stress-test

---
name: stress-test
description: Use when validating business assumptions before commitment—revenue projections, market size, competitive moat, hiring plans, or customer behavior models.
category: C-Level Advisory
version: 1.0.0
tools: []
---

# Stress-Test — Business Assumption Stress Testing

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. But it becomes a liability when assumptions get inflated by the same optimism that got you started. **The most dangerous assumptions are the ones everyone agrees on.**

Stress testing isn't pessimism. It's calibration.

## The Stress-Test Methodology (5 Steps)

### 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 more testable. Vague assumptions are unfalsifiable.

**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 it's wrong.

Ask:
- Who has tried this and failed?
- What data contradicts this?
- What does the bear case look like?
- If a smart skeptic looked at this, what would they point to?
- What's the base rate for assumptions like this?

**Sources:** Comparable failures, customer churn data, historical forecast accuracy, industry reports, competitor findings.

### Step 3: Model the Downside

Most plans model base case and upside. Stress testing means modeling downside explicitly.

**For quantitative assumptions:**

| Scenario | Value | Impact |
|----------|-------|--------|
| Base case | [Original] | |
| Bear case | -30% | |
| Stress case | -50% | |
| Catastrophic | -80% | Does business survive? |

**For qualitative assumptions:**
- Earliest signal this assumption is wrong?
- How long to notice?
- What happens between when it breaks and when you detect it?

### Step 4: Calculate Sensitivity

Some assumptions matter more than others. **If this assumption changes, how much does the outcome change?**

Examples:
- If CAC doubles, how does runway change?
- If churn goes 5% → 10%, how does NRR change in 24 months?
- If deal cycle is 6 months instead of 3, how does Q3 revenue change?

High sensitivity = key lever. Wrong = big problem.

### Step 5: Propose the Hedge

For every high-risk assumption:

- **Validation hedge** — test it before betting (pilot, customer conversation, experiment)
- **Contingency hedge** — if it's wrong, what's plan B?
- **Early warning hedge** — leading indicator tells 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% pipeline converts
- Doesn't account for slippage, churn, seasonality
- New channel assumed to work before tested at scale

**Stress questions:**
- Actual historical win rate on pipeline?
- If top 3 deals slip to next quarter, what happens?
- If new sales rep takes 4 months to ramp, not 2?
- If expansion revenue doesn't materialize, what's growth rate?

**Test:** Build model from historical win rates, not hoped-for ones.

### Market Size

**Common failures:**
- TAM calculated top-down from reports without bottoms-up validation
- Conflating total market with serviceable market
- Assuming 100% of SAM is reachable

**Stress questions:**
- How many companies in ICP actually exist? Can you name them?
- Serviceable obtainable market in year 1-3?
- What percentage of ICP currently spends on any solution?
- What does "winning" look like; what market share required?

**Test:** Build list of target accounts. Count them. Multiply by ACV. That's your SAM.

### Competitive Moat

**Common failures:**
- Technology advantage can be built in 6 months
- Network effects haven't materialized
- Data advantage requires scale you don't have

**Stress questions:**
- If competitor copies best feature in 90 days, what do customers do?
- Retention rate among customers who tried alternatives?
- Is moat real today or theoretical at scale?
- Cost for 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 cycle, not current market
- Ramp time not modeled (3-6 months before full productivity)
- Key hire dependency: plan works only if specific person hired

**Stress questions:**
- If VP Sales hire takes 5 months, not 2?
- If you hire 70% of planned headcount, what executes?
- Which single person, if they left tomorrow, damages plan most?
- Achievable with current team if hiring freezes?

**Test:** Model 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 market leader copies product in 6 months, how does pricing change?
- If competitor raises $30M to attack your space?
- Which customers have vendor relationships with competitors?

---

## Output Format

```
ASSUMPTION: [Exact statement]
SOURCE: [Where from — model, pitch, gut feel]

COUNTER-EVIDENCE
• [Evidence that challenges]
• [Comparable failure]
• [Contradicting data point]

DOWNSIDE MODEL
• Bear case (-30%): [Impact]
• Stress case (-50%): [Impact]
• Catastrophic (-80%): [Impact — business survives?]

SENSITIVITY
This assumption has [HIGH / MEDIUM / LOW] sensitivity.
A 10% change → [X] change in outcome.

HEDGE
• Validation: [How to test before betting]
• Contingency: [Plan B if wrong]
• Early warning: [Leading indicator + threshold]
```