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 rollback
3. Environment misconfiguration (11.5) -- incremental improvements to validation
4. Untested code prevention (10.4) -- maintain current pre-deploy checks
5. Onboarding time (9.0) -- consider simplifying; current docs may be over-engineered

Small-Team Adaptations

Ulwick's methodology assumes large-scale surveys (100+ respondents). For small agile teams:

• 5-10 interviews sufficient for directional signals. Treat scores as relative rankings, not absolute.
• Support tickets as proxy data: High-frequency complaints = high importance + low satisfaction.
• Team consensus estimation: When user access limited, rate collectively. Document as "team estimate."
• Iterate: Re-score after each release as satisfaction shifts.

Integration with Other Prioritization Methods

Method	Best For	Combine With Opportunity Scoring
MoSCoW	Sprint-level scope decisions	Use opportunity scores to inform Must/Should/Could
Value/Effort matrix	Quick relative ranking	Use opportunity scores as "value" axis
RICE	Feature-level prioritization	Use opportunity score as "Impact" component
Story mapping	Release planning	Use opportunity scores to prioritize rows (MVP vs later)

Cross-References

• For core JTBD theory and job map steps: load jtbd-core skill
• For interview techniques to gather importance/satisfaction data: load jtbd-interviews skill
• For translating high-priority outcomes to BDD scenarios: load jtbd-bdd-integration skill
• For story-level prioritization (MoSCoW, Value/Effort): load leanux-methodology skill