Will machine learning change the landscape of content marketing?
Will machine learning change the landscape of content marketing? The very simple answer to this question is yes – but even more pertinently machine learning is already a defining element of the content marketing space. In this article we will explore the precise meaning of machine learning, how it is changing content marketing and how you can harness it in your strategy.
What is machine learning?
The phrase machine learning is often thrown around in reference to general advances in computing power, but what exactly does it refer to? Essentially it’s a branch of technology under the umbrella of Artificial Intelligence that deals with automated analytical model building. Translation: machine learning finds insights in data without being explicitly programmed where or how to look.
A great example of this is the Netflix video streaming service. Not only is the service’s user interface automatically optimised for the thousand different devices – and multiple locations and types of viewer they serve – according to the company’s technology blog the service pre-loads videos its algorithms predict you will watch, in order to reduce loading times when you actually click ‘play’!
Naturally, machine learning is also extremely useful for extracting insights from marketing data in order to more effectively target campaigns – and it’s an essential tool in creating modern virtual assistants, apps and any systems designed to respond to human users.
How is machine learning changing content marketing?
Machine learning is a key part of the engine behind the curtain for many tools and processes used in content strategy. And its sophistication is increasing all the time. Here are some of the key ways that it is likely to alter the content industry in the near future.
- By getting even more personal
Personalisation is a killer tool for content strategists and creators alike. Primarily because customers are far more likely to engage with brands that tailor their content to them.
However, true data-driven personalisation has traditionally been very difficult to achieve. This is because it requires the constant distilling of big data into meaningful actions, sometimes within seconds. For example a chatbot designed to produce a personalised chat experience has to register a human question, process its meaning and then deliver a personalised and accurate response almost immediately. And this sort of behaviour has to be achieved at scale. It’s no use personalising your content for a single customer – the process needs to be actioned for your entire customer base.
Machine learning technology helps to do this. How? By recording and categorising all the actions taken by customers on your website – or elsewhere online – the machine learning algorithms in marketing automation tools can refine content to better suit individuals, boosting engagement and ROI at scale.
Armed with highly focused customer data sets, marketers can build pieces that will be useful to very specific groups of people – rather than having to take a wild stab in the dark at a broad demographic range. Tools of this sort allow content marketers to make informed decisions faster so that content remains highly relevant and optimised for the current marketplace.
- Through increasingly sophisticated SEO
Unsurprisingly, Google’s algorithms also rely heavily on the rapidly expanding capabilities of machine learning. Which in turn has seen the art and science of Search Engine Optimisation become ever more sophisticated.
Perhaps the best example of machine learning’s influence on SEO is Google’s RankBrain – an algorithm that has existed since 2015 and has been regularly updated to reflect the continued growth in AI. In essence RankBrain is a machine learning system that converts written language into mathematical vectors which can then be interpreted by computers. This enables Google to match search queries with the right websites in ways that are targeted to that user, their intent, and in line with the content of the sites.
The machine learning factor is particularly significant as it allows the algorithm to match words and phrases that are unfamiliar. It does this by relating them to terms that have a similar meaning and also by taking into account the entire search query. For example try entering “Which hat” into Google image search – you’ll likely get pointy black pictures on the assumption that you can’t spell (or can’t be bothered to spell) ‘witch’. However go on to type “Which hat is best” and the context added by the additional words will see Google’s algorithm change the assigned meaning of the word ‘which’ – and deliver different images (with no witches hats, which is a little unkind – we’re sure they’re the best hats for some people).
Essentially as Machine learning improves in the coming years RankBrain will become better and better at matching human queries with relevant sites and information. So what does this mean for your SEO strategy? It’s difficult to tell at this point but in broad strokes it would suggest that SEO will be more difficult to ‘game’. Old school techniques like keyword stuffing and backlinking will become increasingly redundant and the creation of good quality and well-researched content will instead be vital.
How can you harness machine learning in your content strategy right now?
There are two clear ways that marketers can improve their strategy and success with machine learning.
- By using AI-driven marketing tools: As machine learning becomes more prevalent more high-quality tools are becoming available to content strategists. For example platforms like Atomic Reach and Rare.io can provide insights on when it’s best to send content and automate your personalisation.
- By planning for the future of SEO: Keeping abreast of changes in SEO is a content strategist’s bread and butter, but it is well worth implementing a long-term approach. The success of machine learning in RankBrain suggests it is important to plan for constantly increasingly the quality of your content.