How marketers can adapt to LLM-powered search

Large language models (LLMs) provide a search experience that’s dramatically different from the web-browser experience

LLMs provide direct answers to queries, rather than the links found in traditional web browsers. Customers are increasingly turning to apps like ChatGPT or Perplexity, or search platforms like Google's AI Overviews or Bing's Copilot, to discover products and brands through natural language responses. This consultative and conversational approach is creating a new information channel that marketers must monitor to ensure their brands are accurately represented and featured in relevant queries.

Key takeaways

• Marketers need to track if and when their brands appear in LLM results, evaluate the favorability of their representation and any associated negatives, and compare their products' visibility to competitors in highly relevant queries;
• Just as search engine optimization (SEO) emerged in the era of browser-based search, a new field of LLM optimization (LLMO) is emerging, and marketers need to capitalize on it;
• LLM algorithms are different from traditional search algorithms. For example, Google SGE uses a unique set of ranking factors. Traditional search algorithms prioritize authoritative, comprehensive, and relevant links, while LLMs excel at quickly composing accurate and engaging answers by integrating multiple content types and modalities.

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