LLM Optimisation

    7-Step Challenger Brand AI Search Protocol

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

    10 July 2025
    5 min read
    7-Step Challenger Brand AI Search Protocol
    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.

    Ready to Optimise Your Brand for AI?

    Let LMO7 help you improve your visibility in AI shopping assistants and LLM responses.