The Future of Semantic Search and What It Means for Content Marketing
Content marketing seems to have stabilized over the past few years. Originally, as part of an SEO strategy, content was solely intended to be a means of holding keywords. Search marketers would optimize keyword-stuffed articles in the hopes of getting enough presence to land a high ranking for that particular keyword. As search algorithms evolved to detect such black-hat practices, search marketers retracted their keywords to comprise only two to five percent of their total content. The popularity of quality content marketing skyrocketed, flooding the market with gimmicky sites and entrepreneurs blindly writing large volumes of content to join the bandwagon.
Today, content marketing is still extremely valuable, though the significance of specific keyword inclusion has dwindled to almost nothing. It has also diminished in hype, allowing it to mature from a popular buzzword to a familiar strategy that almost every website uses.
Content marketing is a constant at this point. Even though search engine algorithms are still updating regularly, putting a new focus on advanced elements like HTTPS encryption and site speed, content marketing is and will always be valuable because it provides information to users. The elements that make “good” content are consistent, regardless of technical search engine changes, so it’s somewhat surprising that a new direction could one day shape the way we think about content.
A concept known as “semantic search” is starting to emerge, though it’s currently in the early stages of development. Semantic search would look at the intentions and meaning of the words a user enters into a search engine, rather than the words themselves. For example, in a traditional search of “how do I make a grilled cheese sandwich”, a search engine would pick out keywords like “how,” “make,” and “grilled cheese sandwich,” then find content that contains those words. A semantic search of the same query would look at the user’s intention—producing a grilled cheese sandwich, probably at home—and display results that allow the user to achieve that goal. In the former case, you might get a handful of how-to articles and an encyclopedia entry for “grilled cheese sandwich.” In the latter case, you might get several how-to articles as well as options for cheese and bread.
Understanding the future of semantic search can allow you to take advantage of these intuitive results. Rather than optimizing for a specific keyword, content will one day optimize for a specific intention. If you can understand your demographics’ intentions and deliver them, you’ll easily rise to the top.
The Limits of Semantic Interpretation
Why isn’t semantic search in full force today? It’s due to the limitations of current technology. Algorithms, the complex series of processes that formulate search results, are mathematical processes. Everything is based in numbers, logic, and “yes or no” responses. Understanding the meaning behind a written phrase cannot be reduced down to a simple logical interpretation—as anybody who’s misread the tone of a sarcastic email can attest.
But at the same time, it’s clear that almost anything is possible with technology, given enough time and resources. Watson, the sophisticated algorithm-based supercomputer that outperformed previous champions on television game show Jeopardy!, was successful because its programmers allowed it to adapt to new information and analyze the intent behind otherwise indecipherable meanings—such as pun-based wordplay or contextual clues.
This same technology is already starting to emerge in Google’s search algorithms, and it’s only a matter of time before they become complicated enough to turn the world of SEO on its head.
Hummingbird and Conversational Search
The Hummingbird update, back in 2013, was not as significant as the Panda and Penguin updates of 2011 and 2012, respectively. But it did add one new feature that marks the beginning of the semantic search trend: conversational search. According to Google, “conversational searches” are very popular, and they’re trying to increase the relevance of results that are produced from this type of query.
For example, if you type in an ambiguous question like “where is the best place for chicken?”, older algorithms would focus on certain keywords like “best” and “chicken” and blindly compile results that contained those keywords. A conversational algorithm, like the one deployed with Hummingbird, would instead focus on the meaning behind each individual word in the sentence, even “where.” Hummingbird would determine that “chicken” could mean either live chicken or chicken meat, and would determine that chicken meat is the likely intention, since few people look for locations where live chickens can be bought. Hummingbird might also detect that since the searcher is looking for the “best place” for chicken, he/she is also likely looking for a restaurant, rather than a grocery store, and would therefore compile a list of well-reviewed restaurants in the area that serve chicken.
This is a speculative example, of course, but it’s evidence that semantic interpretation is already emerging. It’s in a basic and alterable form, but the gears are starting to turn.
The Future of Semantic Search
Google is already quite impressive. It’s able to reasonably guess the meaning behind your given search phrase, even with the elementary Hummingbird update. But the future of semantic search will likely extend far beyond the current limits of algorithm technology.
Already, Google is beginning to incorporate various external factors into its search results, based on your own personal data. It might creep you out to learn that this is happening, but it’s also giving you much more relevant results. Google likely knows exactly where you live, and can use your previous search history to customize predictive search results.
If we take those factors and incorporate them into an environment that is built on semantic search, we end up with a search engine that can guess users’ intentions based on their previous behavior—maybe even before they search for it. By using big data to analyze and interpret patterns of behavior based on individuality, time of day, social media activity, and even recent news, Google could take the world of search into a direction previously limited to science fiction. We’re likely a decade or more away from building a machine that can accurately guess what you’re thinking, but knowing Google, we’re probably already closer than you think.
How to Adjust Your Content Marketing Strategy
In some ways your content marketing strategy shouldn’t change. Presently, subject-focused content strategies tend to pay off. Writing about a given topic will naturally attract people searching for keywords related to that topic. It’s all about giving people what they’re looking for, and that fundamental principle will remain firm.
However, in order to adapt to the surely-coming revolution of semantic search, you need to go a step further. You need to understand the meaning behind why people are searching for a given topic. It’s a fancy way of saying you need to understand your demographics better, through surveys, studies, and big data analysis. Understand exactly what motivates your customers to search for a given topic, and extend your content strategy to cover those peripheral motivators.
Doing so will put you ahead of the search engines—Google will attempt to understand what’s motivating your customer, but you’ll already know. And if you can provide that to them with relevance, uniqueness, and quality, Google will reward you with a high rank.
Want more information on content marketing? Head over to our comprehensive guide on content marketing here: The All-in-One Guide to Planning and Launching a Content Marketing Strategy.
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