Forward-thinking: AI and machine learning in SEO

First, a couple of definitions…

There’s a lot to AI, too much to go into in one small insight… More simply put, Artificial
Intelligence is a machine’s ability to perform the cognitive functions we usually associate with human minds.

Machine learning ‘simply’ learns how to improve an algorithm’s performance over time by adjusting the parameters based on the data they are exposed to. There are a number of types of machine learning, including:

• Supervised learning

This process involves training a model to make predictions by utilising a labelled dataset, where the data is trained and contains input features along with their corresponding correct output labels. Once the model is trained on this labelled dataset, it gains the ability to make predictions for new, unseen data.

• Unsupervised learning

This is where a model is not provided with labelled data, and must autonomously uncover patterns and relationships within the data. Without being informed of the correct output for a given input, the model relies on its own analysis of the data to make predictions.

• Reinforcement learning

This approach centres on using a reward and punishment model to train agents in making decisions within a specific environment. It holds the potential to be applied across a diverse array of problems, including search engine algorithms.

• Transfer learning

This technique involves taking the knowledge learned by a model on one specific task and applying it to a different, but related, task. Within SEO transfer learning can be used to improve the performance of algorithms that are used to understand and rank websites.

How we already use AI and Machine learning in SEO

Artificial Intelligence in SEO

Artificial Intelligence (AI) has been making significant strides in various industries, and the world of SEO is no exception.

Integrating artificial intelligence into SEO has many benefits, including:

1. Automate tasks

Manual tasks can be automated through AI, creating time for an SEO team to focus more on the analysis of data, findings and execution of the strategy and the analysis of data. However, even tasks that have been automated should always be reviewed to ensure accuracy, relevancy and quality.

2. Analyse website data

Data-driven insights play a pivotal role in various aspects of SEO, offering valuable opportunities to understand customer behaviour, optimise page structures, and spot SEO trends.

Data-driven insights play a pivotal role in various aspects of SEO, offering valuable opportunities to understand customer behaviour, optimise page structures, and spot SEO trends.

While AI cannot predict the future, it can identify patterns in data that would otherwise be impossible for humans to detect. This means that AI can help us make more accurate predictions about what will happen in the future, and even suggest possible solutions to problems before they arise.

Those working in SEO can use AI findings to help and support build future strategies.

3. Predict consumer behaviour

AI-driven algorithms have the ability to analyse customer online behaviours and preferences, and can harness this valuable data to fine-tune SEO strategies. This involves optimising for semantic search, targeting head terms, and offering personalised content tailored to individual needs. The AI algorithms’ continuous learning capabilities ensure that their predictions and insights become more accurate over time, further enhancing the effectiveness of SEO activities and delivering relevant and satisfying experiences to users.

Machine learning in SEO

Machine learning is a subset of AI that involves training algorithms to analyse and learn from data, without being explicitly programmed… sound familiar? It should, Google Analytics 4 is the perfect example of machine learning in action within what we already use.

Google states that:

Google Analytics automatically enriches your data by bringing Google machine-learning expertise to bear on your dataset to predict the future behaviour of your users

Machine learning algorithms can play a crucial role in analysing and comprehending various aspects with SEO. They can be employed to understand user behaviour on a website, process images during crawling, and establish connections between different websites and the content they host. By leveraging machine learning, SEO efforts can be enhanced and tailored to meet user needs more effectively.

Another use of machine learning that we, as marketers, have adopted is through content optimisation- with keyword research usually at the forefront of our content strategies which we often use Google’s own keyword planner – hey! Machine learning in action! Included in content optimisation is ensuring that your titles and meta descriptions are optimised, as well as any images or videos that are included in the content.

In 2019, Google introduced BERT, a type of NLP (natural language processing) to analyse content marketing efforts. The rollout of BERT caused a lot of buzz in the SEO space due to it officially affecting 10% of search queries when released – and with 8.5 billion Google searches every day that impact was… noticeable.

The future of AI and machine learning in SEO

In the pre-ChatGPT era, our comprehension of SEO primarily revolved around conventional keyword research and optimisation methods. These practices centred on identifying pertinent keywords and integrating them into content to achieve higher rankings on SERPs.

Conversational AI is a type of artificial intelligence (AI) that can simulate human conversation.

Google briefly explains how it works:

Conversational AI systems are trained on large amounts of data, such as text and speech. This data is used to teach the system how to understand and process human language. The system then uses this knowledge to interact with humans in a natural way. It’s constantly learning from its interactions and improving its response quality over time.

With a promise to change the landscape of search with the introduction of Google’s Bard AI and Bing’s Bing AI, it’s important to bear in mind that people are still relying on their own abilities to find information without the use of conversational AI. However, with that being said conversational AI has the potential to enhance and streamline the human-powered search process just as Google did in the past.

Such approaches were built on the belief that users would use specific keywords or phrases in their searches, and search engines would heavily rely on these keywords to assess a page’s relevance. Nevertheless, the growing influence of natural language processing technologies like ChatGPT demands that data-driven marketers adapt their SEO strategies to remain at the forefront of the field.

In addition to this, voice search optimisation may also be an important consideration for SEO professionals. The rise of voice assistants like Amazon’s Alexa, Apple’s Siri, and Google Assistant has led to a significant increase in voice search queries. As AI becomes more advanced, voice recognition technology will continue to improve, making voice search an essential aspect of SEO.

In summary, the potential of the future of AI and ML in SEO is vast. What we do know for certain is that it will accelerate the direction search engines are already moving.

Final thoughts

Artificial Intelligence and Machine Learning play a crucial role in search engines, contributing to improved user experiences and the delivery of highly accurate and personalised search results. By comprehending the user’s intent, context, and behaviour associated with each query, search engines can fine-tune their responses to better meet individual needs.

Moreover, search engines leverage AI and ML to enrich their search functionalities and formats, including featured snippets, knowledge graphs, image search, video search, and more. These elements are prominently displayed on SERPs and aim to promptly provide comprehensive and valuable answers to users’ enquiries.

SEO is not a static or simple process, keeping up-to-date with current and future practices is pivotal to SEO success.