--- # === IDENTITY === id: consulting/recipes/counterfactual-scenario-workshop/2026 canonical_question: "How do you build personalized failure simulations and counterfactual scenarios for target accounts?" aliases: - "counterfactual scenario workshop for consulting sales" - "pre-mortem analysis for target accounts" - "Gary Klein pre-mortem applied to B2B sales" - "business entropy trajectory modeling" entity_type: execution_recipe domain: consulting > recipes > Counterfactual Scenario Workshop region: global jurisdiction: global temporal_scope: 2026-2027 # === VERIFICATION === last_verified: 2026-03-30 confidence: 0.85 version: 1.0 first_published: 2026-03-30 # === TEMPORAL VALIDITY === temporal_validity: status: evolving last_breaking_change: "Initial release — Rorschach GTM Module 6 hands-on exercise" next_review: 2026-09-26 change_sensitivity: high # === CONSTRAINTS === constraints: - "Maximum 3 target accounts per team per workshop — more than 3 produces shallow analysis" - "Failure simulations must use publicly available data only — no insider information or confidential intel" - "Loss aversion messaging must be calibrated to avoid manipulation — present verifiable consequences, not emotional exploitation" - "Business entropy projections must include confidence intervals — point estimates create false precision" - "Mock incident reports must be clearly labeled as simulations — any ambiguity about real vs. simulated events is an ethical violation" # === SKIP CONDITIONS === skip_this_unit_if: - condition: "User needs friction gate design, not counterfactual scenarios" use_instead: "consulting/recipes/friction-gate-design-exercise/2026" - condition: "User needs the full GTM roadmap, not just counterfactual design" use_instead: "consulting/recipes/gtm-roadmap-assembly/2026" - condition: "User needs signal detection theory, not scenario construction" use_instead: "consulting/rorschach-gtm/rorschach-meets-signal-stack/2026" # === AGENT HINTS === inputs_needed: - key: account_count question: "How many target accounts will be analyzed?" type: choice options: ["1 account (deep dive)", "2-3 accounts (recommended)", "4+ accounts (surface-level)"] - key: data_availability question: "What public data is available on target accounts?" type: choice options: ["public company (SEC filings, earnings calls)", "private company (Crunchbase, press releases)", "limited data (website + LinkedIn only)", "existing relationship (some internal context)"] - key: industry question: "What industry are the target accounts in?" type: choice options: ["technology/SaaS", "financial services", "healthcare", "manufacturing", "professional services", "other"] # === EXECUTION METADATA === execution: required_inputs: - name: "Target account list (3 accounts)" source: "consultant/sales team" format: "document" - name: "Public data dossier per account" source: "web research" format: "document" - name: "Industry failure case studies" source: "research/analyst reports" format: "document" outputs: - name: "Personalized Failure Simulation per Account" format: "document (narrative + data)" description: "Account-specific counterfactual scenario showing probable failure trajectory without intervention" - name: "Mock Incident Report per Account" format: "document" description: "Simulated post-mortem report written as if the predicted failure has already occurred, with specific claims and timeline" - name: "Business Entropy Trajectory Model" format: "spreadsheet + visualization" description: "6/12/18-month degradation projections with confidence intervals for each target account" tools_required: - name: "Public Data Research" purpose: "SEC filings, earnings calls, press releases, Glassdoor reviews, industry reports" tier: "free" cost: "$0" alternatives: ["SEC EDGAR", "Crunchbase", "LinkedIn", "Glassdoor"] - name: "Spreadsheet Tool" purpose: "Business entropy trajectory modeling" tier: "free" cost: "$0" alternatives: ["Google Sheets", "Excel"] - name: "Presentation Tool" purpose: "Mock incident report formatting" tier: "free" cost: "$0" alternatives: ["Google Slides", "PowerPoint", "Notion"] credentials_needed: [] estimated_duration: "4-6 hours (workshop) + 2-4 hours (per-account research)" estimated_cost: "$0 (tools) + consultant time" # === DISTRIBUTION === canonical_source: "https://knowledgelib.io/consulting/recipes/counterfactual-scenario-workshop/2026" suggested_citation: "Source: knowledgelib.io — AI Knowledge Library (verified 2026-03-30)" # === RELATED UNITS === related_kos: depends_on: - id: "consulting/rorschach-gtm/rorschach-meets-signal-stack/2026" label: "Signal Stack detection feeds counterfactual scenario construction" feeds_into: - id: "consulting/recipes/gtm-roadmap-assembly/2026" label: "GTM roadmap integrates counterfactual templates as sales assets" related_to: - id: "consulting/recipes/friction-gate-design-exercise/2026" label: "Friction gates qualify prospects before counterfactual delivery" - id: "consulting/rorschach-gtm/friction-meets-compliance-moat/2026" label: "Loss aversion calibration draws on costly signaling principles" # === SOURCES === sources: - id: src1 title: "Sources of Power: How People Make Decisions" author: Klein, G. url: https://mitpress.mit.edu/9780262611466/sources-of-power/ type: academic_paper published: 1998-02-01 reliability: authoritative - id: src2 title: "Thinking, Fast and Slow" author: Kahneman, D. url: https://us.macmillan.com/books/9780374533557/thinkingfastandslow type: academic_paper published: 2011-10-25 reliability: authoritative - id: src3 title: "Prospect Theory: An Analysis of Decision under Risk" author: Kahneman, D. & Tversky, A. url: https://doi.org/10.2307/1914185 type: academic_paper published: 1979-03-01 reliability: authoritative - id: src4 title: "The Challenger Sale" author: Dixon, M. & Adamson, B. url: https://www.penguinrandomhouse.com/books/304913/the-challenger-sale-by-matthew-dixon-and-brent-adamson/ type: primary_research published: 2011-11-10 reliability: high - id: src5 title: "Performing a Project Premortem" author: Klein, G. url: https://hbr.org/2007/09/performing-a-project-premortem type: primary_research published: 2007-09-01 reliability: high --- # 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 showing what happens without intervention, produces mock incident reports written as if the predicted failure already occurred, and models business entropy trajectories at 6/12/18-month horizons. The result is a set of hyper-personalized sales assets that leverage prospect theory — 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 for analysis - [ ] **Public data dossiers prepared** — SEC filings, earnings calls, press releases, Glassdoor reviews, industry analyst reports (2-4 hours research per account) - [ ] **Industry failure case studies** — 3-5 documented cases of organizations that failed in the way you predict these accounts could fail - [ ] **Pre-mortem methodology understood** — participants have read Klein's HBR article "Performing a Project Premortem" [src5] - [ ] **Friction gates designed** — counterfactual scenarios are delivered after prospect passes qualification (see Friction Gate Design Exercise) ## Constraints - Use publicly available data only. Using confidential information obtained through personal connections or data breaches is both unethical and legally risky. - Mock incident reports must be clearly, prominently labeled as simulations. Any suggestion that a simulated failure is a real event is fraud. [src1] - Loss aversion messaging presents verifiable consequences with evidence, not emotional manipulation. Kahneman's research shows that factual loss framing is more persuasive than emotional appeals anyway. [src2, src3] - Business entropy projections include confidence intervals. A projection of "revenue will decline 15%" without confidence bounds creates false precision and damages credibility when the actual number differs. - Maximum 3 accounts per team per workshop. Depth defeats breadth — shallow counterfactuals are useless because they are obviously generic. ## Tool Selection Decision ``` Which counterfactual approach? ├── Target is public company (SEC data available) │ └── PATH A: Financial Entropy Model — quantitative degradation projections from earnings data ├── Target is private company (limited financial data) │ └── PATH B: Operational Entropy Model — qualitative degradation from Glassdoor, press, LinkedIn ├── Target is in regulated industry │ └── PATH C: Compliance Entropy Model — regulatory risk trajectory from public filings └── Target has existing relationship (some internal context) └── PATH D: Hybrid Model — combine public data with relationship intelligence ``` | Path | Data Sources | Projection Quality | Personalization Depth | Effort per Account | |------|-------------|-------------------|----------------------|-------------------| | A: Financial Entropy | SEC