Consumer Questions Convert to Clicks Twice as Often They Used To — Here’s Why

Convert to click

Due in part to the uncertainty of the past several months, customers are asking more questions than ever — which is hardly surprising. But what is noteworthy is that these consumer questions asked in searches convert to page clicks more than twice as often as they did back in 2018 — by a whopping 109%, according to recently compiled data on Yext Pages. 

This increase in traffic to businesses’ pages as a result of search queries is great news, but why is it happening? Why do questions turn into clicks so much more often than they did just two years ago?

The answer likely has something to do with the kinds of questions that often lead to clicks — and how search engines have learned to process them. 

The rise of long-tail, high-intent queries

 Understanding the phenomenon of “questions to clicks” depends on first understanding focused search intent. Longer, more complex questions asked using natural language — which AI has now made search engines able to answer — show a more focused user search intent. 

This makes sense when you break it out with an example: a customer searching for a “small charcoal grill that fits on a balcony” has a clear idea of what they want and they are likely closer to clicking on a result and making a purchase than someone searching for “grills” or “types of grills.” This is why research shows that longer, high-intent queries, which account for 25% of all searches, convert at 2.5x the rate of single-word searches, according to a report from Conductor. (Such searches are also 37% more likely to result in a click.) Research from Yext also confirms this relationship between query length and intent: an analysis of all Google search queries for Yext Pages clients in 2019 shows a strong relationship between the number of words in a query and the click-through rate for Yext Pages.

But the ability of search engines to actually answer these longer, natural language questions is a relatively new phenomenon. While people have long posed questions to Google and other search engines, getting the desired answer has historically been anything but seamless. The sticking point, of course, has always been the computer’s ability to understand human language; likewise for humans’ ability to submit queries in the language of 1s and 0s. Advances in natural language processing, however, have changed the game: computers now use AI to process human language delivered via either speech or text. In essence, NLP enables us to interact with machines pretty much the way we do with one another (up to a point, obviously, but a surprisingly sophisticated one).

 Key to that understanding of late is Google’s BERT (Bidirectional Encoder Representations from Transformers), a neural network-based technique for natural language processing pre-training. What that actually means is that because BERT prioritises natural language over keywords, Google is better able to decipher the context of words in search queries. Pandu Nayak, Google’s vice president of Search, illustrates BERT’s ability to understand the intent behind a search with the following example: “2019 brazil traveller to usa need a visa.” As he explains it, “The word ‘to’ and its relationship to the other words in the query are particularly important to understanding the meaning. It’s about a Brazilian travelling to the US, not the other way around. Previously, our algorithms wouldn’t understand the importance of this connection, and we returned results about US citizens travelling to Brazil. With BERT, Search is able to grasp this nuance and know that the very common word ‘to’ actually matters a lot here, and we can provide a much more relevant result for this query.”

 But there’s another reason detailed search is resulting in an increased number of clicks. Longer questions increasingly include long-tail keywords and long-tail keywords are less competitive to rank for — making them a place where businesses can rise above the noise. As this post explains, “It is much easier to rank for long-tail keywords than for more common keywords because fewer websites compete for high rankings in the result pages of Google. The longer (and more specific) search terms are, the easier it is to rank for the term. Because of the vastness of the internet, it is easier to find your audience for your particular niche. Focusing on a group of long-tail keywords will result in a great deal of traffic altogether.

 To illustrate: showing up as the only “e-commerce site that ranks for a long-tail keyword such as ‘softest cardigan with Neapolitan stripes’ could be more lucrative than trying to rank for ‘sweaters’ or even ‘women’s clothing,’” according to Forbes.

The growth of long-tail search paired with computers’ increasing comfort with human language in all its permutations equates to better, richer search results — and thus more clicks (and more sales.) “As Google gets smarter and voice search becomes more commonplace, search queries are becoming more conversational, which means long-tail keywords are on the rise,” Forbes notes. “Customers who type in long-tail, descriptive keywords are generally highly motivated customers who are ready to make a purchase.” In short, although long-tail keywords may generate less search traffic, their specificity generally results in a higher conversion value.

Now, how can your business capture this search intent and drive more of those clicks? Two words: Schema markup.

 Offering a way to rise above your SEO competitors, Schema markup is a type of microdata — a specification that helps data to be embedded inside HTML documents and make it readable for computers. Essentially, it’s what enables a better browsing experience for users by aiding in analysing information on web pages to produce relevant results in the form of what are known as rich (i.e., more detailed) snippets.

 Rich, relevant results lie at the core of driving traffic. Search today more often starts with a question; one communicated as a typed keyword or phrase or — increasingly — asked of Alexa, Google, Siri and other voice assistants. And as outlined above, people being people, those queries tend to be complex and messy, leaving it to your AI-embedded website to decipher just what it is consumers want to know about your product or brand, and then to provide rich, specific answers via consistent, properly marked-up pages. And if you’re able to do it? The rewards are rich — as evidenced by the increase in clicks to pages phenomenon that started this discussion. 

 “Remember that your website’s visitors are either looking to solve a problem or obtain more information about a problem they have otherwise, they wouldn’t be using a search engine,” notes a Forbes contributor. “Keyword research is now more of an art than a science. The qualitative process of understanding user intent and making sure that your keywords match is the secret to keyword research success in 2020.”

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