Voice search, also known as Conversational Search, is a new kind of human-machine interaction where devices can answer to human spoken questions. Conversational search analyzes a string of words and replies with responses which resemble our natural language.
Voice search is now integrated into many devices such as mobile phones, home assistants like Amazon Echo and Google Home, and cars which support Intelligent Agents.
Today this technology has four main players:
- Amazon, whose Alexa Voice Search uses Bing as a search engine
- Apple, whose Siri uses Bing as a search engine
- Google, whose Google Voice Search, of course, relies on its own algorithms
- Windows, whose Cortona uses Bing – which is also owned by Microsoft – as a search engine.
Voice search is a fast-growing technology which may change the way we use search engines and have an impact over their algorithms. In 2016, Google declared that 20% of mobile searches were conversational and according to ComScore, voice search will keep on growing, covering at least 50% of all searches by 2020.
What users need when using voice search is a quick and actionable answer: here is why, when it’s possible, intelligent agents try to give an immediate answer skipping the websites. The bright side is that when your content is structured and rich, your articles and pages may be displayed as the instant answers by Intelligent Agents giving to you a boost in CTR.
Moreover, voice searches are often longer and more contextualized than typed ones: with a keyboard, users try to be specific enough using as few words as possible, on the other hand, asking something to an intelligent agent users tend to indulge in details related to their context. Content writers should get ready to voice searches trying to be relevant to the long-tail of complex searches.
How can a website get ready for voice search?
Content structuring is the key. With semantic markup you can feed intelligent agents with unequivocal information readable as data, that can be used to reply to very specific questions. The more specific your content connotation is, the more you can avoid ambiguity for search engines. And since your data can be linked to trustable open datasets on the web, a huge amount of consistent occurrences validates your information.
In other words, when you organize your content with Schema.org markup, you enable machines to read and contextualize it, serving your pages and articles to the right reader at the right moment. This is the very purpose of the Semantic Web: being understandable by machines to make communication smoother for humans.
While Artificial Intelligence helps you structure your data to ensure machines can really understand your point, you need to focus on writing for humans. Forget about keywords and answer to longer – and voice friendly – questions: you can also use them as titles, as we did for this entity. Find your way to keep your content relevant to your readers and address concrete questions or concerns they might have.
As you may notice, you just have to go back to write better content.