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SEO looks a lot different today than it did in the past. Several years ago, keyword stuffing was commonplace, as people tried to game the system in order to get on the SERP.
Instead of providing valuable content, which is the aim of content marketing today, people used the same words over and over again. In fact, Google search was treated more like a print magazine or a phone book than a search engine. All of that is different now.
Semantic search is an integral and increasingly important component of SEO. It seeks to improve search accuracy by understanding searcher intent and the contextual meaning of terms.
When we talk about understanding searcher intent, we mean that instead of pushing the information we like best, it is better to answer questions that searchers are actually looking for. The hope is that along the way, these users will gain an interest in our brand, product or service. As a marketer, it is important to fully understand the meaning of the keywords we use and carefully choose our content.
This branch of linguistics essentially strives to comprehend natural language like the human brain. Human language is unique because it uses figurative speech, idioms, prepositions, contextual meaning, and abstract concepts. Machine learning has a long way to go before it fully understands how humans speak to each other, which is why we want to match the sort of language searchers are using. Luckily, the more often people search for relevant terms and click on the best matches, the easier it becomes for machine learning to do its job.
The Knowledge Graph was Google’s first attempt with natural language. It sought to prioritize context over keywords, which the company described as “things, not strings.” This was the beginning of changes to the algorithm. It amassed 570 million concepts and relationships to create a comprehensive ontological graph. This even synthesized various languages so that the system could understand a question in another language and translate it to English if the query demanded such.
The next stage of Google’s semantic search came in 2013 when the company introduced Hummingbird to further address the keyword stuffing issue. This initiative changed the algorithm by ranking pages that match meaning higher than those that match for keywords. A couple of years after Hummingbird, Google introduced Rankbrain. This update was much like its predecessor in terms of searcher intent analysis, except Rankbrain also employed machine learning.
Why are we all talking about voice search lately? Like all new technology, voice search has reached a high level of functionality. It can now recognize words despite tone, accent, and noise in the background. People find it useful because they can ask questions more quickly, even when they’re on-the-go or doing other things.
The popularity of voice has had a huge effect on the evolution of semantic search, as it contributes towards higher ranking. Unlike a normal query, verbally posed questions are longer form and get to the point right away. Because of this, voice search has increased the need for conversational, straightforward language in search engine optimization. Whether they be on a tablet, smartphone, smartwatch or digital home assistant, there are endless potential customers out there posing queries you can optimize for.
So how should we use semantic search in our marketing strategy? Here are eight tips to get you thinking.
This branch of linguistics makes it easier for users to ask the questions they want answered without having to think twice about altering their language. Google wants marketers to follow along and provide searchers with the best user experience. This is mutually beneficial for Google, searchers, and your brand. Search engines seek to maximize the delivery of useful information. Google wants to serve as a personal assistant or concierge, one that is professional and responsive. As marketers, we should seek to add value and contribute towards facilitating this productive query-result relationship online.
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