Basis Technologies' Noor Naseer breaks down the most important trends for digital advertisers to know for 2024.
If you’ve been searching for a comprehensive, intelligible introduction to machine learning that doesn’t read like it was written by Einstein for Einstein, we’re here to help.
Because, come on. There was only one Einstein, and no one has had enough coffee to be on his level right now. Or ever.
Given how saturated our media landscape has become with mentions of machine learning, however, we felt the need to explain the topic as well as clear up some of the misconceptions between it and AI. Moreover, it’s incredibly important if you’re a marketer or business to understand machine learning and what it can do for you.
Ready to learn … about how machines learn? Read on.
Today’s average marketer already has an almost unmanageably full plate. Not only are you supposed to create meaningful campaigns, understand the difference between and select the best search engine marketing strategies, and somehow find time for that cup of coffee – now you’re supposed to understand machine learning too?
Before you give this a pfft and head to the nearest monastery to resign from your worldly way of life, be comforted: Machine learning isn’t actually that complex a topic.
In a nutshell, machine learning is a way of helping machines access information. It’s a complex world, and to give an algorithm all of the information it needs to perform a certain task – for instance, predict consumer behavior and design ad strategies accordingly – would be nigh impossible. The rate at which humans can feed information to our exponentially faster machine friends is laughably slow.
Instead, we feed sample pieces of information to machines. Given enough samples (we’re still talking in the millions), the machine can then learn what it’s “looking for.” Calum McClelland uses the example of a cat: Give a machine enough pictures of cats/not cats and tell them which is which, and it can eventually build a model for recognizing whether or not an image contains a cat.
So when the robots come for us, we can at least rest assured they will not be confused by cats.
Seriously, though, if machine learning is all about an algorithm teaching itself to do stuff, then how is this different from AI?
Good question. No introduction of machine learning would be complete without a quick look at the difference between it and artificial intelligence.
In a nutshell, artificial intelligence is any intelligence that can mimic human behavior to perform a complex task: say, recognizing human speech or faces (or cats). Machine learning leads to artificial intelligence. Machine learning about the difference between cats and non-cats >> the AI ability to recognize cats in the future.
This is one type of AI, recognizing cats, and as such is classified “narrow.” Once AI can do most or all of the things humans can, that’s general AI. We’re not there yet.
Where are we? At the point of a machine learning explosion in business.
Lest you think machine learning is some nebulous future endeavor, know that it’s not. In fact, smart companies are already using it to build ever-more-sophisticated digital marketing campaigns. It is officially changing the customer journey.
So how can you use machine learning for marketing? Well, for instance, you could use machine learning to teach an algorithm which of your products consumers have historically searched for on a given holiday sale weekend, then use the sophisticated algorithm it builds in your campaign strategies going forward.
You can also use it to predict retail cross-selling, financial risk assessment, and medical mortality. The leg up this will give you over your competitors is substantial – although it’s worth noting that more and more businesses are jumping on the machine learning bandwagon, so you won’t stay ahead of the curve for long if you don’t start leveraging it today.