Are you paying attention to vectors yet? The future of SEO might depend on it.
Google recently updated its spam policy documentation. These policies cover common spam practices such as cloaking, doorway abuse, expired domain abuse, hacked content, link abuse, keyword stuffing, link spam, machine generated traffic, scaled content abuse, etc.
These updates reflect an increasing shift in SEO towards the use of vector-based models, signalling a move beyond traditional keyword matching.
As Google’s algorithms evolve, there’s a growing focus on the meaning and context of data, with vector search becoming central to determining relevancy.
We started working for a beauty products client in January 2023. Understanding how Google algo’s focus on contextually relevant searches, we realised that semantic relationships between topics and terms is essential.
Large Language Model Optimization (LLMO) techniques allowed us to tap into these semantic relationships. These techniques focus on optimising content for meaning and intent, rather than just keywords, allowing search engines to retrieve results based on semantic connections and deeper contextual relevance.
Overview of results
When we started in January 2023, the site ranked for 871 keywords, with 428 keywords sitting in the 51+ range and 285 in the 21-50 range. By October 2024:
- We witnessed remarkable growth, with a 123.17% increase in ranked keywords, across both informational and transactional queries. The content became more discoverable, with strong positioning for high-value terms.
- Additionally, there was a 395.66% increase in organic traffic during the same period. With the help of vector search optimization, the site attracted a wider audience, pulling in users from diverse, relevant searches, even those where the exact keywords weren’t present.
How did we do it?
We leveraged LLMO techniques and entity optimization. This enhanced vector search by ensuring that content was contextually relevant, aligning with how Google’s algorithms understand the relationships between topics.
- User intent analysis: Understanding the intent behind searches is crucial. We combined traditional user behaviour analysis with LLMO insights to tailor content that catered to the exact needs of potential customers.
- Content structuring: Focused on creating more contextually relevant and semantically rich content. This involved restructuring the existing content to target relevant topics, improving the connection between terms, and providing in-depth information about specific product categories.
- Keyword research and new content creation: Leveraging LLMO, we conducted an in-depth keyword analysis, targeting specific terms related to the website. This allowed us to create fresh content that was not only SEO-optimised but also contextually relevant.
- Vector search: By focusing on vectors, we improved the relevancy of the client’s content in the eyes of searchers and the search engines. This meant that even without specific keyword mentions, our content remained highly relevant based on the user’s search intent and contextual meaning.
- Schema markup and attributes: Leveraged schema markup to ensure that Google could understand the relationships between data points on the client’s site. By adding as many relevant attributes as possible to the content, we boosted visibility and relevance for semantic search. These included product details, categories, and specific features that helped the search engine understand the products offered and their context.
The key takeaway from this case study is the importance of moving beyond traditional keyword optimization strategies and embracing LLMO techniques. With Google’s shift towards vector search and greater attention to semantic relationships, SEO strategies need to adapt.
LLMO and vector search aren’t just the future of SEO—they’re delivering results today!
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