This is the kind of a broad term where the searcher isn’t researching a specific aspect of the process, but rather researching the entire process and looking for the steps. You see, your “ultimate guide” may rank for the more generic terms like “how to buy vinyl”. Alas, for the time being, it’s unlikely that it will. The basic idea behind the new-school approach is that you shouldn’t be worrying about keywords at all - instead, you should build a comprehensive, original, high-quality resource, and Google will figure out the rest. The new-school, topic-based approach implies creating a single page that covers all these topics, aka “The ultimate guide to buying vinyl”. More likely than not, you’ll be outranked by competitors who offer more comprehensive answers. The problem with this is that in 2017, this kind of content will hardly ever be considered comprehensive, or even remotely useful, particularly in competitive niches. If you took the old-school approach, you’d come up with tens of similar pages: a separate page (even if it’s just a few sentences long) for each of the queries above. You’ve researched these questions, and apparently, the most common ones look like this: To see how these approaches may work, imagine you’ve got a website where you sell second-hand vinyl records, and you’re looking to write a bunch of blog articles that answer your target audience’s questions. The very first question you should be asking yourself when you think of embracing semantic SEO is this: How do I build my content? Should I (a) create pages around individual keywords, or (b) focus on broad topics and cover them in-depth?įrom the SEO perspective, these are the two (rather marginal) approaches to creating content today: (a) is the old-school way that you’re probably used to, and (b) is the new-school approach that’s becoming increasingly popular with the rise of semantic search. There are many aspects to pay attention to if you’re looking to embrace semantic search, from choosing what to focus your pages on to researching keywords and topics and improving relevance. Google’s recently said that RankBrain is “involved in every query”, and affects the actual rankings “probably not in every query but in a lot of queries”. As a result, the pages that are deemed to be good responses to the query may not even contain the exact words from the query, but are nonetheless relevant. Such “features” are determined by analyzing the best-performing search results (according to Google’s user satisfaction metrics, such as SERP click-through rate, pogo-sticking, time on page, etc.) and looking for similarities between these pages. RankBrain’s ranking component analyzes the pages in Google’s index and looks for specific features (e.g., usage of certain related terms) that make those pages a good fit for the query. For the former, RankBrain attempts to better interpret queries (particularly the rare or unfamiliar long-tails) by associating them with more common queries, so that it can provide better search results in response. RankBrain is a machine learning system that includes two components: the query analysis part and the ranking part. Its purpose is similar to that of Hummingbird, but the mechanics behind it are different. RankBrain (launched in October 2015) forms part of Google’s Hummingbird algorithm. Hummingbird uses context and searcher intent (as opposed to individual keywords in a query) to ensure that “pages matching the meaning do better, rather than pages matching just a few words”. It all started with Google’s Hummingbird update back in 2013. Semantic search aims to improve search accuracy by understanding searcher intent, contextual meaning of terms, and relationships between words to provide more relevant search results. We’ve heard about entity SEO, conversational content, optimizing for topics (as opposed to keywords), and even completely ditching old-school SEO tactics, like link building and keyword targeting, in favor of creating the most relevant and useful piece of content there is, and letting Google do the rest.īut is Google really giving up on keywords, and should SEOs do the same? What exactly does “optimizing for relevance” mean, how do you do it, and can you rely on it alone? How, after all, does semantic search work, and where do you get started? This article is an attempt to answer these questions. Semantic search has sparked a lot of buzz in the SEO space. On-Page SEO in: How to Optimize for RankBrain and Semantic Search
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