A Practical Guide, which is available for download.
“Imagine what it would be like to have 1 million marketers at your disposal to understand and engage with every single customer on an individual level. That’s what machine intelligence is like.“
Has marketing technology finally reached the point where machines can do a better job of connecting with customers than people can? C’mon, do I seriously have to answer that?
The reality is obviously “no” – marketing technology by itself cannot connect with customers better than a person can.
However, when you combine a skilled marketer with machine learning, marketers become superhuman. With the rise of the machines, marketers can finally engage on a 1:1 level at the scale required by today’s publishers, retailers and service providers. Machine learning for marketing requires the human component; the machines are an extension of the marketer, not a replacement.
Not that long ago, a new technology called Marketing Automation was getting a lot of buzz (it actually still is). It promised marketers dramatic performance gains if they would loosen their grip on the levers of engagement (gasp!) and let the computers automatically send notifications based on pre-programed triggers and schedules. It was the new-new for marketers, and dozens of platforms emerged, like Marketo, Hubspot, and newer players like Iterable. These platforms allowed for the frequency and timing of email and push campaigns to be personalized based on individual behavior at any scale, while still giving marketers the ability to set the rules governing that cadence.
Today, marketers are hearing a new buzzword. A technology that promises a similar revolution in performance and customer experience, but requires that we relinquish more control over the content and cadence of our communications. Machine Learning is a lot like Marketing Automation, except in this case, the computers write (and continually rewrite) their own rules about which content to send and when.
Machine Learning is transforming the way both consumers and marketers interact with content. It promises to:
- Cut through the noise of massive data sets to find meaningful insights and subtle patterns in real time
- Enable greater personalization by taking the guesswork out of content curation
- Automate processes to save you time
- Lift engagement, reduce churn, and substantially increase the lifetime value of your customers
Seth Godin, I think we as marketers can finally start sending you those “Me-mails” at scale. It was Seth Godin who once said, “I don’t want to get email from anybody; I want to get ‘me-mail’” in a TEDtalk from 2003 (yes, over a decade ago…sorry it took so long, Seth!)
But what exactly is Machine Learning, and why should you care?
“…Think of Machine Intelligence much as you would think of raising a child: the goal is to create something that can make smart decisions when you’re not there.“
A New Flavor of Smart
Practical examples of machine intelligence are rarely as dramatic as self-driving cars or Jeopardy-winning robots. More often, they take the form of Netflix movie suggestions and Amazon product recommendations. Machine Intelligence (MI) is synonymous with artificial intelligence (AI). John McCarthy, who coined the term in 1955, defined AI as “the science and engineering of making intelligent machines.”
This definition isn’t particularly helpful, since it’s hard to define MI or AI without defining intelligence itself, which is an immensely complex and subjective topic. For the the purposes of this paper, think of Machine Intelligence much as you would think of raising a child: the goal is to create something that can make smart decisions when you’re not there. Machine Learning is simply the process by which a machine develops that ability. Like human learning, it involves developing a number of basic skills and then combining and refining them to gradually multiply the power of that intelligence.
While we don’t always exercise it, humans have an immense and unique ability to make those “smart decisions.” We can weigh many different and nuanced variables to determine the right course of action for a given scenario—such as what subject line and what content should go into a promotional email. But we can only see and do so many things at a time.
Machine learning pairs the processing power of computers with powerful, unbiased algorithms. It finds patterns we cannot see ourselves and applies multiple insights simultaneously to solve problems at a scale and speed that we—humans—cannot physically match. By approximating human intelligence in machines, we’re seeking a way to “scale up” our own smarts and even improve our accuracy by bringing more variables into the mix.
“The power to send relevant, timely, and engaging content to every customer, every time, at any scale.“
What Machine Intelligence means for you:
In a word, relevance.
Imagine that your goal is to sell clothing to a 34-year-old woman who lives in Austin, reads the New Yorker, and recently bought a car seat. You could probably do a decent job of writing an email that’s relevant to her, but you’d be making a lot of assumptions that might make the email feel off-target or impersonal.
Now imagine you’re asked to promote that same array of clothing products to your friend Karen. You know all the same things about Karen, but you also know that she always loves to read and share something funny on her lunch break. You know that she bought that car seat as a gift for her brother. That’s the difference between segmentation and 1:1 personalization. 1:1 personalization is sending an email to Karen, not just people like Karen.
But even a close friend might not know that Karen’s on-site behavior fits a pattern that indicates she is likely to consider buying something for herself soon, or that her search history indicates that she’s a budget-conscious shopper with less affinity for brand names, or that her interest in Antarctica was likely an aberration that could be attributed to a pop culture phenomenon and is unlikely to influence her behavior in the long term.
Now imagine Karen is one of a million people who you and your 5-person marketing team want to deliver content to, and you’ll begin to see the potential for Machine Learning. It’s the power to send relevant, timely, and engaging content to every customer, every time, at any scale. Machine Learning gives you the equivalent of a million email marketers all crafting individual emails for every one of your customers or subscribers.
You will learn:
- Why machines are finally good enough to operate without so much oversight from us (Hint: because they have become more like an extension of us.)
- Understanding the differences (and similarities) between Machine Learning & Predictive Analytics
- Machine Intelligence helps you cut through the noise by keeping your messaging relevant and timely for each user at an unlimited scale.