Portfolio Decision Engine
Executive decision support · scoring models · portfolio strategy
A synthetic capstone model that combines demand, attendance, completion, satisfaction, lifecycle age, data quality, audience coverage, strategic whitespace, and portfolio overlap into transparent recommendations to Scale, Maintain, Refresh, Consolidate, or Develop.
The workbook is designed as a governance-friendly decision system: every recommendation is formula-driven, the weights and thresholds are visible, and a reviewer can change the assumptions without rewriting the model.
Decision output
| Action | Synthetic recommendation count | Decision use |
|---|---|---|
| Scale | 5 | High-performing offers with meaningful strategic whitespace |
| Maintain | 8 | Stable products without an urgent intervention signal |
| Refresh | 6 | Aging offers that retain enough value to preserve |
| Consolidate | 5 | Redundant products with limited differentiated value |
| Develop | 6 | High-demand whitespace with low current audience coverage |
Portfolio action mix
Maintain — 8
Refresh — 6
Develop — 6
Scale — 5
Consolidate — 5
All products, scores, thresholds, counts, and recommendations shown here are synthetic demonstrations.
Highest-priority decisions
- Prompting for Strategic Work — Scale: high performance, strategic gap, and low redundancy.
- Applied AI Foundations — Scale: strong demand and learner signals with room for further portfolio expansion.
- Customer Journey Blueprint — Scale: high-performing offer aligned to strategic whitespace.
- GenAI Brainstorming Lab — Consolidate: high portfolio overlap with limited differentiated value.
- AI Governance for Marketing Leaders — Develop: strong whitespace and demand with low current audience coverage.
- Responsible AI Decisions — Scale: strong composite performance and strategic need.
Transparent decision model
The default performance model weights the signals below. These weights and all action thresholds are editable on the workbook’s Decision Model tab.
| Signal | Default weight |
|---|---|
| Demand | 20% |
| Attendance | 15% |
| Completion | 15% |
| Satisfaction | 15% |
| Data quality | 10% |
| Audience coverage | 10% |
| Strategic gap | 15% |
Example rules
- Scale: performance ≥ 75, strategic gap ≥ 0.60, and overlap below the consolidation threshold.
- Refresh: refresh age ≥ 18 months and performance ≥ 55.
- Consolidate: overlap ≥ 0.68 while performance remains below 68.
- Develop: gap ≥ 0.75, demand index ≥ 75, and current audience coverage ≤ 0.45.
Workbook architecture
- Executive Dashboard — action counts, ranked decisions, topic-level portfolio view, and model controls.
- Recommendations — combined ranking of existing products and development opportunities.
- Portfolio Inputs — demand, learner outcomes, lifecycle, QA, audience, gap, and redundancy signals.
- Gap Opportunities — new-product concepts scored for development.
- Decision Model — explicit weights and thresholds controlling the recommendations.
- Topic Portfolio — topic-level rollups for footprint, demand, gap, and development opportunity.
- Scenario Comparison — alternative growth, experience, reach, and trust lenses.
- Data Dictionary / Portfolio Guide — definitions, privacy context, and review instructions.
The workbook is intended to demonstrate transparent analytical decision support—not to claim that one fixed weighting system is universally correct.