{"id":19945,"date":"2025-08-22T07:00:00","date_gmt":"2025-08-22T11:00:00","guid":{"rendered":"https:\/\/ipullrank.com\/?p=19945"},"modified":"2025-08-27T09:01:36","modified_gmt":"2025-08-27T13:01:36","slug":"loreal-case-study-ai-search","status":"publish","type":"post","link":"https:\/\/ipullrank.com\/loreal-case-study-ai-search","title":{"rendered":"How Anyone Can Strengthen Visibility In LLMs: A L\u2019Or\u00e9al Case Study"},"content":{"rendered":"\t\t
\n\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Why dominating the space doesn\u2019t always mean dominating the AI answer<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Where do you learn about beauty products? Some might depend purely on YouTube, TikTok, Reddit, or\u2026ChatGPT?<\/span><\/p>

Marketers of the world’s largest brands conduct extensive research on their target audience. Beauty is purely visual. Despite your image and video channels driving traffic and visibility, brands can\u2019t ignore the emergence of AI Search platforms. If you\u2019re not monitoring and strategizing for AI Search, you\u2019re opening the door for competitors to establish a stronger foundation that could hurt your brand in the long run.<\/span><\/p>

As AI becomes the front door to information, brand visibility is being rewritten. It\u2019s no longer enough for a product to appear in search results or dominate social. In large language models, the rules have changed. Visibility now depends on how well a brand is represented within the structure of the LLM\u2019s knowledge \u2014not just that it\u2019s mentioned, but <\/span>also the topics it’s associated with<\/span><\/i>.<\/span><\/p>

Even brand giants have blind spots when it comes to AI.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

The Problem: You Might Be Present, But Not Relevant<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

When someone asks an LLM like ChatGPT about a topic, it doesn\u2019t just repeat what it finds on the top-ranking site. It builds an answer by drawing from a web of entities and concepts: mascara ingredients, application techniques, and product performance in humidity. If your brand hasn\u2019t clearly articulated its presence in those topic areas, it simply doesn\u2019t appear in the response.<\/span><\/p>

Some of this insight is captured by the LLMs in the training data (compiled over a long period), and some is often captured in the final mile through a search lookup, known as \u201cRetrieval Augmented Generation<\/a>\u201d or RAG for short. If you get picked up in the training data, you stand a much better chance of your brand getting a good airing than if you have to rely on the RAG alone to get seen.<\/span><\/p>

You could make the argument that Entity SEO has been a staple of a responsible Organic Search strategy for years, but it doesn\u2019t fit neatly into the various best practice blog posts that you\u2019ll see on the topic. But as search engines (and LLMs) increasingly depend on a semantic web driven by the relationships between words, concepts, and things, brands need more than a keyword strategy. They need an <\/span>entity strategy.<\/b> A way to understand how LLMs interpret them and where they\u2019re being outplayed by competitors consistently on their underlying topics.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

A Quick Example: L\u2019Or\u00e9al Paris vs. Fenty vs. Est\u00e9e Lauder<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

To explore how LLMs understand brands in specific product contexts, I ran a structured comparison across four leading models: Gemini, Sonar, ChatGPT, and Claude. Each was asked the same question three times, varying only the brand: <\/span>What do you know about [brand] in the context of mascara?<\/b> The brands tested were <\/span>L\u2019Or\u00e9al<\/b>, <\/span>Fenty<\/b>, and <\/span>Est\u00e9e Lauder<\/b>.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

From there, I broke down each response into a knowledge graph\u2014mapping out the key <\/span>entities<\/b> and <\/span>topics<\/b> mentioned. This allowed me to see how each brand is represented conceptually by the model, and where certain ideas or attributes are more (or less) likely to appear.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The above Knowledge Graph in table form:<\/span>
<\/span>
<\/span>A little context to explain the table below. By asking 5 LLMs what they know about Mascara in relation to three brands, we get 15 long-form responses. I go through every response, with the help of a tool like Waikay, to count every mention of every topic (by which we mean \u201centity\u201d for the purists amongst you), and we tabulate the number of occurrences.<\/span><\/p>

To highlight differences, I used:<\/span><\/p>