7-Step Challenger Brand AI Search Protocol

LLM Optimisation | 5 min read | Published:

By , Founder of The Lmo7 Agency

If a challenger brand asked us how to transform their visibility in LLMs, then this is what we would do.

**1. Listen to Real Customers** Scrape support emails, DMs, reviews, Reddit, and community threads. Search Perplexity for questions like: “Is [your product] better than [big brand]?” “Best [category] for sensitive skin / travel / teens?” Document actual buyer phrases, not just keyword terms. **2. Cluster by Buying Intent** Group findings into 5–10 use-case clusters, such as: “Compare vs mainstream brands” “Longevity or safety questions" “First-time buyer hesitations” Don’t optimise for keywords — optimise for buyer context. **3. Run LLM Simulations** Ask real buyer queries in: ChatGPT Gemini Perplexity Track: Does your brand show? Which content types (e.g., blog, review, Reddit) are LLMs surfacing? Who’s dominating — and why? **4. Patch the Gaps Fast** For each top topic, decide: Update: a stale landing page or PDP Create: a comparison page or explainer Place: a mention on a trusted 3rd-party source (Reddit, blog, roundup) **5. Optimise for AI Pickup** Use tools like Surfer SEO or AirOps. Focus on: Conversational phrasing Embedded questions and FAQs Clear trust signals (reviews, certifications, founder story) **6. Track AI Mentions and Traffic** Use: JellyFish Share of Model AI for LLM brand presence GA4 for ChatGPT/Perplexity referrers Focus on what converts, not just what’s seen. **7. Earn Smart Citations** Seed brand mentions where LLMs pull data: Niche blogs Reddit threads Review platforms Product directories Add schema.org markup for bonus discoverability.

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