This means that featured snippets have an impact on voice search — bad snippets, or no snippets at all, and digital assistants struggle. From typing to talking Back when dinosaurs roamed the earth and queries were typed into search engines via keyboards, people adapted to search engines by adjusting how they performed queries. We pulled out unnecessary words and phrases, like “the,” “of,” and, well, “and,” which created truncated requests — robotic-sounding searches for a robotic search engine. Digital assistants and machine learning By looking at how digital assistants do their voice search thing (what we say versus what they search), we can see just how far machine learning has come with natural language processing and how far it still has to go (robots, they’re just like us!). For example, when we asked our Google Assistant, “What are the best headphones for $100,” it queried [best headphones for $100]. Snippets are appearing for different kinds of queries So, what are we to make of all of this? This means that if we want to snag more snippets and help searchers using digital assistants, we need to build out long-tail, natural-sounding keyword lists to track and optimize for. Format your snippet content to match When it’s finally time to optimize, one of the best ways to get your content into the ears of a searcher is through the right snippet formatting, which is a lesson we can learn from Google. Get tracking You could be the Wonder Woman of meta descriptions, but if you aren’t optimizing for the right kind of snippets, then your content’s going to have a harder time getting heard. Want to learn how you can do that in STAT?
This post was originally published on the STAT blog.
We all know that featured snippets provide easy-to-read, authoritative answers and that digital assistants love to say them out loud when asked questions.
This means that featured snippets have an impact on voice search — bad snippets, or no snippets at all, and digital assistants struggle. By that logic: Create a lot of awesome snippets and win the voice search race. Right?
Right, but there’s actually a far more interesting angle to examine — one that will help you nab more snippets and optimize for voice search at the same time. In order to explore this, we need to make like Doctor Who and go back in time.
From typing to talking
Back when dinosaurs roamed the earth and queries were typed into search engines via keyboards, people adapted to search engines by adjusting how they performed queries. We pulled out unnecessary words and phrases, like “the,” “of,” and, well, “and,” which created truncated requests — robotic-sounding searches for a robotic search engine.
Of course, as search engines have evolved, so too has their ability to understand natural language patterns and the intent behind queries. Google’s 2013 Hummingbird update helped pave the way for such evolution. This algorithm rejigging allowed Google’s search engine to better understand the whole of a query, moving it away from keyword matching to conversation having.
This is good news if you’re a human person: We have a harder time changing the way we speak than the way we write. It’s even greater news for digital assistants, because voice search only works if search engines can interpret human speech and engage in chitchat.
Digital assistants and machine learning
By looking at how digital assistants do their voice search thing (what we say versus what they search), we can see just how far machine learning has come with natural language processing and how far it still has to go (robots, they’re just like us!). We can also get a sense of the kinds of queries we need to be tracking if voice search is on the SEO agenda.
For example, when we asked our Google Assistant, “What are the best headphones for $100,” it queried [best headphones for $100]. We followed that by asking, “What about wireless,” and it searched [best wireless headphones for $100]. And then we remembered that we’re in Canada, so we followed that with, “I meant $100 Canadian,” and it performed a search for [best wireless headphones for $100 Canadian].
We can learn two things from this successful tête-à-tête: Not only does our Google Assistant manage to construct mostly full-sentence queries out of our mostly full-sentence asks, but it’s able to accurately link together topical queries. Despite us dropping our subject altogether by the end, Google Assistant still knows what we’re talking about.
Of course, we’re not above pointing out…
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