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Using Machine Learning In Digital Marketing

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Posted by Corey Brown

Content Marketing Manager

Published in Marketing, and Strategy

As digital marketers, we are always on the lookout for the newest tools that might offer us newer and more actionable insights and help us connect even more with our audiences. Machine learning is a technology that is becoming more readily available and more widely implemented in many industries, including MarTech. By being able to quickly analyse immense pieces of behavioural data and predict experience-based decisions in a human-like way, machine learning has lifted our ability to efficiently deliver effective marketing strategies and provide more consistent and reliable customer experiences. 

What is machine learning?

Machine learning is a type of artificial intelligence and an important part of the growing fields of computer and data science. Its purpose is to use data and algorithms to imitate the way that humans learn. The extent of its usefulness is vast: 

  • Self-driving cars make use of machine learning to make sense of their surroundings and predict how other vehicles will behave.
  • Netflix automatically curates your entertainment with its recommendation engine through machine learning. YouTube’s algorithm functions similarly – in fact, 70% of videos watched by users are fed to them through recommendations. 
  • Popular language-learning app Duolingo uses machine learning to determine user vocabulary strength and to know when to provide a refresher.

In the context of digital marketing, machine learning can be leveraged in a number of ways to boost engagement, drive conversions, improve content relevancy, and provide an overall better user experience.

Applications of machine learning in digital marketing

Machine learning has played a role in the strongest marketing strategies for some time. It assists marketers and businesses in numerous ways, including understanding what is most important to the customer by leveraging behavioural data and applying learned information to optimise digital experiences, enabling us to continuously iterate and improve on how our customers interact with our brands. It can also play a role in streamlining content management and production processes.

IDC reports that marketers believe that a key enabler of customer loyalty is “support for a consistently high-quality brand experience” and that improvement of the relevance of their communications to their customers through personalisation is the way to achieve this – a method that machine learning can facilitate.

Check out a handful of ways that this tech can be applied in digital marketing:

Team brainstorming

1. Pay per click campaigns

The fundamental role of machine learning is to replace slow and expensive – but intelligent – human decision-making with a faster and more reliable alternative. Digital advertising methods like pay-per-click (PPC) involve lots of data, which has to be sorted through in order to determine the next best move to be made. Advertisers benefit from machine learning by quickly sorting through ad performance data, identifying top-performing ads and automatically optimising a campaign in real-time, sparing companies enormous sums of money in ad spend by saving time and avoiding the typical guesswork.     

2. Personalisation and Contextual Content 

Personalisation is an increasingly important staple in the modern digital marketer’s toolkit. It’s a feature that forms part of the core of digital experience platforms which are aimed at improving customer experiences wherever a brand is found on the internet. 

Personalisation in marketing is about leveraging the useful data we have on a user to provide more relevant, useful, and meaningful content. No two visitors are the same, and the more we can offer a digital experience that addresses their wants and needs, the more value we can provide them and, in turn, increase the likelihood of conversion and long-term retention.

Woman with mobile
Content personalisation is a key function of a digital experience platform, automatically optimising your content for specific user types. 

Machine learning improves the customer experiences we deliver by analysing what content visitors engage most positively with and automatically adjusting content for each type of user, in turn providing us with suggestions for future content ideas.

Let’s take a second to highlight the difference between contextualising and personalising content from a conversion rate optimisation (CRO) exercise, which are similar in execution but with different goals. CRO is traditionally aimed at delivering and maximising business-centric outcomes like conversions, click-throughs, and other marketing metrics. While revenue is naturally the end goal for any real business objective, using machine learning for improving digital experiences should prioritise understanding the needs of the user first and foremost, ensuring they derive as much value as possible from an exceptional user experience. Marketing outcomes are a byproduct of delivering a well-designed digital experience, and machine learning enables us to do that more accurately than ever.

3. Content Production

Machine learning doesn’t just help us better serve customers and users on the other side of the virtual counter; content creators’ lives are also made easier with machine learning. Content marketers experience the benefits of working with these algorithms, speeding up creative decision-making and facilitating the editorial process in content production.

Machine learning can build spelling suggestions based on historical selection and come to understand your preferred terminology, taking new inputs for every change that a user makes on a post and applying the learned information where it can to improve future suggestions. An algorithm can also provide keyword and tag suggestions for your posts based on content analysis while it comes to know your tastes and tendencies from your historical selections.

Computer screen
Machine learning algorithms can come to understand your stylistic preference in much the same way a co-worker might.

4. Image recognition and SEO

A relatively newer class of machine learning called deep learning drives image recognition technology; that is, the identification of objects within a photo or an image. While this is usually an easy task for humans to perform, machines still struggle with simple visual recognition and the categorisation of objects. When dealing with larger media libraries, tagging of images and applying alt texts – a critical component of SEO, accessibility, and CMS file management – quickly becomes arduous and expensive when done by human hands. Deep learning helps machines perform this function with remarkable accuracy. Leveraging machine learning and deep learning technology, tools like Altis provide automatic intelligent image tagging for all files uploaded to a CMS, satisfying standard SEO requirements cleanly, consistently, and at scale.

The Altis Digital Experience Platform and Machine Learning

Altis DXP brings sophisticated machine learning capabilities to WordPress, empowering marketers and content creators with a unique editorial toolkit and the ability to provide highly personalised customer experiences.

Book a demo to find out how machine learning fits into your content marketing engine: