--- title: "/em:stress-test — Business Assumption Stress Testing" description: "/em:stress-test — Business Assumption Stress Testing - Claude Code skill from the C-Level Advisory domain." --- # /em:stress-test — Business Assumption Stress Testing
:material-account-tie: C-Level Advisory :material-identifier: `stress-test` :material-github: Source
Install: claude /plugin install c-level-skills
**Command:** `/em:stress-test ` Take any business assumption and break it before the market does. Revenue projections. Market size. Competitive moat. Hiring velocity. Customer retention. --- ## Why Most Assumptions Are Wrong Founders are optimists by nature. That's a feature — you need optimism to start something from nothing. But it becomes a liability when assumptions in business models get inflated by the same optimism that got you started. **The most dangerous assumptions are the ones everyone agrees on.** When the whole team believes the $50M market is real, when every investor call goes well so you assume the round will close, when your model shows $2M ARR by December and nobody questions it — that's when you're most exposed. Stress testing isn't pessimism. It's calibration. --- ## The Stress-Test Methodology ### Step 1: Isolate the Assumption State it explicitly. Not "our market is large" but "the total addressable market for B2B spend management software in German SMEs is €2.3B." The more specific the assumption, the more testable it is. Vague assumptions are unfalsifiable — and therefore useless. **Common assumption types:** - **Market size** — TAM, SAM, SOM; growth rate; customer segments - **Customer behavior** — willingness to pay, churn, expansion, referrals - **Revenue model** — conversion rates, deal size, sales cycle, CAC - **Competitive position** — moat durability, competitor response speed, switching cost - **Execution** — team velocity, hire timeline, product timeline, operational scaling - **Macro** — regulatory environment, economic conditions, technology availability ### Step 2: Find the Counter-Evidence For every assumption, actively search for evidence that it's wrong. Ask: - Who has tried this and failed? - What data contradicts this assumption? - What does the bear case look like? - If a smart skeptic was looking at this, what would they point to? - What's the base rate for assumptions like this? **Sources of counter-evidence:** - Comparable companies that failed in adjacent markets - Customer churn data from similar businesses - Historical accuracy of similar forecasts - Industry reports with conflicting data - What competitors who tried this found The goal isn't to find a reason to stop — it's to surface what you don't know. ### Step 3: Model the Downside Most plans model the base case and the upside. Stress testing means modeling the downside explicitly. **For quantitative assumptions (revenue, growth, conversion):** | Scenario | Assumption Value | Probability | Impact | |----------|-----------------|-------------|--------| | Base case | [Original value] | ? | | | Bear case | -30% | ? | | | Stress case | -50% | ? | | | Catastrophic | -80% | ? | | Key question at each level: **Does the business survive? Does the plan make sense?** **For qualitative assumptions (moat, product-market fit, team capability):** - What's the earliest signal this assumption is wrong? - How long would it take you to notice? - What happens between when it breaks and when you detect it? ### Step 4: Calculate Sensitivity Some assumptions matter more than others. Sensitivity analysis answers: **if this one assumption changes, how much does the outcome change?** Example: - If CAC doubles, how does that change runway? - If churn goes from 5% to 10%, how does that change NRR in 24 months? - If the deal cycle is 6 months instead of 3, how does that affect Q3 revenue? High sensitivity = the assumption is a key lever. Wrong = big problem. ### Step 5: Propose the Hedge For every high-risk assumption, there should be a hedge: - **Validation hedge** — test it before betting on it (pilot, customer conversation, small experiment) - **Contingency hedge** — if it's wrong, what's plan B? - **Early warning hedge** — what's the leading indicator that would tell you it's breaking before it's too late to act? --- ## Stress Test Patterns by Assumption Type ### Revenue Projections **Common failures:** - Bottom-up model assumes 100% of pipeline converts - Doesn't account for deal slippage, churn, seasonality - New channel assumed to work before tested at scale **Stress questions:** - What's your actual historical win rate on pipeline? - If your top 3 deals slip to next quarter, what happens to the number? - What's the model look like if your new sales rep takes 4 months to ramp, not 2? - If expansion revenue doesn't materialize, what's the growth rate? **Test:** Build the revenue model from historical win rates, not hoped-for ones. ### Market Size **Common failures:** - TAM calculated top-down from industry reports without bottoms-up validation - Conflating total market with serviceable market - Assuming 100% of SAM is reachable **Stress questions:** - How many companies in your ICP actually exist and can you name them? - What's your serviceable obtainable market in year 1-3? - What percentage of your ICP is currently spending on any solution to this problem? - What does "winning" look like and what market share does that require? **Test:** Build a list of target accounts. Count them. Multiply by ACV. That's your SAM. ### Competitive Moat **Common failures:** - Moat is technology advantage that can be built in 6 months - Network effects that haven't yet materialized - Data advantage that requires scale you don't have **Stress questions:** - If a well-funded competitor copied your best feature in 90 days, what do customers do? - What's your retention rate among customers who have tried alternatives? - Is the moat real today or theoretical at scale? - What would it cost a competitor to reach feature parity? **Test:** Ask churned customers why they left and whether a competitor could have kept them. ### Hiring Plan **Common failures:** - Time-to-hire assumes standard recruiting cycle, not current market - Ramp time not modeled (3-6 months before full productivity) - Key hire dependency: plan only works if specific person is hired **Stress questions:** - What happens if the VP Sales hire takes 5 months, not 2? - What does execution look like if you only hire 70% of planned headcount? - Which single person, if they left tomorrow, would most damage the plan? - Is the plan achievable with current team if hiring freezes? **Test:** Model the plan with 0 net new hires. What still works? ### Competitive Response **Common failures:** - Assumes incumbents won't respond (they will if you're winning) - Underestimates speed of response - Doesn't model resource asymmetry **Stress questions:** - If the market leader copies your product in 6 months, how does pricing change? - What's your response if a competitor raises $30M to attack your space? - Which of your customers have vendor relationships with your competitors? --- ## The Stress Test Output ``` ASSUMPTION: [Exact statement] SOURCE: [Where this came from — model, investor pitch, team gut feel] COUNTER-EVIDENCE • [Specific evidence that challenges this assumption] • [Comparable failure case] • [Data point that contradicts the assumption] DOWNSIDE MODEL • Bear case (-30%): [Impact on plan] • Stress case (-50%): [Impact on plan] • Catastrophic (-80%): [Impact on plan — does the business survive?] SENSITIVITY This assumption has [HIGH / MEDIUM / LOW] sensitivity. A 10% change → [X] change in outcome. HEDGE • Validation: [How to test this before betting on it] • Contingency: [Plan B if it's wrong] • Early warning: [Leading indicator to watch — and at what threshold to act] ```