Traditional media buying is dead: long live the machines
Today, media buying has not evolved much beyond traditional methods such as manual insertion orders. As consumers continue to shift away from more established media sources in favor of digital-first options, how marketers think about and approach advertising in a holistic sense must evolve.
Through machine learning, marketers don’t have to rely on much to achieve their goals when running advertising campaigns.
Luckily, machines – namely machine learning – are here to help. Through machine learning, marketers don’t have to rely on audiences, publications, customer panels or anything of the sort to achieve their goals when running advertising campaigns.
How machine learning works in digital advertising
To understand why machine learning is so much more effective than traditional media buying, it helps to understand how the algorithms work and how that approach differs from what has been done in the past.
At its core, machine learning takes in a wide variety of inputs and then learns over time which of those variables is most strongly correlated with a desired outcome or output. The more data it has, the more effective machine learning models can be at providing key results – no matter what the KPI might be.
For example, let’s say a crypto trading app was looking to acquire new users through paid channels. If they took a traditional approach to user acquisition, then they would probably target younger people along with people reading about crypto within properties such as news apps.
But just because someone fits either of these descriptions doesn’t mean they are interested in installing a crypto trading app. Many other variables may be much better predictors – and machine learning is more often than not much better at finding these distinctive predictors than humans are.
How machine learning has already revolutionized advertising
In many respects, none of this is new. After all, the biggest players in digital marketing today (Meta, Google, TikTok and Amazon) are successful because of this technology.
The company formerly known as Facebook is a great example of this. Its ad business has grown to be the juggernaut it is today because it has been able to use its data to help advertisers of any size effectively find and target their best audiences.
The problem is that outside of these self-attributing networks, effective, quality machine learning is largely unavailable. As a result, advertisers can’t use machine learning to reach their best audiences.
According to Insider Intelligence, the average adult will spend around three hours and 39 minutes a day using a mobile device by 2024. Less than a third of that time (around an hour and eight minutes) will be spent on social media.
If advertisers want to reach consumers outside of social media, they’re still reliant on old-school methodologies. And in an increasingly fast-paced and privacy-first world, audiences and manual insertion orders are not going to cut it.
In order to see quality results at scale, marketers will need to embrace machine learning for all of their advertising. Machine learning is already here – it’s just time to make it more universal in marketing and advertising.
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