TL;DR: Machine Learning 101: 3 Things Marketers Need to Know
I bet you do.
Mountains of data, in fact. Terabytes of data. Libraries worth of data. With more streaming in every hour of every day.
We marketers love our data, but, let’s face it … we probably only use a fraction of the data we collect.
It’s not that we don’t want to use more of it. We do.
It would be fantastic, for example, to follow each and every customer around, to see everything they read, how long they read it for, where they clicked next. You might even want to drop a cookie on their computer and see all the other websites they went to. You could survey them, too, and send them personal messages on social media. Test when is the best time to send them messages, and which channel they respond to best.
Then, with all that wonderful knowledge, you could hole up in your office and design a complete soup-to-nuts marketing strategy just for them.
I’m not talking about something like account-based marketing, where your work is for one big target company. I’m talking about a totally personalized, hand-crafted marketing strategy and execution for every single possible prospect your company could have.
Just think of it: thousands of completely personalized marketing plans. Tens of thousands of personalized messages. Hundreds of thousands of hours poring over the data, studying exactly how each and every single prospect behaves.
That’d be great, right?
Well, if you had unlimited time and unlimited resources, maybe. If you never had to sleep, and had no family and no life … and the assurance that you’d live to be at least 312.
Otherwise … forget it.
Being able to focus that closely and to process every little bit of data we have about our prospects and customers is laughable. Delusional.
We are not machines.
But what if machines could do all that?
What if a well-trained algorithm could follow each one of your prospects around and could recommend the perfect piece of content and send it to them at the perfect time, in the channel they’d be most likely to respond to it in? And what if the algorithm could even predict the perfect time for your ace salesperson to finally give them a call?
That’s what machine learning can do.
Here’s what you need to know about it (at least for starters).
Machine learning is a subset of artificial intelligence.
At its simplest definition, machine learning is nothing more than “using data to answer questions.” Hat tip to thank Google’s superb video series on machine learning for that definition.
It’s a specific type ‒ or discipline, if you will ‒ of artificial intelligence. One of its strengths is that a machine learning algorithm’s accuracy can improve over time. It can “learn.” So. while a program that can play chess might be considered artificial intelligence, a program that can learn to play chess, and ping pong, and any other game, would be an example of machine learning.
More complicated machine learning systems are often called “deep learning.” So, for the game example, deep learning systems are set up to use multiple levels – called “neural nets” ‒ to do their processing.
Here’s a Venn diagram to help understand: