Machine LearningSEO

7 Ways Machine Learning Impacts Your SEO

5 Mins read
Machine learning was introduced as a part of Artificial Intelligence back in 2010. After explaining how machine learning isn’t channeled to bring out the evils of mankind, the fear of eternal – read: automated – damnation was lifted away gradually.

Sure, a machine evolving explicitly under its automated and technological influence seemed scary at first, but we are 9 years too late. Artificial Intelligence is channeling a breakthrough for humans due to technological innovation and rapid evolution.

Optimizing a system with machine learning algorithms and artificial intelligence technology was hard – no doubt. Despite these out-of-the-box efforts, machine learning has facilitated digital marketing to such an extent that it has not only helped to increase effective personalization via search engines but also improved user-experience.

Impact of Machine Learning on SEO

From the emphasis on mobile-first indexing to accelerated mobile pages (AMPs); from voice search to a strong focus on visual search – Google has implemented Machine Learning in its SEO watch thoroughly for the facilitation of better user experience.

Today, there are around 3.5 billion and 1.2 trillion search queries that are conducted monthly and annually, respectively, on Google. More than 90% of all online experiences begin with the help of a query posted on Google – a search engine that has been bent upon improving user experience ever since its conception.

Not many digital marketers understand RankBrain – a Google algorithm for categorizing and ranking your webpages on its search engine – actually works. We all know one fact and that is: Google’s search engine is now constantly evolving, thanks to artificial intelligence and innovations via machine learning.

In order to facilitate the B2B  industries, Google integrated its system and algorithm with machine learning so that both marketers and consumers can have a taste of its automated evolution. SEO is a powerful lead generation tool for B2B companies, which is why you need a strong B2B SEO strategy in order to stay one step ahead of the competition

In simpler terms, machine learning helped to influence better link-building strategies and higher website ranking with rigorous vigilance and penalties so that websites can create quality content for their consumers. It helped to improve the user experience for consumers, giving them a clear direction to follow through for their shopping needs. 

Revolutionizing SEO with Machine Learning

Mentioned below are 7 ways that Machine Learning has helped impact SEO with its evolving technology.

Identification of Spam Content

Most websites tend to introduce webpages with content that is neither original nor of quality. Plagiarism or rehash content is a personal favorite of low quality or amateur marketers who tend to opt for the short-term routes in order to increase attention via publications to their websites.

While there are human-raters as well, machine learning makes it quite easier to sift through multiple webpages for the identification of the content that is spam, repetitive, plagiarized, and consists of synonyms or fluff to increase the word count for more user engagement.

Identification of Broken Links

Since backlinks are highly important in ranking a website on Google, marketers opt to integrate as many webpages in order to redirect users from external web sources to a parent website or vice versa. Backlinks are integrated as hyperlinks in anchor texts of keywords that have higher volume and ranking on the search engine.

Quite often, these websites might consist of hyperlinks to webpages with broken links or hyperlinks to content that don’t match the ones being promoted.

Machine Learning has helped Google to analyze such webpages and their backlinks thoroughly, using them as an ultimate proof of a website’s credibility and reputation in the niche industry.

Improved Quality of Search Results

Google considers ranking signals to be of utmost importance – third-most to be exact – as they work in coordination with the patterns of search queries to improve the quality of results on display. Since search engines play a major role in helping brands uncover user-centric data for the creation of buyer personas, these integrated ranking signals will continue to help then ensure predictions as well.

In this way, search engines will help human innovators to create advanced technological projects while Machine Learning takes the cake for handling their content optimization and marketing strategies. However, while Google’s main agenda is to improve user experience, it doesn’t necessarily mean that it has to achieve it through complete automation.

Users demand live experiences as well – machine learning is just a small yet highly advanced portion of automated integration and therefore, doesn’t threaten the legacy of human interaction at all.
Categorized Search Results

A study conducted on the Russian search engine, Yandex, found that this search engine focused primarily on presenting search results in categories by using phrases largely for queries in context. This categorization and customization of search results were integrated due to Machine Learning, which later on was adopted by Google for improved and personalized user experience.

Google learned that stringed queries help to personalize searches and increase click-through rates by a whopping 10%. It was learned that machine learning was actually helping the search engine to understand and garner results related to past searches.

This also helps search engines to rule out predictive texts in correspondence to the entered search query, therefore helping to facilitate users for better UX experience in case of large, conference settings or urgencies.

Qualitative and Improved Image Search

In 2013, it was reported that Flickr alone, published around 1.4 million images in a single day, with an image-and-video oriented social media channel, Instagram, taking the toll up to 40 billion a day.

Since humans have a short attention span, it is no such secret that we focus primarily on content that consists of images, videos, or any kind of graphics for maximum retention of information. Similarly, in order to facilitate us in our searches, Google integrated machine learning with schema data of images so that it can learn about their texture, color, shape, and other graphics as well.

Machine learning also helped to make image queries possible instead of text queries. You can now add in an image in the required format on the search engine for a customized and cataloged array of results by Google. This will help users to find images that are similar to the one they uploaded on Google, therefore, aiding in their search instantaneously and maximally.

Similarities in Words

Since millennials and Generation Z users tend to use words that have little to no meaning in their sentences, a typical search engine wouldn’t present any results since there would be no certain evidence of it.

Due to the intended and increasing usage of such words, machine learning has learned to adapt its software with results from the web that are not only improving with time but are accurate as well.

These results, therefore, are chosen from searches used over time with the help of identification of similar patterns and signals that somehow, seem ideal for the provision of such information. 

Depiction of Synonyms

Machine Learning has allowed Google to identify keywords with correspondence to their synonyms for advanced and integrated results for White Label SEM.

It is reported that there are around 8,000 – 12,000 homonyms that can make sentence interpretation seem like a big job, had it only been for Google bots and other search bots. With Machine Learning, there is a bigger task at bay since not only does AI have to understand the meanings of keywords used with context to their spellings or pronunciation, but also has to interpret the sentence for the desired search result.

For instance, if you enter the word, ‘Ph.D. Degree’ on Google, then the search engine will bring up results that match the mentioned phrase along with its synonyms as well. Similarly, if you intend on searching for a ‘travel guide’ then chances are that Google might present replace the word ‘travel’ with ‘trip’, ‘getaway’, ‘cruise’, or any other synonym or word that best fits the conundrum. 

The Wrap Up

Machine learning has changed the cultural landscape of SEO. It helps to connect with the right audience at the right time, providing them the best online experience. You’d soon identify a big drop in your customer’s search engine visibility, traffic, and conversions. Machine Learning has also helped Google integrate voice searches for user intent, which shaped most of the digital marketing trends of 2018 and will continue to do so this year.

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