5 Ways SEOs Can Use Machine Learning

How Can Machine Learning and AI Improve Your Processes?

Advances in machine learning and artificial intelligence can optimise your SEO efforts and improve business processes in ways you can scarcely imagine.

Machine learning is one of the fastest-developing areas of technology, and its implications can be far-reaching. SEOs spend plenty of time and effort looking to second guess the ML-powered algorithms used by Google, but there are also applications closer to home.

Here, we examine five ways in which the continuing innovations from machine learning developers can have a fundamental impact, bringing focus and efficiency to your SEO efforts.

1) Can you out-smart Google?

To out-smart the most powerful search engine might sound a little ambitious, but as businesses are better able to exploit machine learning, they are working with the same tools that power Google’s enigmatic algorithms that lie at the very heart of SEO. With every update, the SEO world is abuzz with theories as to what aspects of a site are the biggest ranking factors. It could be that as our use of ML becomes more sophisticated, we come closer to revealing Google’s most jealously guarded secrets.

2) Scale up the analytics

If you think cracking the code of the Google algorithm sounds a little like the Quest for the Holy Grail of SEO, there are other applications of machine learning that can still reap big rewards. The whole point of any type of automation is to do what a human can do, only quicker, more accurately and at a greater scale. In today’s data-driven marketing environment, that is crucial to get the most accurate information. And best of all, it means those weekly reports won’t just be more detailed, they can also be delivered automatically, saving you time and hassle.

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3) Optimising your content

Automation of analytics gives you more time to focus on creating great content. But ML can help you there, too. By analysing internal and external data, it can identify the types of content that your readers typically respond well to, as well as the topics that are trending most right now. It can also guide you as to what type of data works best at various points in the buying cycle – in other words, helping you optimise not only the “what” but also the “when.”

4) Understanding consumer touch points

Search is evolving in ways that would have been hard to predict five years ago. For example, voice search negates the need for a SERP as such, delivering just the “best” result, while Google’s increasing ability to directly answer search queries as opposed to directing searchers to appropriate sites redefines the very concept of what a search engine is. Adjusting your digital marketing strategy to meet these new dynamics is not easy, but the first stage in the journey is understanding how your target customers interact with these tools and platforms. Machine learning is the perfect analytical tool when so many complex variables are in play.

5) Using predictive modelling to enhance UX

The more we know, the better we can predict what customers want and the actions they will take. As ML develops, so our ability to use it in this way will also be enhanced. Better understanding of your customer’s tastes and interests means a more bespoke experience for them when they visit your site. We see this already with sites like Amazon offering suggestions of things you might like, but that is just the beginning. ML can create a tailored experience where the website anticipates what the visitor wants before they even know it themselves. It is potentially the most exciting ML application of them all.

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