What Does AI Think of Your Brand vs. Competitors? This Prompt Shows You… | Orbit Media Studios

Source
ai-searchgeogenerative-engine-optimizationcompetitive-analysisbrand-positioningconversion-optimizationai-recommendations

Summary

Andy Crestodina presents a practical framework for auditing how AI models perceive your brand versus competitors, using specific prompts that reveal training data biases and recommendation patterns. The key distinction is between AI visibility (being mentioned) and AI recommendations (being suggested as the best option), with the latter driven more by conversion copywriting than traditional SEO. The article provides copy-paste-ready prompts for competitive audits, buyer-persona-based analysis, and predicting how prospects actually phrase their AI research queries.

Key Insight

  • The critical distinction is AI visibility vs. AI recommendations. Being listed is table stakes; being recommended first is where leads come from. Traditional SEO gets you visible, but conversion optimization (trust signals, proof, specifics) is what gets you recommended.
  • AI prompts from buyers are fundamentally different from search queries — they are longer, more contextual, and unknowable through keyword research. There is no “search volume” or “keyphrase difficulty” for prompts.
  • Four prompt types trigger AI to search the web (RAG) rather than rely on pre-training: requests for specifics, time-sensitive info, source-backed recommendations, and high-stakes decisions. For these, traditional SEO still matters heavily.
  • The “buyer-specific competitive analysis prompt” is the most valuable: it forces you to define your buyer persona in detail, then reveals which brands AI favours for that specific buyer’s likely research patterns.
  • Future B2B research will involve AI agents building custom buyer guides automatically (e.g. Genspark “Super Agent” mode), reducing brand control over the research experience even further.
  • The 40-prompt prediction exercise produces a reusable asset for messaging audits, content strategy, paid media targeting, and evidence gap analysis.