filings, earnings calls, analyst reports | High — quantitative | Very high | 3-4 hours | | B: Operational Entropy | Glassdoor, LinkedIn, press releases, industry data | Medium — qualitative | High | 2-3 hours | | C: Compliance Entropy | Regulatory filings, enforcement actions, industry trends | High — pattern-based | High | 3-4 hours | | D: Hybrid | All public sources + relationship intelligence | Highest | Highest | 4-6 hours | ## Execution Flow ### Step 1: Select and Research Target Accounts **Duration**: 2-4 hours (per account) **Tool**: Web research + document assembly Select 3 target accounts per team. For each account, compile a public data dossier covering: - **Financial signals**: Revenue trajectory, margin trends, capital allocation patterns, debt levels (public companies only) - **Operational signals**: Glassdoor review sentiment trends, LinkedIn hiring/firing patterns, press releases about restructuring or pivots - **Competitive signals**: Market share trends, competitor movements, technology shifts in their industry - **Leadership signals**: Executive turnover, board changes, strategic messaging shifts in earnings calls or interviews **Verify**: Each account dossier contains minimum 10 distinct data points from at least 3 independent sources. **If failed**: If data is too sparse for a target account, replace it with a better-documented account. Sparse data produces generic scenarios that are useless. ### Step 2: Build Personalized Failure Simulations **Duration**: 60 minutes per account **Tool**: Structured template + team discussion For each target account, apply Klein's pre-mortem methodology [src1, src5]: assume the organization has failed in the specific way your service prevents. Work backward from the failure state to identify the causal chain: - **Define the failure state**: What specifically went wrong? (e.g., "Lost 30% of enterprise accounts to competitor X due to inability to scale integration infrastructure") - **Identify the causal chain**: What 3-5 sequential failures led to this outcome? Each link in the chain must be supported by a data point from the dossier - **Map the decision points**: Where could intervention have changed the trajectory? These become your value propositions - **Quantify the stakes**: What is the financial impact of the failure? Use industry benchmarks for credibility [src2] **Verify**: Each failure simulation has 3-5 causal chain links, each supported by at least one data point. The scenario feels account-specific, not generic. **If failed**: If the scenario reads as generic ("company fails due to lack of innovation"), add more account-specific detail from the dossier until it is unmistakably about this specific organization. ### Step 3: Apply Pre-Mortem Analysis (Gary Klein Method) **Duration**: 30 minutes per account **Tool**: Structured pre-mortem template Execute Klein's pre-mortem protocol [src5] adapted for sales scenario construction: 1. **Assume failure**: "It is 18 months from now. This organization has experienced [specific failure]. Your task is to explain why." 2. **Individual ideation** (5 minutes): Each participant independently writes 3-5 reasons the failure occurred 3. **Round-robin sharing**: Each participant shares one reason at a time until all are surfaced 4. **Prioritize by plausibility**: Vote on the 3 most likely causal factors based on the account dossier 5. **Construct the narrative**: Weave the top 3 causal factors into a coherent failure story with timeline **Verify**: Pre-mortem produces at least 3 distinct causal factors per account, prioritized by evidence strength. **If failed**: If team converges on a single causal factor, explicitly prompt for alternatives: "What else could go wrong, assuming this first factor is handled?" ### Step 4: Create Mock Incident Reports **Duration**: 45 minutes per account **Tool**: Document template (formatted as internal post-mortem) Write a mock incident report formatted as if the failure has already occurred. The report must: - Be dated 12-18 months in the future - Use the specific financial and operational claims from the failure simulation - Include a "contributing factors" section that maps directly to your service's value propositions - Include a "what could have been done differently" section — this is your sales pitch reframed as hindsight - Be **prominently labeled "SIMULATION — NOT A REAL EVENT"** at the top and bottom The mock incident report leverages