Machine learning has come a long way since Hollywood painted it as shiny robots fueled by artificial intelligence. In the Hollywood version, robots usually end up replacing humans. But today, we’re actually using machine learning to supplement many of the things that humans do best. Take art, for example. Music. Customer service. Video games. Financial trading. News reporting. And of course, advertising.
Even the words 'machine learning' carry a lot of baggage for people. They feel foreign, scientific, and hard to understand. And for many professionals, the phrase still sounds like highly technical jargon. It certainly lacks the clear definition that would lead to a marketer feeling they can use it in everyday, real world applications.
Let’s start at the beginning: machine learning is a type of artificial intelligence that gives computers the ability to do things like diagnose problems, predict things, control variables and outcomes, and plan things – all without being explicitly programmed to do each of those things. It is fueled by algorithms, and these algorithms teach themselves to grow and change when they’re exposed to new data.
You can think of machine learning the same way you think about human learning. A child watches a TV documentary about lions in Africa, witnessing a pack of lions brutally hunt and devour other animals. Some time later, he visits a tiger sanctuary in the US and through inference from his previous exposure to the documentary, understands that big animals with sharp teeth are potentially dangerous and so consciously stays a safe distance away from the tigers.
If machine learning is about applying new data and information to future scenarios, you can probably already see the wide and impressive applications it has in the real world. It’s affecting almost every industry you can think of – and its impact on advertising is really starting to make a difference in how marketers target and deliver ads.
If you’re a marketer who has started hearing words like 'programmatic', 'data-driven', and 'artificial intelligence' everywhere, you’re not alone. And you’re especially not alone if you’re still trying to figure out how machine learning can help make your ads perform better. Here are just four of the ways it can do exactly that:
Maximize real time bidding
If you’ve been in the marketing and advertising industry for more than a couple of years, you can remember the days of buying and selling ads manually (maybe even over the phone!). But buying and selling advertising inventory that way just isn’t efficient anymore. It’s doesn’t help you optimize your advertising, and it’s not scalable. That’s why we turn to machine learning to help make ad programs more efficient and to help make sure they perform the way we need them to.
In an incredibly complex operation, machine learning can help determine the optimal amount to bid on every single impression. The algorithm has to know the performance metrics you’d like to achieve – such as click through rate or install rate – and then it can assess the probability of each impression’s result being positive for that outcome. The data for this exercise can be provided from a number of sources – but the important thing is that it means your dollars are working harder for you, and you’re seeing better results.
Capitalize on lookalike targeting
We can thank Facebook for the rich data that provides algorithms with opportunities to specifically target lookalike audiences. Remember filling out your Facebook profile years ago, identifying your interests, which TV shows you watch and which sports teams you follow? That input affects the way you’re targeted today.
The machine learning algorithm can create specific audience clusters based on which kind of people will help reach a certain objective. For example, an algorithm may learn women over 30 who show an interest in online poker are found to be more likely to complete a tutorial program. This information then informs the targeting and placement of your ads, based on the action you’d like users to take. Ultimately, lookalike targeting means your ad is shown only to people with a high likelihood of response.
Data mining for optimal targeting
You’re probably aware by now that no matter what you do online, you’re leaving a trail of data with every click. As a marketer, this is something you can appreciate – it helps you glean more information about the people you want to target. The problem is that much of this data is dispersed across different service providers, providing an incomplete view of your target audience.
Data services use dynamically-created statistical models to derive additional, relevant information about people. This information could come from publishers or from social data like ratings and reviews. This means that you can learn an incredible amount of detail about your audience and optimize your spend to best reach them in the right place, at the right time with the right ads.
Predictive performance analysis
Perhaps one of the coolest uses of machine learning in advertising is the ability to predict ad performance before they are served. This is possible using the advertiser’s own historical ad performance data as well as insights from similar advertisers. This allows you to make intelligent creative selection to maximize campaign performance.
This is just the tip of the iceberg when it comes to machine learning’s impact on advertising. There is so much that algorithms can do to make our advertising more efficient and more effective – the truth is, we probably don’t even know or understand all the ways it can be applied! We may not yet be in the world of robots that can replace our most mundane, everyday tasks – but it’s clear that machine learning has provided the advertising industry with an incredible amount of detail and power when it comes to targeting, bidding, placement and analysis.
Winner of Facebook’s Innovator of the Year, ReFUEL4 is the world’s leading data- driven creative platform. We deliver assets from 10,000 global creators backed by predictive AI technology. Learn more here.