Five ways machine learning will transform your marketing

Earlier this year, we ran an article explaining why machine learning is much bigger than Google and RankBrain. The technology isn’t just making our search engines and devices more intelligent; it’s transforming the way we approach and manage our marketing campaigns.

The machine learning revolution has already begun and things are going to get a lot more exciting over the next few years. So, to give you a taste of what’s to come, here are five ways machine learning will transform your marketing workflow.

#1: Big data becomes usable

As things stand, most marketers are swimming in more data than they can handle. The thing with data though, is that it means nothing without context and manually analysing the stuff takes a lot of time.

The industry leaders like Google, Facebook and Amazon crunch vast amounts of numbers, using machine learning to handle large volumes and apply that all-important ingredient: context. This is the power behind Rank Brain, Google Translate, self-driving cars and the rest of Google’s AI technology. And machine learning isn’t reserved for the tech giants either.

By automating the data collection and analysis process, there’s no limit to the amount you can handle. The trick is knowing what data to collect and the lessons you want your algorithms to “learn”.

#2: Predictive analytics is here

Machine learning draws patterns from previous data and applies it to everything it collects further down the line. This allows the technology to make predictions based on the data it’s already handled – for example, Google’s personalised search results or Amazon’s product recommendations.

For marketers, machine learning also opens the door to predictive analytics where consumer trends are spotted before they actually happen. Suddenly, an algorithm that tells us which takeaway is most popular during the X Factor final and automatically targets viewers with personalised ads throughout the day is perfectly feasible.

#3: Customer insights like never before

Part of the predictive analytics package is a more sophisticated level of customer insights. This starts with customer segmentation powered by machine learning, which can automatically create dynamic lists based on shared behaviours and preferences – all you need to do is set the parameters.

Want a list of customers who bought product A and went on to buy product B? No problem. Likewise, if you want a list of customers who bought product A and didn’t buy product B, you’re only one click away. In fact, machine learning can find new opportunities on your behalf and create customer lists you might never otherwise think of.

Once you combine customer insights with predictive analytics, machine learning can help you discover why some customers don’t buy product B. From here you can predict customer churn/drop off and adapt your offer to prevent them slipping away. Alternatively, you can create new campaigns designed to ease the concerns that prevent them from making the all-important second purchase.

Taking this further, you’ll have a more accurate model for predicting the lifetime value of customers. Not just an average lifetime value either, but a range of forecasts for different types of customer, based on their buying habits and interactions with your brand.

#4: Taking the pain out of technical SEO

Most webmasters still manually optimise every image they publish on their site. Setting heights and widths, compressing file sizes, writing alt descriptions and all kinds of other repetitive tasks – for every single image.

Wouldn’t you rather spend that time on creating better content?

Of course you would. And machine learning is taking the hassle out of repetitive technical SEO tasks like these. So why not define your image sizes, alt description format and compression parameters once and let automation take care of the rest?

It’s not only mundane tasks that machine learning can take out of your technical SEO workflow either. Automated reporting and audits allow you to detect technical issues faster and even predict them before they happen. Notifications and suggested fixes based on the history of your site and other resources mean technical SEO will become more efficient and less painful as your business grows.

#5: Personalised search and universal content

Personalised search is nothing new but machine learning is taking it to new places. Imagine a piece of instructional content showing users how to fix a common issue with the latest iOS update. One problem Google and content producers alike have is knowing which kind of format users prefer.

Some people would rather watch an instructional video while others simply want a list of bullet point instructions. Some users will be in a quiet environment where watching a video is suitable while others might be sitting on a noisy bus without any headphones.

Machine learning makes it easier to understand these preferences. Google can predict which kind of content users prefer based on their history of engaging with instructional content. It can even differentiate between their habits with recipe instructions, digital camera tutorials and solving problems with Windows 10.

This is great for the end user, of course, but it presents a bigger challenge for content publishers.

Do you invest in video content or stick with the simple bullet point lists? Maybe you should go with both. The problem is that it’s difficult to gather data from users who never see your listing in the first place because your content format doesn’t suit their personalised search experience. Definitive answers are difficult to get.

So how do we approach formatting content in a future where every search is personalised and users move from desktop to mobile and smartwatch to any number of other devices?

Content like water: design flexible content to flow anywhere. “Put water into a cup, it becomes the cup”—Content strategist Bruce Lee #fowd

— Josh Clark (@bigmediumjosh) November 7, 2011

It’s becoming increasingly apparent that the only sustainable option is a kind of singular, universal content – or “content like water” as Josh Clark put it in 2011.

Credit: Content Is Like Water by Stéphanie, inspired by the words of Josh Clark and Bruce Lee

As you can see, this isn’t a new concept at all. In fact, it’s one the likes of Josh Clark, Cindy Krum and Brad Frost have been talking about since the dawn of the mobile web. The only long-term solution will be media files that contain video, audio, images and text so search engines and devices can extract the content format that best suits the user in any given environment.

First of all, this solves the personalised search issue because each format is catered for. But it also completes the cross-device experience. Users can take the same content from their 8K TV screens to the headphones wirelessly connected to their smartphone, which they originally found on Google Home or their smartwatch.

Machine learning is already changing the way we manage our marketing campaigns, but this technology hasn’t even experienced its first growth spurt yet. Big things are going to happen over the next few years and the marketers who can get the most out of machine learning will be the ones who win the next phase of data-driven marketing.

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