The search engine model forms a basis for a real-time environment that can analyze ranking depth or distance, utilize statistical gap analysis to determine exactly which part of the model your target page is weak in, and even give you automatic optimization tasks based on your team's capabilities. This gives you a positive expected value on every optimization you make.
Is fully customizable, allowing each user to mimic ANY target search engine environment (TSEE), like the US version of Google. They can upload their own metrics like revenue, conversions, and more to make the simulations as locally accurate as possible.
Is automatically self-calibrating. When the user first creates their search engine model, the algorithmic weights are run through a genetic program called "Particle Swarm Optimization". In it, each algorithmic weight becomes a particle in a swarm of other particles. This Particle Swarm Optimization process allows your search engine model to dynamically respond to the targeted search engine environment.
When Google changes, your search engine model changes.