lyndonkl/chain-estimation-decision-storytelling

Chain Estimation → Decision → Storytelling

Table of Contents

• Workflow
• Common Patterns
• Guardrails
• Quick Reference

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."

Workflow

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.

Common Patterns

For build vs buy decisions:

• Estimate: Development cost (effort × rate), maintenance cost, SaaS subscription, implementation cost
• Decision: 3-5 year TCO, risk-adjusted for schedule overruns and feature gaps
• Story: "Build gives us control but costs $X more and takes Y months longer..."

For market entry decisions:

• Estimate: TAM/SAM/SOM, CAC, LTV, time-to-profitability
• Decision: Expected NPV with market uncertainty (optimistic/pessimistic scenarios)
• Story: "If we enter now, base case is $X revenue by year 3, but if market adoption is slower..."

For resource allocation:

• Estimate: Cost per initiative, expected impact (revenue, cost savings, strategic value)
• Decision: Impact/effort scoring or expected value ranking
• Story: "Given $X budget, these 3 initiatives deliver $Y expected return vs. $Z for alternatives..."

For technology decisions:

• Estimate: Migration cost, operational cost, performance improvement, risk reduction
• Decision: TCO over 3-5 years plus risk-adjusted benefits
• Story: "Migrating to X costs $Y upfront but saves $Z annually and reduces outage risk from..."

For hiring/staffing decisions:

• Estimate: Compensation, recruiting cost, ramp time, productivity impact
• Decision: Cost per incremental output vs. alternatives (contractors, vendors, automation)
• Story: "Adding 3 engineers at $X cost delivers $Y additional capacity, enabling..."

Guardrails

Do:

• Use ranges for uncertain estimates (not false precision)
• Assign probabilities based on data or explicit reasoning
• Calculate expected value correctly (probability-weighted outcomes)
• Perform sensitivity analysis (test assumptions)
• State assumptions explicitly
• Acknowledge risks and limitations
• Tailor narrative to audience (exec vs technical vs finance)
• Include "what would change my mind" conditions
• Show your work (transparent methodology)
• Test robustness (does conclusion hold with different assumptions?)

Don't:

• Use single-point estimates for highly uncertain variables
• Claim false precision ("$347,291" when uncertainty is ±50%)
• Ignore risk or downside scenarios
• Cherry-pick optimistic assumptions
• Hide assumptions or methodology
• Overstate confidence in estimates
• Skip sensitivity analysis
• Make recommendation before analyzing alternatives
• Use jargon without defining terms for audience
• Forget to state next steps or decision criteria

Common Pitfalls:

• Anchoring bias: First estimate becomes "default" without testing alternatives
• Optimism bias: Best-case scenarios feel more likely than they are
• Sunk cost fallacy: Including past costs that shouldn't affect forward-looking decision
• Overconfidence: Narrow ranges that don't reflect true uncertainty
• Ignoring opportunity cost: Not considering what else could be done with resources
• Analysis paralysis: Spending too much time estimating vs. deciding with available info

Quick Reference

• Template: resources/template.md - Structured estimation → decision → story framework
• Methodology: resources/methodology.md - Advanced techniques (Monte Carlo, decision trees, real options)
• Examples: resources/examples/ - Worked examples (build vs buy, market entry, hiring decision)
• Quality rubric: resources/evaluators/rubric_chain_estimation_decision_storytelling.json
• Output file: chain-estimation-decision-storytelling.md
• Key distinction: Combines quantitative rigor (estimation, expected value) with qualitative persuasion (narrative, stakeholder alignment)
• When to use: High-stakes decisions with uncertainty that need buy-in (not routine choices or purely data-driven optimizations)