The New Challenges of Organic Promotion in the Shift from Search Engines to Language Models

Recent developments in the search landscape reveal fascinating trends. The use of AI-powered chatbots for “search” purposes has increased by 37%, while traditional search engine usage has dropped by 11%.* These numbers highlight a clear shift in user behavior, prompting marketers to reconsider their strategies. Is it time to rethink the entire concept?

What is LMO, and How Does It Differ from SEO?

Language Model Optimization (LMO) is a new term describing the process by which brands and businesses strive to rank higher in the responses provided by large language models (LLMs) like GPT or Claude. It is akin to SEO, the optimization process we’re accustomed to with traditional search engines, particularly Google, which still accounts for 91% of global searches.

SEO primarily focuses on understanding Google’s algorithm and tailoring content to rank higher in search results by employing keywords, hierarchical content structuring, internal and external linking, and other criteria that evolve over time. LMO, however, revolves around understanding how language models interpret, analyze, and prioritize content to deliver high-quality, relevant answers to users.

What Should Brands Do to Stay Relevant in This New Search Era?

To succeed with language models, brands need to focus on producing high quality, accurate, and authentic content based on reliable sources. This content should directly address common user questions. Traditional keyword research must evolve into FAQ (frequently asked questions) research. With AI-driven search, queries are becoming more specific and detailed. Instead of focusing on long-tail keywords (three to five words), brands must now cater to ultra long tail queries entire sentences or even paragraphs.

Moreover, content must be well structured, easy to read, and organized to align with how users formulate their increasingly nuanced queries.

Can Content Flooding Influence Language Models?

Language models often prioritize frequently recurring sequences. This introduces a significant challenge: content flooding. Linked to the concept of Retrieval Augmented Generation (RAG), content flooding involves repeatedly injecting similar content into prompts or training datasets to dominate specific categories.

This tactic could allow brands to monopolize certain search categories by pushing their competitors into the margins. However, it risks creating biased responses and undermining the credibility and authenticity of language model results.

The Philosophical Dilemma: How Will Language Models Treat AI-Generated Content?

This raises a critical question: can language models recognize content they themselves generated? If so, will they rank such content as less authentic? Moreover, could models “penalize” websites overloaded with AI-generated material? This creates a chicken-and-egg problem, raising concerns about how to maintain content visibility while avoiding the pitfalls of over-reliance on AI-generated material.

Conclusion

While SEO is far from dead Google still dominates the search landscape the rise of LMO is inevitable for brands that want to stay relevant. Understanding and adopting LMO practices will be critical to ensuring your brand’s visibility as a default or recommended option in language model responses.

• Sources for Statistical Data: HubSpot State of Consumer Trends Report, January 2024. EarthWeb 2024 Report