Machine learning has risen in stature and significance over the last few years to become one of the most important pieces of marketing technology for those looking to improve their marketing. However, there’s still confusion as to what exactly machine learning is and how it applies to marketing. This is because the term has become so wrapped up in the technology sector that few marketers understand what it is and why it’s important for them to understand and implement it in their marketing campaigns. There’s even a sense of fear about it.
In this article, we look at the ins and outs of machine learning and how it can be beneficial for marketers to take advantage of it.
How is Machine Learning Different from AI?
Put simply, machine learning is a form of artificial intelligence. It describes the concept that a machine or algorithm can be set up in such a way as for it to be able to derive learnings from information without those learnings being explicitly given to it. While machine learning is similar to artificial intelligence, it’s not actually artificial intelligence, as the machine isn’t thinking for itself. The machine can’t gather information on its own, it can only compute and derive learnings from the data it’s given to anFalyse within a set of actions it’s asked to perform.
A simple example of machine learning in action is Facebook’s News Feed. The News Feed algorithm is programmed to understand patterns in user data and then take learnings on their preferences from that data. If, for example, User A has liked a number of posts from User B but very few from User C, the algorithm will learn that User A is more interested in User B’s posts than User C and subsequently start showing User A more content from User B than User C. However, it can’t take any actions beyond those it’s been asked to perform.
Of course, that’s a very simplistic example, but machine learning has many more, and much more complex, applications. The technology has been used by Cornell University to identify the location of whales in the ocean and pass that information on to ships so they can use it to avoid hitting the animals while sailing, and IBM has implemented it to understand the risk of heart failure in patients and try to put preventative measures in place. As the technology grows, such society-changing applications are likely to increase and benefit our way of life still further.
How does Machine Learning apply to Marketing?
It might not seem like the most obvious jump from those examples to marketing, but there are clear uses of machine learning for marketers to take advantage of. Indeed, machine learning is pretty much a natural fit for marketers. In recent years, modern digital marketing has taken a keen interest in the ability to collect, analyse, and understand the needs and preferences of the general public before acting upon that intelligence. Whereas once, that was done through consumer testing and focus groups, now in the digital age, it’s achieved through anonymously tracking people’s use of the internet and analysing the data.
That, of course, can be done through any standard analytics package, and has been done like that for many years. But machine learning has the benefit of scale and efficiency. As workloads and volumes of data have increased, the need to parse huge quantities of data and draw decision from them has increased too. We’re still in a position where this can (just about) be done by humans, but we’re quickly getting into a situation where there’s simply too much data to analyse properly. And even if you have the resource for a vast data analysis team, it’s hardly an efficient use of time when such labour can be automated.
More than that, machine learning is becoming so advanced now that it’s not just capable of understanding isolated pieces of information but can tie together multiple strings of data that may not have any clear association and draw learnings from them. This means that machine learning is evolving beyond the Facebook News Feed example previously given and even beyond the capabilities of even very large teams of human data analysts. It’s becoming something much more complex, something much bigger, and something much, much more powerful.
This has been explained further by Adobe Marketing Cloud’s Senior Product Manager John Bates, who offers in his blog post Hey, Data-Driven Marketing, Ready for Machine Learning? It’s Ready for You, a practical example of the complexity of machine learning, and how it could get even more complex in the future. “If every time you ask your phone to find a restaurant for you, you immediately ask it to also request an Uber, AI solutions would learn this pattern,” he explained, “and eventually, when you ask for a restaurant, your phone will ask if you would also like to request an Uber.”
So here, we see multiple data points coming into action that feed into the machine learning device to drive a complex response. Not just A happened so that equals response B, but A and B happened, so that equals response C. As the technology develops, there are huge implications for marketing techniques like personalisation. Sites could understand the purpose that someone is buying a particular item for, and make a prediction based on those learnings. So if you’re buying items for a carrot cake, machine learning could help a site understand that and pre-fill your shopping basket with the rest of the ingredients.
For marketers looking to drive traffic to a site with innovative marketing campaigns, there are also a world of opportunities. Machine learningcould work to understand that a certain customer likes to make those carrot cakes on a Saturday afternoon, and serve that person with ads for the ingredients, content about how to make the perfect carrot cake, or recipes for carrot cakes with a twist in the build-up to Saturday afternoon and that all-important bake. The possibilities are endless.
Machine learning opens up a world of opportunities for savvy marketers looking for sophisticated and automated technologies to use in their marketing campaigns. The technology still has a little way to go to be at the level we’ve discussed in this article, but it’s not all that far off, and it’s already capable of adding tremendous value to a campaign. But marketers need not worry just yet. While machine learning is very sophisticated, it still requires the creativity and effort of a human to understand how this automated technology is best implemented.
How do you think machine learning will impact marketing? Let us know in the comments below or via social media, and don’t forget to subscribe to the blog to receive new articles straight to your inbox.