
- Source: [user interviews / team estimates / support ticket analysis]
- Sample size: [N respondents]
- Confidence: [High if N >= 10 with user data, Medium if team estimates]

Worked Example

Context: CLI tool for deploying applications. 8 users surveyed.

| # | Outcome Statement | Imp. | Sat. | Score | Priority | |---|-------------------|------|------|-------|----------| | 1 | Minimize time to identify root cause of failed deployment | 92% | 35% | 14.9 | Extremely Underserved | | 2 | Minimize likelihood of deploying untested code | 88% | 72% | 10.4 | Appropriately Served | | 3 | Minimize time to roll back a bad deployment | 85% | 30% | 14.0 | Underserved | | 4 | Minimize time to onboard a new team member to deploy | 65% | 40% | 9.0 | Overserved | | 5 | Minimize likelihood of misconfiguring environment variables | 80% | 45% | 11.5 | Appropriately Served |

**Prioritization result**: 1. Root cause identification (14.9) -- build better deployment diagnostics 2. Rollback speed (14.0) -- invest in one-command ro