Jtbd Opportunity Calculator

JTBD Opportunity Calculator

Use customer importance and satisfaction ratings to pinpoint the most valuable unaddressed needs.

Use this when

• You have quantitative survey rating data ($1\text{--}10$ scale) for customer desired outcomes.
• You need to compute mathematical ODI Opportunity Scores ($Opp = Importance + \max(Importance - Satisfaction, 0)$).
• You want to classify outcome metrics into extreme_underserved, high_underserved, moderate_underserved, appropriately_served, or overserved_candidate.

Don't use this when

• You do not have numerical survey data (never convert qualitative interview text into fake numbers!).
• You need to formulate outcome metric statements from scratch (use jtbd-outcome-engineer).
• You need to evaluate market growth strategies (use jtbd-growth-strategist).

Minimum input

• Minimum Input: Numerical importance and satisfaction mean ratings on a matching $1\text{--}10$ scale, sample size $N$, and outcome statements.
• Templates Available: Use templates under skills/jtbd-opportunity-calculator/templates/:
  • survey-input-template.json
  • survey-input-template.csv
  • survey-question-template.md

If ratings are missing or scales are non-10-point, returns calculation_status: blocked.

What you get

1. Executive Summary: Top underserved outcomes, outcomes not to prioritize, data strength rating, and recommended next steps.
2. Mathematical Opportunity Scores: Exact deterministic score calculations ($Opp$ range $1.0\text{--}20.0$).
3. Outcome Classifications: Objective categorization based on mathematical thresholds.

Quick prompt

"Compute Opportunity Scores for this survey data: '[Paste JSON/CSV ratings or run calculate_opportunity.py]'."

What to do next

• High underserved outcomes ($Opp \ge 10.0$)? Collect market price/cost/performance evidence and pass to jtbd-growth-strategist to evaluate candidate growth strategies.

Mathematical Formula

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

• Scale Requirement: Importance and Satisfaction MUST be on matching $1.0\text{--}10.0$ scales.
• Threshold Classifications:
  • extreme_underserved: $Opp \ge 15.0$
  • high_underserved: $12.0 \le Opp < 15.0$
  • moderate_underserved: $10.0 \le Opp < 12.0$
  • appropriately_served: $8.0 \le Opp < 10.0$
  • overserved_candidate: $Opp < 8.0$ (Requires $S > I$)

Deterministic Script Execution

Run the bundled Python calculation script directly against a survey JSON file:

python3 skills/jtbd-opportunity-calculator/scripts/calculate_opportunity.py \
  path/to/survey_input.json > opportunity_results.json

Output Format

executive_summary:
  top_underserved_outcomes: []
  outcomes_not_to_prioritize: []
  data_strength: exploratory | adequate_but_not_representative | unverified
  recommended_next_step: ""

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
  representativeness: unverified
  collection_method_status: reported | missing

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

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

data_limitations: []

Reference

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

• Handling small sample sizes ($N < 100$)
• Evaluating satisfaction relations ($S > I$)
• Rejecting non-10-point scales