Google has begun to integrate AI into a number of their products which has had a significant impact on the world of SEO – particularly affecting how websites are ranked in the search results. As AI technology continues to develop, so will Google’s AI offering, which will alter the search landscape even more.
Currently Google uses AI to perform three main functions:
- Understanding search intent: Google uses AI to understand the intent of a search query. This helps Google to return more relevant results to users.
- Ranking websites: Google uses AI to rank websites in the search engine results pages (SERPs). This is done by evaluating several factors, including the quality of the content, the relevance of the content to the search query, and the website’s authority.
- Personalising search results: Google uses AI to personalise the search results for each user. This is done by considering a user’s search history, location, and other factors.
These three functions all have one thing in common – they provide a better user experience. Google is a service, and they want to ensure they provide the best experience possible so that users continue to use their search engine over competitors. With a rise in competition from the likes of Bing partnering with ChatGPT, AI helps Google to stay on top.
Here are some specific examples of how Google uses AI in SEO:
RankBrain is Google’s machine learning algorithm that helps understand search queries and deliver relevant search results. It uses AI techniques to interpret the meaning behind user queries and match them with relevant web pages, even if the search terms are ambiguous or uncommon. RankBrain considers various factors, including user behaviour signals and historical search data, to improve search result accuracy. This provides a better user experience as the user is presented with exactly what they are looking for, even if their search queries are vague.
2. Natural Language Processing (NLP)
Google employs NLP techniques to better understand the content of web pages and match them to relevant search queries. NLP enables Google to analyse the context, semantics, and intent behind the words used in queries and web content. By understanding natural language more effectively, Google can provide users with more accurate and relevant search results.
3. Multitask Unified Model (MUM)
MUM is a large language model that can understand and respond to complex questions. MUM is still under development, but it is believed to have the potential to revolutionise the way that people interact with search engines. Its basic aim is to answer queries in a way a human expert would. For example, if you were to ask a teacher a complex question they would provide you with an answer straight away, whereas it may take you typing a few different questions into a search engine before you find your answer. MUM aims to solve this issue.
4. Content Evaluation
Google uses AI algorithms to evaluate the quality and relevance of web content. These algorithms analyse various factors such as the overall structure, readability, and comprehensiveness of a webpage. AI helps Google understand if the content is authoritative, trustworthy, and aligns with user search intent. It also assesses factors like page load speed and mobile-friendliness to ensure a positive user experience.
5. Image and Video Recognition
AI-powered algorithms enable Google to analyse and understand images and videos. This capability helps Google index visual content and display relevant results for image and video searches. By using AI to recognise objects, scenes, and text within images and videos, Google can provide more accurate search results and improve the visibility of multimedia content. This is a great opportunity for challenger brands to rank higher in the search results. If you optimise your multimedia content ready for Google to analyse then you could rank highly in the video or image feature snippets. This is a great way to get on the first results page without needing to compete with leading brands that dominate the SERPs.
6. User Experience Signals
Google utilises AI to analyse user experience signals, such as bounce rates, click-through rates (CTR), and dwell time. These signals help Google understand how users engage with search results and web pages. AI algorithms interpret this data to assess the relevance and usefulness of search results, enabling Google to improve its ranking algorithms and refine the user experience.
How does Google’s AI integrations impact marketers?
The impact of AI on the marketing industry is still unknown, however SEO professionals need to get to grips with Google’s changes if they don’t want to get left behind. AI helps Google to analyse and understand site content so they can display the most relevant results to its users. Brands want to ensure their content is clear and easy to understand so that Google sees a clear connection between your site and the user query, and therefore, ranks you highly in the search results for the user to click. The best way to do this is by optimising current content, images and videos to make their contents and context obvious to the search engines. This ensures that you do not lose any ranking positions as Google begins to use AI more, so that your traffic, clicks and impressions are not affected.
However, this does not mean that AI is going to completely change your SEO strategy overall. It’s important to note that SEO is a complex and multifaceted field, and while AI plays a significant role in Google’s search algorithms, there are many other factors to consider for effective SEO. You still need to consider content quality, backlinks, website structure, and user experience design if you want to succeed in the increasingly competitive search results.
For more information contact our expert team today to see how we can help you form a successful SEO strategy in the time of the AI revolution.