Last year, Google announced that it was using a system called RankBrain to help guide the development of its search algorithm. 

Essentially, the aim was to provide better and more relevant search results to users.

According to a new study by Searchmetrics, the move has paid off.

Here’s a bit more info on RankBrain and what’s happened since it was introduced.

What is RankBrain?

RankBrain is an artificial intelligence system that uses machine learning to better understand exactly what people are looking for when they type a search query into Google.

If RankBrain sees a word or phrase it doesn’t understand, it can make a guess as to what words or phrases might have a similar meaning and filter the result accordingly. It can also update itself over time, applying its conclusions about how and why people search to future results.

In other words, it is designed to decipher complicated, vague, or poorly worded long-tail queries to deliver exactly what the user is looking for.

How has it impacted search results?

Searchmetrics recently analysed the results of 10,000 keyword searches on Google.com to better understand what high ranking pages have in common.

Overall, the results show that search results are now more relevant than ever before. But even more interesting, it also concluded that the techniques marketers use to artificially boost their search rankings are becoming less effective.

Five things that prove Google is more relevant

1. Search results show greater semantic understanding

According to Searchmetrics, higher ranking search results are significantly more relevant to the search query than those lower down, however, this is not simply based on an analysis of matching keywords.

Now, search results show a greater understanding of the semantic relationship between the words in search queries and the content shown in results.

While positions one and two tend to be reserved for top brand websites, those in three to six are said to be the most relevant.

My own Google search confirms this, with the third result giving me exactly the answer I was looking for. 

Interestingly, the second result (which also answered my question) is from a lesser-known publication, confirming that relevance does indeed appear to trump even more recognisable sources.

2. Word count is increasing on higher-ranking pages

Searchmetrics found that while word count is increasing (due to results being more detailed and more holistic) - the amount of keywords is not.

Again, this is because Google is trying to interpret the search intention, not simply match keywords. 

3. Bounce rates are rising for top ranking results

Bounce rates are usually considered in a negative light, but when it comes to search results, a higher bounce rate indicates that Google is doing its job.

In its analysis, Searchmetics found bounce rates have risen for all positions in the top 20 search results and for position 1 have gone from 37% in 2014 to 48%.

This suggests that users are being directed to the right result, meaning there is no need to look or search elsewhere.

4. Backlinks becoming less important for ranking

As content relevance grows in importance, other factors like backlinks are becoming less so.

This is also because of the rise of mobile search queries, with pages viewed on mobile devices often being ‘liked’ or shared but rarely linked to.

  

5. Google is prioritising relevance over optimisation

Finally, Searchmetrics found that the URLs for pages that feature in the top 20 search results are around 15% longer on average than in 2015. 

This shows that Google is better able to identify and display the pages that answer the search intention rather than merely displaying highly-optimised pages, with longer URLs more likely to be buried deeper within websites.

In conclusion...

With the introduction of RankBrain, there's no doubt that Google is taking AI and machine learning more seriously.

According to CEO, Sundar Pichai, it is just the start. He recently commented that "be it search, ads, YouTube, or Play, you will see us — in a systematic way — apply machine learning in all these areas.”

Undoubtedly, it could shape more than just search in 2017.

Further reading:

Nikki Gilliland

Published 16 December, 2016 by Nikki Gilliland @ Econsultancy

Nikki is a Writer at Econsultancy. You can follow her on Twitter or connect via LinkedIn.

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