Chains estimation, decision analysis, and storytelling to transform uncertain choices into clear, stakeholder-ready recommendations. Quantifies uncertain variables, applies expected value analysis to identify the best option, then packages the analysis into a persuasive narrative. Use when evaluating strategic options (build vs buy, market entry, resource allocation), quantifying tradeoffs, justifying investments, pitching to decision-makers, or when user mentions ROI analysis, expected value, business case, cost-benefit, or needs to combine estimation with persuasive communication.
Three phases: Estimation (quantify uncertain variables with ranges and probabilities), Decision (apply expected value or scoring to identify best option), Storytelling (package analysis into compelling narrative for stakeholders).
Quick Example:
# Should we build custom analytics or buy a SaaS tool?
## Estimation
Build custom: $200k-$400k dev cost (60% likely $300k), $50k/year maintenance
Buy SaaS: $120k/year subscription, $20k implementation
## Decision
Expected 3-year cost:
- Build: $300k + (3 × $50k) = $450k
- Buy: $20k + (3 × $120k) = $380k
- Difference: $70k savings with Buy
Expected value with risk adjustment:
- Build: 30% chance of 2x cost overrun → $510k expected
- Buy: 95% confidence in pricing → $380k expected
- Recommendation: Buy (lower cost, lower risk)
## Story
"We evaluated building custom analytics vs. buying a SaaS solution. While building seems cheaper initially ($300k vs. $380k over 3 years), custom development carries significant risk—30% of similar projects experience 2x cost overruns, bringing expected cost to $510k. The SaaS solution offers predictable pricing, faster time-to-value (2 months vs. 8 months), and proven reliability. Recommendation: Buy the SaaS tool, saving $130k in expected costs and delivering value 6 months earlier."
Copy this checklist and track your progress:
Chain Estimation → Decision → Storytelling Progress:
- [ ] Step 1: Clarify decision and gather inputs
- [ ] Step 2: Estimate uncertain variables
- [ ] Step 3: Analyze decision with expected value
- [ ] Step 4: Craft persuasive narrative
- [ ] Step 5: Validate and deliver
Step 1: Clarify decision and gather inputs
Define the choice (what decision needs to be made?), identify alternatives (2-5 options to compare), list uncertainties (what variables are unknown or probabilistic?), determine audience (who needs to be convinced?), and clarify constraints (budget, timeline, requirements). Ensure the decision is actionable and the options are mutually exclusive.
Step 2: Estimate uncertain variables
For each alternative, quantify costs (fixed, variable, opportunity), estimate benefits (revenue, savings, productivity), assign probabilities to scenarios (best case, base case, worst case), and perform sensitivity analysis (which inputs matter most?). Use ranges rather than point estimates. For simple cases → Use resources/template.md for structured estimation. For complex cases → Study resources/methodology.md for advanced techniques (Monte Carlo, decision trees, real options).
Step 3: Analyze decision with expected value
Calculate expected outcomes for each alternative (probability-weighted averages), compare using decision criteria (NPV, payback period, IRR, utility), identify dominant option (best expected value or risk-adjusted return), and test robustness (does conclusion hold across reasonable input ranges?). Document assumptions explicitly. See Common Patterns for decision-type specific approaches.
Step 4: Craft persuasive narrative
Structure story with: problem statement (why this decision matters), alternatives considered (show you did the work), analysis summary (key numbers and logic), recommendation (clear choice with reasoning), next steps (what happens if approved). Tailor to audience: executives want bottom line and risks, technical teams want methodology and assumptions, finance wants numbers and sensitivity.
Step 5: Validate and deliver
Self-check using resources/evaluators/rubric_chain_estimation_decision_storytelling.json. Verify: estimates are justified with sources/logic, probabilities are calibrated (not overconfident), expected value calculation is correct, sensitivity analysis identifies key drivers, narrative is clear and persuasive, assumptions are stated explicitly, risks and limitations are acknowledged. Minimum standard: Score ≥ 3.5. Create chain-estimation-decision-storytelling.md output file with full analysis and recommendation.
For build vs buy decisions:
For market entry decisions:
For resource allocation:
For technology decisions:
For hiring/staffing decisions:
Do:
Don't:
Common Pitfalls:
resources/template.md - Structured estimation → decision → story frameworkresources/methodology.md - Advanced techniques (Monte Carlo, decision trees, real options)resources/examples/ - Worked examples (build vs buy, market entry, hiring decision)resources/evaluators/rubric_chain_estimation_decision_storytelling.jsonchain-estimation-decision-storytelling.md