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:
And here’s an extremely good, easy-to-understand video from Google that explains how we humans have figured out how to give a machine instructions that let it “think” independently.
Machine learning applies to almost any large data set.
While we marketers might be interested in machine learning to identify leads, or to optimize our messaging systems, there are also vast applications for machine learning in medicine, finance, weather … in any large data set, really.
It’s good at categorizing things, as we saw in the Google video. One application for that already in use is in recognizing photographs.
Facebook and Google have been doing this for a while, of course, but soon the algorithms may be good enough to recognize us even with sunglasses or a mask.
If you’d like to play around with a much more benign form of photograph identification, download Google Lens.
It lets you photograph things, and then gives you back an assessment of what it thinks the photograph is of. It can recognize anything from barcodes to flowers to restaurant entrances.
Photos are just the tip of the iceberg, though. Machine learning is also being used for recommendations – whether it’s Netflix informing you of movies you might like, Amazon suggesting products, or Google serving up a list of results based on your search queries.
Speaking of search … voice search and voice recognition is one of the most promising applications for machine learning. This is not at all a futuristic, ten-years-down-the-road sort of application. Even last year, Google reported that 20% of its queries were voice searches. Gartner predicts “30% of searches will be done without a screen by 2020.”
Marketers have massive hopes for machine learning.
80% of marketing executives believe artificial intelligence (which includes machine learning) will “revolutionize” the marketing industry over the next five years.
That’s saying something. But it may not necessarily translate into doing something, as only 10% of the same marketers surveyed are actually using AI.
Even more sobering, only 26% of these marketers are very confident they even understand how AI is used in marketing. (Hopefully reading this article will help you move over into that 26% … if only by a little.)
This issue about marketers being murky about how machine learning really works came up in a different study from TechEmergence. They interviewed 50 executives of machine learning companies, with a particular focus on the marketing industry. These executives say their biggest challenge selling their services is just “demystifying the technology.” And if you look at some of the other answers given (like “people are confused by the technology”), this issue of marketers not really understanding machine learning may be one of the biggest impediments to its adoption.
Despite the confusion, marketers do appear to know which parts of their work AI might help them with:
- 60% of them said AI can give them better insights into their accounts;
- 56% expect it to help them analyze their campaigns better;
- 53% said it will help them identify prospective customers; and
- 53% said it will increase the efficiency of daily tasks (thank you, spam filters).
That’s a bit different than what the vendors think the opportunities are(although it’s not exactly an “apples to apples” comparison). Vendors pick search, “customer segmentation/targeting” and “recommendation engines” as the most promising applications.
Despite all the promise, marketers have plenty of concerns about implementing machine learning or any form of AI:
- 60% are concerned about integrating AI into their existing technology (this matches what the vendors say is a problem with data quality and integration);
- 54% are worried about training their employees;
- 46% fret about interpreting the results; and
- 42% are uneasy about the cost.
Still, marketers are willing to dive in any way, so long as they can be assured of:
- a better close rate for sales (59% said so);
- increased revenue (58%);
- improved traffic and engagement on their websites (54%); and
- a higher conversion rate for leads (52%).
Machine learning may well change the world. None other than Vladimir Putin has said, “The one who becomes the leader in this sphere will be the ruler of the world.”
And so, while it might be confusing at times, and it requires all of us go back and improve the quality of our data, machine learning’s rewards are there. The marketers who can lead in this field might end up ruling their industries.
Back to you
Are you among the one in ten marketers already using machine learning (or any form of AI) in your marketing? Do you have plans – and budget allocated ‒ to implement it next year? Leave a comment and tell us where you’re at on this.