The way people search for information online is changing. Using machine learning, natural language processing (NLP) and deep learning, today’s AI algorithms provide users not just with highly relevant results but also accurate information in the form of AI overviews. In this post, we look at how search engines have evolved from keyword matching and generic results, to focus on user intent, personalised results and voice and visual search.
Contents
From keywords to intent
In the past, search engines relied on matching the keywords people used in their queries with those contained in web pages. If someone searched for ‘cheap holidays in Spain,’ websites that optimised that phrase as a keyword stood a reasonable chance of appearing in the results.
AI enables search engines to provide much more relevant results. Using NLP and contextual analysis it can accurately interpret the user’s search intent, i.e., why the person is searching for something, and thus deliver results that satisfy the user’s intentions.
Sophisticated AI models, like Google’s BERT and MUM, focus on three main types of intention: informational (where a user wants to learn something), transactional (where they want to do something, e.g., buy a product), or navigational (where people want to find a particular website). By understanding which one of these intentions is applicable, the AI can filter the results to ensure the user sees those most relevant to their needs.
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Personalised results
In addition to understanding intent, AI algorithms also analyse user behaviour, location, browsing history and previous interactions to personalise search results. Able to process huge datasets in real-time, they can predict what users are looking for, even while someone is still inputting their query.
The implication for websites is that ranking factors are not simply based on keyword matches but on the searcher’s individual preferences based on past searches and interests, together with current intent. As a consequence, the sites that appear in search results are more likely to engage users and lead to conversions for the website.
Voice and visual search
Voice assistants and visual search tools (e.g. Google Lens) have provided new ways for users to interact with search engines. AI voice search, which uses NLP and speech recognition, can go beyond traditional keyword-based searches to understand conversational queries spoken using natural language.
Platforms like Google Assistant, ChatGPT, Microsoft Copilot, Siri and Alexa can process conversational queries to provide highly accurate answers. Websites wishing to be featured in these results will need to optimise content for voice search by using conversational terms and long-tail keywords.
AI visual search tools enable users to search using images rather than text. By analysing the objects, visual patterns and text within images, together with image metadata, they have extended a search engine’s capabilities. Today, if users see a dish they like, they can find the recipe by uploading an image of the dish. Similarly, they can take a photograph of a product and use it to search for a retailer.
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Continuous learning
AI search engines use machine learning to continuously improve. The more users engage with search results, the more the AI can analyse search patterns, user interactions and clickthrough rates to refine its algorithms and provide better responses.
To achieve this, AI models use Reinforcement Learning from Human Feedback (RLHF), a machine learning technique that trains models to learn from human interaction and perform tasks that better align with human goals. Using this, AI search engines can consistently improve their ability to deliver relevant, personalised results.
SEO’s new frontier
To keep up with evolving search algorithms, businesses must adapt their SEO strategies. Traditional keyword optimisation is no longer sufficient; instead, businesses must focus on creating high-quality content, focused on user intention, which meets AI algorithm ranking criteria. Structured data, voice search optimisation and enhanced user experience (including faster loading times) are also essential components of SEO.
Another concern is that AI search results, such as Google’s AI Overviews and Bing’s AI-generated answers, are changing how users interact with search engines. These overviews provide answers without users needing to click on traditional search results, potentially causing websites to see a decline in organic traffic.
Sites that previously relied on search rankings for traffic will need to adapt by providing high-quality content that is optimised for inclusion in these AI-generated responses. According to Google, in results where overviews are displayed, web pages referenced in the overviews receive the highest number of clickthroughs – even outperforming those that come first in the traditional results.
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Conclusion
AI algorithms are transforming how people search online and how search engines process and deliver results. Their ability to understand user intent, personalise results and deliver voice and visual search responses means websites will need to adapt how they conduct SEO if they are to stay visible and attract organic traffic.
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