Counterfactual Scenario Workshop

Purpose

This recipe executes Module 6 of the Rorschach GTM curriculum: building personalized failure simulations for target accounts using Gary Klein's pre-mortem methodology, Kahneman's loss aversion research, and business entropy modeling. Each team selects 3 target accounts, constructs account-specific counterfactual scenarios, produces mock incident reports, and models business entropy trajectories at 6/12/18-month horizons. People are 2-2.5x more motivated to avoid losses than to achieve equivalent gains. [src1, src3]

Prerequisites

• 3 target accounts selected — named accounts with sufficient public data
• Public data dossiers prepared — SEC filings, earnings calls, press releases, Glassdoor reviews
• Industry failure case studies — 3-5 documented cases of relevant failures
• Pre-mortem methodology understood — Klein's HBR article read [src5]
• Friction gates designed — counterfactuals delivered after prospect passes qualification

Constraints

• Use publicly available data only. Confidential information is both unethical and legally risky.
• Mock incident reports must be prominently labeled as simulations. Any ambiguity is fraud. [src1]
• Loss aversion messaging presents verifiable consequences with evidence, not emotional manipulation. [src2, src3]
• Business entropy projections must include confidence intervals. Point estimates create false precision.
• Maximum 3 accounts per team. Depth defeats breadth — shallow counterfactuals are useless.

Tool Selection Decision

Which counterfactual approach?
├── Public company (SEC data) --> PATH A: Financial Entropy Model
├── Private company (limited data) --> PATH B: Operational Entropy Model
├── Regulated industry --> PATH C: Compliance Entropy Model
└── Existing relationship --> PATH D: Hybrid Model

Execution Flow

Step 1: Select and Research Target Accounts

Duration: 2-4 hours per account · Tool: Web research + document assembly

Compile public data dossiers covering financial signals, operational signals (Glassdoor, LinkedIn), competitive signals, and leadership signals for each of 3 target accounts.

Verify: Each dossier contains minimum 10 data points from 3+ independent sources. · If failed: Replace sparse accounts with better-documented ones.

Step 2: Build Personalized Failure Simulations

Duration: 60 minutes per account · Tool: Structured template + team discussion

Apply Klein's pre-mortem: assume failure, work backward through 3-5 causal chain links each supported by dossier data. Map decision points where intervention could change trajectory. [src2]

Verify: Each simulation has 3-5 data-supported causal chain links. Scenario is account-specific, not generic. · If failed: Add more account-specific detail from dossier.

Step 3: Apply Pre-Mortem Analysis (Gary Klein Method)

Duration: 30 minutes per account · Tool: Structured pre-mortem template

Execute Klein's protocol: assume failure, individual ideation (5 min), round-robin sharing, prioritize by plausibility, construct narrative. [src1, src5]

Verify: 3+ distinct causal factors per account, prioritized by evidence. · If failed: Prompt for alternatives assuming first factor is handled.

Step 4: Create Mock Incident Reports

Duration: 45 minutes per account · Tool: Document template

Write mock incident reports dated 12-18 months future. Include contributing factors mapping to value propositions and "what could have been done differently" section. Prominently labeled "SIMULATION." [src1, src2]

Verify: Target account would recognize scenario as about them. Labels are unambiguous. · If failed: Inject more account-specific details.

Step 5: Calibrate Loss Aversion Messaging

Duration: 30 minutes · Tool: Messaging framework template

Apply prospect theory: loss framing at 2-2.5x investment, certainty effect, reference point anchoring, social proof of loss. [src3]

Verify: Framing feels factual and evidence-based, not manipulative. A CFO would find it credible. · If failed: Remove emotional language, increase data density. [src2]

Step 6: Model Business Entropy Trajectories

Duration: 45 minutes per account · Tool: Spreadsheet with visualization

Model degradation at 6, 12, and 18-month horizons. Each projection includes central estimate, 80% confidence interval, and explicit assumptions.

Verify: Projections credible — financial analyst would not dismiss as alarmist. · If failed: Widen confidence intervals and moderate central estimates.

Output Schema

{
  "output_type": "counterfactual_scenario_package",
  "format": "document + spreadsheet + presentation",
  "sections": [
    {"name": "account_dossiers", "type": "array", "description": "Public data dossier per account"},
    {"name": "failure_simulations", "type": "array", "description": "Personalized failure scenario per account"},
    {"name": "pre_mortem_analyses", "type": "array", "description": "Klein pre-mortem output per account"},
    {"name": "mock_incident_reports", "type": "array", "description": "Simulated post-mortem per account"},
    {"name": "loss_aversion_messaging", "type": "object", "description": "Calibrated loss framing guidelines"},
    {"name": "entropy_trajectories", "type": "array", "description": "6/12/18-month degradation models"}
  ]
}

Quality Benchmarks

If below minimum: Invest more research time per account.

Error Handling

Cost Breakdown

Anti-Patterns

Wrong: Creating generic failure scenarios applicable to any company

"Company X will lose market share due to digital transformation failure" — so generic it has zero persuasive power. [src4]

Correct: Build hyper-specific scenarios from the account dossier

Use specific financial data, named competitors, actual market trends. Specificity is persuasion.

Wrong: Using confidential information in failure simulations

Insider knowledge or unauthorized data access creates legal liability and permanent relationship destruction.

Correct: Use only publicly available data

Public-source constraint actually improves credibility — demonstrates analytical superiority from shared information. [src4]

Wrong: Calibrating loss aversion to maximize fear

Sophisticated buyers recognize manipulation and reject both scenario and consultant. [src2, src3]

Correct: Calibrate to empirical prospect theory ratios

Present losses at 2-2.5x the investment (empirical loss aversion coefficient). Let data carry the emotional weight. [src3]

When This Matters

Use when building hyper-personalized sales assets for named target accounts using counterfactual reasoning and loss aversion psychology. Module 6 of Rorschach GTM — follows friction gate qualification (Module 4), precedes GTM roadmap assembly (Module 8).