business-model-math-validation

📁 samarv/shanon 📅 4 days ago
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npx skills add https://github.com/samarv/shanon --skill business-model-math-validation

Agent 安装分布

amp 2
opencode 2
kimi-cli 2
codex 2
github-copilot 2
claude-code 2

Skill 文档

Business Model Math Validation

This skill moves beyond product “art” (UX, delight, features) to analyze the underlying “math formula” that determines business survival. It ensures that product leaders don’t spend years polishing a solution that is structurally incapable of returning a profit.

The Core Principle: The Business as an Equation

Every company is an equation where you pour investment into one side and get returns out the other. If the foundational equation is broken, no amount of product iteration, elegant design, or high-quality execution can save it.

The Validation Process

1. Identify the Foundational Equation

Define the relationship between your primary costs and your revenue drivers.

  • Content/Inventory Cost: Is the cost to produce or acquire the “value” fixed or variable? (e.g., Quibi’s $2B bespoke content vs. YouTube’s user-generated content).
  • Acquisition Strategy: Is the growth organic/word-of-mouth or dependent on paid acquisition (CAC)?
  • Retention Requirement: What is the “break-even” retention rate required to recoup the cost of the initial investment?

2. Isolate “Bet-the-Company” Variables

Identify the 2-3 variables that must be true for the math to work. Compare these to industry benchmarks.

  • Distribution Assumption: Does the model require you to stay in the App Store Top 10 indefinitely?
  • Format Assumption: Does the user behavior (e.g., “on-the-go” viewing) actually exist at the scale the math requires?
  • Margin Assumption: Can the product survive if the cost of the “value” (e.g., music royalties or shipping dog food) is dictated by a third party?

3. Determine the “Time to Iterate”

Calculate how much runway the cost structure allows for product-market fit discovery.

  • High-Burn/High-Stake: If you spend $1B/year on content or inventory, you cannot afford a year of “grinding on the funnel.” The product must be a hit on day one.
  • Capital-Efficient: If growth is organic and costs are low (e.g., Zero or early Pandora), you have the “mathematical permission” to iterate on the funnel for years.

4. Build a Full-Funnel Macro Model

Instead of looking at “leaf node” metrics (like button click-through rates), build a spreadsheet that connects:

  • Top of Funnel: Organic installs + Paid installs.
  • Conversion: Registration % -> Trial % -> Paid Subscriber %.
  • LTV: Average revenue per user over 12–24 months.
  • Variable Costs: Server costs, royalties, customer support.

Examples

Example 1: High-Stakes Media (The Quibi Scenario)

  • Context: Evaluating a startup that produces high-end vertical video.
  • The Math: To recoup $2B in content spend, the product requires 20M+ subscribers and Top 10 App Store status from Month 1.
  • The Risk: The “math formula” does not allow for a 2-year iteration cycle. If the Top 10 assumption is “highly improbable” based on history, the product is mathematically doomed regardless of its quality.

Example 2: Organic Subscription App (The Zero Scenario)

  • Context: A health app focusing on fasting or metabolic health.
  • The Math: 0% spent on paid acquisition. Growth is driven by word-of-mouth (organic).
  • The Opportunity: Because CAC is effectively zero, the “math formula” allows the team to focus entirely on LTV expansion and funnel optimization. Every marginal improvement in conversion is pure profit.

Common Pitfalls

  • Mistaking Art for Business: Thinking that because a product is “beautiful” or “delightful,” the math will eventually sort itself out.
  • The “Top 10” Delusion: Assuming your app will naturally land and stay in the Top 10 of the App Store without a sustainable, non-paid distribution engine.
  • Underestimating Third-Party Leverage: Building a business where the “math” is controlled by a statutory license or a third-party vendor (e.g., record labels) that can change the terms.
  • Iterating on a Structural Problem: Trying to “optimize the funnel” of a business that is losing money on every single transaction (e.g., early e-commerce shipping heavy items at low margins).