/stress-test

name: "stress-test"
description: "Stress-test a plan built on untested assumptions. Use whenever the user bets on a hockey-stick model or long pilot."

/executive-mentor:stress-test — Business Assumption Stress Testing

**Command:** `/executive-mentor:stress-test <assumption>`

Take any business assumption and break it before the market does. Revenue projections. Market size. Competitive moat. Hiring velocity. Customer retention.

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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.

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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?

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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 Pl