Jtbd Opportunity Calculator

JTBD Opportunity Calculator

Calculate Outcome-Driven Innovation® (ODI) Opportunity Scores from quantitative customer survey data.

The Opportunity Score identifies where customers struggle to get a job done (underserved) vs. where current solutions exceed customer value without adding utility (overserved).

Scope

Input must include:

• survey_metadata: importance_scale, satisfaction_scale, sample_size, population_definition, collection_method
• outcomes: A list of Desired Outcome Statements with numerical importance_mean and satisfaction_mean ratings

Output:

• Calculated Opportunity Score per outcome statement
• Classification: extreme_underserved ($Opp \ge 15.0$), high_underserved ($12.0 \le Opp I_i$
• Invent, estimate, or hallucinate numeric ratings from qualitative text or interview quotes
• Apply 10-point thresholds to any scale other than matching 1_to_10 inputs; v0.1 rejects non-10-point scales
• Formulate business growth strategies or product roadmaps (delegate to jtbd-growth-strategist)

Mathematical Formula

For each outcome $i$, using 1-to-10 scales:

$$\text{Opportunity Score}i = \text{Importance}i + \max(\text{Importance}i - \text{Satisfaction}i, 0)$$

1. Importance ($I_i$): Mean customer rating of outcome importance (1.0 to 10.0).
2. Satisfaction ($S_i$): Mean customer rating of outcome satisfaction with current solutions (1.0 to 10.0).
3. Satisfaction Gap: If $Ii > Si$, the gap is $(Ii - Si)$. If $Si \ge Ii$, the gap is $0$.
4. Max Value: On a 1-to-10 scale, maximum possible Opportunity Score is $10.0 + (10.0 - 1.0) = 19.0$.

Opportunity Classification Rules

Opportunity classification is determined strictly by the Opportunity Score bounds:

• extreme_underserved: $Opp \ge 15.0$
• high_underserved: $12.0 \le Opp S_i$
• matches_importance: $Ii == Si$
• above_importance: $Si > Ii$
• overserved_signal: Set to true ONLY when $Opp Ii$. High importance outcomes ($Ii \ge 8.0$) with $Si > Ii$ have $Opp \ge 8.0$ and are classified as appropriately_served, NOT overserved_candidate.

Scale & Data Quality Discipline

• Data Quality Status:
• complete: Required metadata and numeric ratings are present.
• incomplete: Missing values, missing metadata, or text input without ratings (calculation_status: blocked).
• invalid: Scale mismatch, out-of-bounds ratings ($ 10.0$), or non-numeric values.
• Methodological Assessment:
• sample_size_status: adequate ($N \ge 100$), small ($N I_i$.

e. Assign opportunity classification based on Opportunity Score.

1. Assess methodological limitations and populate data_limitations.
2. Return the calculation report.

Output Format

survey_metadata:
  importance_scale: "1_to_10"
  satisfaction_scale: "1_to_10"
  sample_size: 0
  population_definition: ""
  collection_method: ""

data_quality_status: complete | incomplete | invalid
calculation_status: completed | blocked

methodological_assessment:
  sample_size_status: adequate | small | unknown
  representativeness: unverified
  collection_method_status: reported | missing

scale_handling:
  calculation_scale: "1_to_10"
  normalization: none
  threshold_interpretation: standard

## Future Scope

Future versions may support caller-provided, documented normalization for non-10-point scales. v0.1 rejects all non-1_to_10 scales and does not apply normalization.

data_limitations: []

results:
  - id: ""
    statement: ""
    importance_mean: 0.0
    satisfaction_mean: 0.0
    satisfaction_gap: 0.0
    satisfaction_relation: below_importance | matches_importance | above_importance
    opportunity_score: 0.0
    classification: extreme_underserved | high_underserved | moderate_underserved | appropriately_served | overserved_candidate
    overserved_signal: true | false
    segment: ""

next_research_question: ""

Reference Materials

Read references/opportunity-algorithm-rules.md before:

• Handling non-10-point scale normalization
• Analyzing opportunity landscapes and satisfaction relations
• Executing deterministic Python calculations using scripts/calculate_opportunity.py

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: lishix520
• Source: lishix520/jtbd-skills
• License: MIT

Install and usage instructions live in the source repository linked above.