By Clark Boyd
The combination of semantics (the science of meaning in language) with search engines that process billions of queries seems a very natural one.
Semantic search has been effective, too; by understanding the intent of a query and the context of the user, the accuracy of results on search engines like Google and Bing has increased significantly.
Search engine results pages today look markedly different to their earlier iterations and, with improvements in local search, voice recognition, and machine learning, they will continue to change over the next few years too.
There is a lot of fascinating theory behind all of this, but we can sometimes focus on this to the detriment of our work today.
Significant algorithm updates like Hummingbird, or the more recent launch of RankBrain, have a big impact on users. As marketers, we need to know exactly what this means for our strategy, our expectations, and our campaign measurement.
As such, this article will focus on some real-world examples of semantic search and provide a practical framework to help marketers avail of the opportunities it brings.
Semantic search in action
Let’s start with a simple example to shed light on how semantic search works. We’ll use a common, everyday search query like [will smith]. This screenshot is what I see above the fold on desktop:
When Google processes this query, it recognizes instantaneously that I am searching for the actor and all-round entertainer Will Smith, but also that the intent of my search is unclear. Therefore, it serves a varied array of options for me to click on. I may want to read news about the Fresh Prince, I may want to see his filmography, I may want to see if he has any new albums in the pipeline. Perhaps I want to see all three.
As is highlighted on the right-hand side in the knowledge panel, Google can retrieve all of this information from its index of 808,000,000 Will Smith-related results, but also from its own vast database of information about noteworthy people and institutions.
I can help Google out here by refining my search. Next, I ask [who is he married to]:
As we can see, results are pulled to the top of the results to highlight his current and former spouse.
This is a demonstration of conversational search in action.
Just like a person would in a conversation, Google knows the ‘he’ in my question refers to Will Smith. I don’t need to state this again. Google also needs to know what the connection is between ‘he’ and both Jada Pinkett Smith and Sheree Zampino.
These may seem like minor changes, but they hint at a fundamental shift in how Google works. Factor in voice search and it is easy to see how important this conversational element is.
If we extend this out to ask about Will Smith’s music, we can start to conceptualize just how complex Google’s network of interconnected entities is:
Asking what an artist’s best song is strays into the realm of subjectivity, so Google pulls the track listing from Will Smith’s greatest hits. Or at least, I hope that’s what’s happening here. If Google genuinely thinks ‘Girls Ain’t Nothing But Trouble’ is Will Smith’s best song, I’ll lose faith in them.
In terms of natural language processing, however, this search query is now quite convoluted. In this last instance, Google has had to keep track of who we’re asking about, having deviated once already to ask who his spouse is; then pull an indirect, best-fit answer to my question about Will Smith’s best song.
Let’s try one more, then we’ll give Google a break:
You get the idea.
We’ve come an awfully long way from the exact keyword matching of just a few years ago.
Furthermore, all of this serves an important illustrative purpose and it’s one that matters for anyone that wants to rank via SEO in 2017.
Why does it matter for brands?
The technology that underpins the above answers is utilized for all queries, so it is very significant for brands. Just launching a page on a website and ‘optimizing it for SEO’ clearly isn’t going to cut it any more.
Let’s say, for argument’s sake, that I run a peanut butter e-commerce site. Logic dictates that I will want to rank first in organic search for [peanut butter]. The results from my location look like this:
We can see the same principle applied to the earlier Will Smith query, but with very different results – both in their format and their content.
I may want to rank for [peanut butter] with my e-commerce site, but unless I have a physical store I can use to rank via local listings, the chances look slim. There are a few organic results above the fold (an anomaly these days), but only one brand that actually produces the product. There is a recipe with an accompanying image, however, and a link to more images, so perhaps these formats would be a more appropriate, achievable way to get onto page one.
At the bottom of this search results page, Google actually provides some strong clues about what people are really looking for when they search for peanut butter:
These related searches are more specific and give us a good idea of which topics we should cover on our site. There is a nice variety of different topics here, all of which are worthy of more investigation.
To pick one, we’ll go with the ‘peanut butter ingredients’ route. If I search for [what is in peanut butter?], Google serves the following results:
We can already sense some opportunities for an e-commerce site either to branch out is content strategy …read more
Source:: Search Engine Watch – SEO