Monte Carlo simulations have been used for decades in finance, physics, and engineering to model uncertainty. Now, we're applying the same principles to understand AI search behavior.
How It Works
- Define Variables: : We identify the key factors that influence AI recommendations (price point, feature set, positioning, etc.)
- Generate Variations: : We create thousands of prompt contexts with controlled variable changes
- Run Simulations: : Each variation is tested across multiple LLM providers
- Analyze Results: : We identify patterns and thresholds in citation behavior
A Real Example
Consider a CRM startup competing with Salesforce and HubSpot. Traditional SEO would focus on keywords like "best CRM software." But LLMs don't process queries the same way.
Our simulations revealed that: - At price points below $50/user/month, the startup was cited 15% of the time - Above $100/user/month, citations dropped to 2% - Adding "AI-powered" to the positioning increased citations by 40%
This data is actionable. It tells you exactly what to adjust and how much.
Beyond Vanity Metrics
Unlike impressions or click-through rates, Decision Boundary data is precise. You know exactly where you stand and what it takes to move the needle.
That's the power of Generative Optimization.