the "narrative bias" — humans are more persuaded by stories than by statistics. The pre-mortem format makes the prospect psychologically experience the failure before it happens. [src1, src2] **Verify**: Mock incident report is specific enough that the target account would recognize it as being about them. Labels are prominent and unambiguous. **If failed**: If the report reads as generic, inject more account-specific details (named competitors, specific product lines, actual market data). ### Step 5: Calibrate Loss Aversion Messaging **Duration**: 30 minutes **Tool**: Messaging framework template Apply Kahneman and Tversky's prospect theory [src3] to calibrate the loss framing in each counterfactual: - **Loss framing ratio**: Present the cost of inaction at 2-2.5x the investment required (matching the empirical loss aversion coefficient) - **Certainty effect**: Present one near-certain small loss alongside the larger uncertain loss — people over-weight certain outcomes - **Reference point anchoring**: Anchor to the prospect's current state, then show degradation — not improvement from current state, but prevention of decline - **Social proof of loss**: Reference named organizations in their industry that experienced this specific failure pattern **Verify**: Loss framing feels factual and evidence-based, not manipulative. A sophisticated CFO would find it credible, not sleazy. **If failed**: If messaging feels manipulative, remove emotional language and increase data density. Let the numbers carry the loss framing. [src2] ### Step 6: Model Business Entropy Trajectories **Duration**: 45 minutes per account **Tool**: Spreadsheet with visualization Model the business entropy trajectory — the rate at which the organization's competitive position degrades without intervention — at 6, 12, and 18-month horizons: - **6-month projection**: Early warning indicators begin manifesting. Quantify: customer churn increase, revenue growth deceleration, operational cost creep - **12-month projection**: Degradation becomes visible to external stakeholders. Quantify: market share loss, talent attrition, competitive disadvantage crystallization - **18-month projection**: Structural damage becomes difficult to reverse. Quantify: customer base erosion, technology debt accumulation, strategic options narrowing Each projection must include: - Central estimate (most likely scenario) - 80% confidence interval (optimistic to pessimistic range) - Key assumptions listed explicitly **Verify**: Entropy projections are credible given the account's current trajectory. A financial analyst would not dismiss them as alarmist. **If failed**: If projections look alarmist, widen confidence intervals and moderate central estimates. Credibility is more important than impact. ## Output Schema ```json { "output_type": "counterfactual_scenario_package", "format": "document + spreadsheet + presentation", "sections": [ {"name": "account_dossiers", "type": "array", "description": "Public data dossier per target account (3 accounts)", "required": true}, {"name": "failure_simulations", "type": "array", "description": "Personalized failure scenario per account with causal chain", "required": true}, {"name": "pre_mortem_analyses", "type": "array", "description": "Klein pre-mortem output per account with prioritized causal factors", "required": true}, {"name": "mock_incident_reports", "type": "array", "description": "Simulated post-mortem report per account, clearly labeled as simulation", "required": true}, {"name": "loss_aversion_messaging", "type": "object", "description": "Calibrated loss framing guidelines with prospect theory ratios", "required": true}, {"name": "entropy_trajectories", "type": "array", "description": "6/12/18-month degradation models per account with confidence intervals", "required": true} ], "expected_sections": "6", "sort_order": "execution sequence" } ``` ## Quality Benchmarks | Quality Metric | Minimum Acceptable | Good | Excellent | |---------------|-------------------|------|-----------| | Account specificity (would target recognize scenario as about them?) | Somewhat specific | Clearly about them | Uncannily accurate | | Data points per account dossier | > 10 | > 15 | > 25 | | Causal chain evidence (% of links supported by data) | > 60% | > 80% | 100% | | Entropy projection credibility (financial analyst would not dismiss) | Plausible | Credible | Compelling | | Loss framing calibration (factual, not manipulative) | Acceptable | Professional | Indistinguishable from analyst report | **If below minimum**: Invest more research time per account. The most common failure mode is insufficient account-specific data producing generic scenarios. ## Error Handling | Error | Likely Cause | Recovery Action | |-------|-------------|----------------| | Account data too sparse for personalized scenario | Private company with minimal public footprint | Replace with better-documented account, or use industry-level scenario with account-specific anchoring | | Pre-mortem produces only one causal factor | Team anchoring on most obvious failure mode | Force 3 additional rounds with explicit constraint: "assume the first factor is resolved — what else fails?" | | Mock incident report reads as generic | Insufficient account-specific detail injected | Add named competitors, specific product lines, actual market data from dossier | | Loss framing perceived as manipulative | Too much emotional language, insufficient data support | Remove adjectives, increase data density, cite specific sources | | Entropy projection dismissed as alarmist | Central estimates too extreme, no confidence intervals | Widen intervals, moderate central estimates, add explicit assumptions | ## Cost Breakdown | Component | Per Account | 3 Accounts (Team) | Workshop Total | |-----------|-----------|-------------------|----------------| | Account research + dossier | 2-4 hours | 6-12 hours | 6-12 hours | | Failure simulation construction | 1-1.5 hours | 3-4.5 hours | 3-4.5 hours | | Pre-mortem session | 0.5 hours | 1.5 hours | 1.5 hours | | Mock incident report | 0.75 hours | 2.25 hours | 2.25 hours | | Loss aversion calibration | — | — | 0.5 hours | | Entropy trajectory modeling | 0.75 hours | 2.25 hours | 2.25 hours | | **Total time** | **5-7 hours** | **15-22.5 hours** | **16-23 hours** | | **Tool costs** | **$0** | **$0** | **$0** | ## Anti-Patterns ### Wrong: Creating generic failure scenarios that could apply to any company Writing "Company X will lose market share due to digital transformation failure" — a scenario so generic it has zero persuasive power. The prospect has heard this a thousand times from every consultant. [src4] ### Correct: Build hyper-specific scenarios from the account dossier "Company X's Q3 earnings call revealed a 15% increase in implementation backlog while competitor Y launched a self-service integration platform. At current trajectory, X loses 3-5 enterprise accounts per quarter to Y within 12 months." Specificity is persuasion. ### Wrong: Using confidential information in failure simulations Incorporating insider knowledge from personal connections, leaked documents, or unauthorized data access. Result: legal liability, ethical breach, and if discovered, permanent relationship destruction. ### Correct: Use only publicly available data Everything in the counterfactual must be traceable to a public source. This constraint actually improves credibility — the prospect knows you built this from the same information any competitor could access, demonstrating analytical superiority. [src4] ### Wrong: Calibrating loss aversion to maximize fear Writing the most alarming possible scenario to shock the prospect into action. Result: sophisticated buyers recognize manipulation and reject both the scenario and the consultant. [src2, src3] ### Correct: Calibrate loss aversion to match empirical prospect theory ratios Present losses at 2-2.5x the investment required (the empirical loss aversion coefficient). Let the data carry the emotional weight. Factual loss framing is more persuasive than emotional amplification. [src3] ## When This Matters Use when a consultant or B2B service firm needs to build hyper-personalized sales assets for named target accounts using counterfactual reasoning and loss aversion psychology. This is Module 6 of the Rorschach GTM curriculum — it follows friction gate qualification (Module 4) and precedes GTM roadmap assembly (Module 8). Requires public data research on 3 target accounts as the primary input. ## Related Units - [Rorschach Meets Signal Stack](/consulting/rorschach-gtm/rorschach-meets-signal-stack/2026) - [Friction Gate Design Exercise](/consulting/recipes/friction-gate-design-exercise/2026) - [GTM Roadmap Assembly](/consulting/recipes/gtm-roadmap-assembly/2026) - [Friction Meets Compliance Moat](/consulting/rorschach-gtm/friction-meets-compliance-moat/2026)