AI in SEO Is So Advanced Now That It’s Going Against Itself
A digital presence is one of the most essential requirements of any business, and a website with high rankings on search pages plays a pivotal role in business success. In order to become the top choice of search engines, websites must adhere to a plethora of guidelines set by AI-powered engines. With more than 5.6 billion searches every day, search engines like Google, Yahoo, Bing, etc., take into account the user’s previous searches and offer the results that best suit the user’s needs. . As search engines use AI and machine learning to optimize their SERPs, websites are adopting a similar method to solve this conundrum. In this tussle between AI-powered search engines and AI-powered SEO tools, who will benefit the most?
Search engines – a brief history
In order to fully understand the need to use AI for SEO, let’s dive into the evolution of one of the biggest search engines developed over the years.
At December 1, 2000, Google launched its first browser toolbar with Toolbar Page Rank (TBPR), a free browser plug-in that allowed users to search for information using Google’s fast and highly relevant search technology. . And so, the dance for SEO began.
Google’s first named update was announced on February 1, 2003 at SES Boston, which brought a combination of changes to the search algorithm, as well as major index refreshes to improve the SERP.
Google Updates has made a name for itself in the world with its update on November 1, 2003, in Florida, as the search engine began to downgrade low-value SEO tactics of the late 90s, like keyword stuffing. This update turned out to be a game changer.
In october 2005Google brought Maps into the game by merging its maps data with LBC to improve its search capabilities.
On May 1, 2007, Google unveiled Google 2.0, an enhanced universal search engine with integrated search results for images, videos, news, local information and other verticals in various formats. This update ushered in a new era of mixed results pages.
June 1, 2010 – Google rolls out its Caffeine update. This update brings a 50% fresher index by increasing raw search engine speed and integrating crawling and indexing more accurately.
September 1, 2010: Launch of Google Google snapshot. This update provides search results to the query as it is entered. It revolutionized SEO when users started using long-tail keywords for better rankings.
February 23, 2010 – Google released its Panda update that hit websites hard, affecting more than 12% of search results. The new algorithm flagged content farms, sites with thin content or a high ad-to-content ratio, and other quality issues.
April 2012 – After weeks of deliberation over an “over-optimization penalty”, the penguin the update is deployed. This update focused on adjusting a number of spam factors like keyword stuffing and affected an estimated 3.1% of search queries.
August 2013 – Google unveils its Humming-bird updated one month before its official launch. This release is a core algorithm update that brings significant changes to semantic search.
October 2019 – Google made major improvements to its underlying hardware and algorithm to accommodate BERT, a natural language processing (NLP) model. This update helped the search engine better understand the context of queries.
How BERT improves search
BERT (Bidirectional Encoder Representations from Transformers) uses transformers to understand the context between words in a text. It uses an encoder mechanism that reads the entire text at once to learn the context based on the whole environment.
The model then tries to predict the actual meaning of the masked words based on the context provided by the unmasked words in the sentence. BERT’s two-way approach (MLM) offers a 15% improvement in its training accuracy at the expense of computational speed.
Improving SEO to tackle SERPs using AI
Search engine optimization is the process of collecting data about a running website’s traffic, identifying areas for improvement, coming up with a plan to fix the problem, and… improvement of the website to increase its ranking. SEO is an ongoing process that requires a lot of data processing and analysis. Artificial intelligence tools can be used to automate these time-consuming tasks to scale and improve overall site performance.
Long tail searches: A to study Bloomreach’s customer service engagement survey found that 82% of B2C buyers’ time is spent researching and browsing for the right content. This means the vast space left for long-tail searches that are overlooked by marketers. The AI integration helps to account for customers’ long-tail searches as it identifies matching content and presents relevant pages that are not related to the search.
SEO Audits: Regular site audits are one of the most complex and time-consuming tasks. These audits are important because they reveal issues such as broken links, duplicate content, etc., which could affect the search performance of the website.
Image: Semrush site audit example
AI-powered SEO tools like Semrush, Ahrefs, Spyfu, etc. provide comprehensive audits and reports, from keyword research to organic search trends, that not only have issues, but also ways to improve your website.
Voice search: According to data from findstack.com, 71% of users prefer to search for information by voice rather than by text. Integrating AI tools like AnswerThePublic helps you target question-like long-tail keywords that are synonymous with voice searches.
Content optimization: One of the key ways to improve your search ranking is to post content with topical credibility. AI tools like Content merging use deep learning to disseminate data from subject matter experts and improve the overall quality of the content itself. Artificial intelligence also helps to integrate visual data such as graphs, images and tables to increase user engagement.
AI – the future of SEO
As search engines are constantly updating their AI to improve the quality of their SERPs, websites must constantly respond to the changes that updates bring in terms of optimization. AI in SEO will open up new avenues to tackle ever-changing guidelines by understanding intent and directly delivering tailored results for user queries.
With content and strategy driving the SEO landscape, AI-powered tools empower SEO strategies to better understand search engine algorithms and improve their overall reach.