Hollywood paints a grim picture of a future populated by intelligent machines. Terminator, 2001: A Space Odyssey, The Matrix and countless other films show us that machines are angry, they’re evil and — if given the opportunity — they will not hesitate to overthrow the human race.
Films like these serve as cautionary tales about what could happen if machines gain consciousness (or some semblance of). But in order for that to happen humans need to teach machines to think for themselves. This may sound like science fiction but it’s an actual discipline known as machine learning.
The machines are coming. But fear not — they could help you become a better marketer. Image via Shutterstock
Still in its infancy, machine learning systems are being applied to everything from filtering spam emails, to suggesting the next series to binge-watch and even matching up folks looking for love.
For digital marketers, machine learning may be especially helpful in getting products or services in front of the right prospects, rather than blanket-marketing to everyone and adding to the constant noise that is modern advertising. Machine learning will also be key to predicting customer churn and attribution: two thorns in many digital marketers’ sides.
Despite machine learning’s positive impact on the digital marketing field, there are questions about job security and ethics that cannot be swept under the rug. Will marketing become so automated that professional marketers become obsolete? Is there potential for machine learning systems to do harm, whether by targeting vulnerable prospects or manipulating people’s emotions?
These aren’t just rhetorical questions. They get to the heart of what the future of marketing will look like — and what role marketers will play in it.
What is Machine Learning?
Machine learning is a complicated subject, involving advanced math, code and overwhelming amounts of data. Luckily, Tommy Levi, Director of Data Science at Unbounce, has a PhD in Theoretical Physics. He distills machine learning down to its simplest definition:
You can think of machine learning as using a computer or mathematics to make predictions or see patterns in data. At the end of the day, you’re really just trying to either predict something or see patterns, and then you’re just using the fact that a computer is really fast at calculating.
You may not know it, but you likely interact with machine learning systems on a daily basis. Have you ever been sucked into a Netflix wormhole prompted by recommended titles? Or used Facebook’s facial recognition tool when uploading and tagging an image? These are both examples of machine learning in action. They use the data you input (by rating shows, tagging friends, etc.) to produce better and more accurate suggestions over time.
Other examples of machine learning include spell check, spam filtering… even internet dating — yes, machine learning has made its way into the love lives of many, matching up singles using complicated algorithms that take into consideration personality traits and interests.
Machine learning may be helpful in getting products or services in front of the right prospects.
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How Machine Learning Works
While it may seem like witchcraft to the layperson, running in the background of every machine learning system we encounter is a human-built machine that would have gone through countless iterations to develop.
Facebook’s facial recognition tool, which can recognize your face with 98% accuracy, took several years of research and development to produce what is regarded as cutting-edge machine learning.
So how exactly does machine learning work? Spoiler alert: it’s complicated. So without going into too much detail, here’s an introduction to machine learning, starting with the two basic techniques.
Supervised learning systems rely upon humans to label the incoming data — at least to begin with — in order for the systems to better predict how to classify future input data.
Gmail’s spam filter is a great example of this. When you label incoming mail as either spam or not spam, you’re not only cleaning up your inbox, you’re also training Gmail’s filter (a machine learning system) to identify what you consider to be spam (or not spam) in the future.
Unsupervised learning systems use unlabeled incoming data, which is then organized into clusters based on similarities and differences in the data. Whereas supervised learning relies upon environmental feedback, unsupervised learning has no environmental feedback. Instead, data scientists will often use a reward/punishment system to indicate success or failure.
According to Tommy, this type of machine learning can be likened to the relationship between a parent and a young child. When a child does something positive they’re rewarded. Likewise, when “[a machine] gets it right — like it makes a good prediction — you kind of give it a little pat on the back and you say good job.”
Like any child (or person for that matter), the system ends up trying to maximize the positive reinforcement, thus getting better and better at predicting.
The Power of Machine Learning
A lot of what machine learning can do is yet to be explored, but the main benefit is its ability to wade through and sort data far more quickly and efficiently than any human could, no matter how clever.
Tommy is currently experimenting with an unsupervised learning system that clusters landing pages with similar features. Whereas one person could go through a few hundred pages in a day, this model can run through 300,000 pages in 20 minutes.
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The advantage is not just speed, it’s also retention and pattern recognition. Tommy explains:
To go through that many pages and see those patterns and hold it all in memory and be able to balance that — that’s where the power is.
For some marketers, this raises a troubling question: If machine learning systems solve problems by finding patterns that we can’t see, does this mean that marketers should be worried about job security?
The answer is more nuanced than a simple yes or no.
Machine Learning and the Digital Marketer
As data becomes the foundation for more and more marketing decisions, digital marketers have been tasked with sorting through an unprecedented amount of data.
This process usually involves hours of digging through analytics, collecting data points from marketing campaigns that span several months. And while focusing on data analysis and post-mortems is incredibly valuable, doing so takes a significant amount of time and resources away from future marketing initiatives.
As advancements in technology scale exponentially, the divide between teams that do and those that don’t will become more apparent. Those that don’t evolve will stumble and those that embrace data will grow — this is where machine learning can help.
Marketers that don’t embrace data will fumble. Those that do will grow — ML can help.
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That being said, machine learning isn’t something digital marketers can implement themselves after reading a quick tutorial. It’s more comparable to having a Ferrari in your driveway when you don’t know how to drive standard… or maybe you can’t even drive at all.
Until the day when implementing a machine learning system is just a YouTube video away, digital marketers could benefit from keeping a close eye on the companies that are incorporating machine learning into their products, and assessing whether they can help with their department’s pain points.
So how are marketers currently implementing machine learning to make decisions based on data rather than gut instinct? There are many niches in marketing that are becoming more automated. Here are a few that stand out.
Lead scoring and machine learning
Lead scoring is a system that allows marketers to gauge whether a prospect is a qualified lead and thus worth pursuing. Once marketing and sales teams agree on the definition of a “qualified lead,” they can begin assigning values to different qualified lead indicators, such as job title, company size and even interaction with specific content.
These indicators paint a more holistic picture of a lead’s level of interest, beyond just a form submission typically associated with lead generation content like ebooks. And automating lead scoring takes the pressure off marketers having to qualify prospects via long forms, freeing them up to work on other marketing initiatives.
Once the leads have reached the “qualified” threshold, sales associates can then focus their efforts on those prospects — ultimately spending their time and money where it matters most.
Content marketing and copywriting
Machine learning models can analyze data points beyond just numbers — including words on your website, landing page or PPC ads. Machine learning systems can find patterns in language and detect words that elicit the most clicks or engagement.
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But can a machine write persuasive copy? Maybe, actually.
A New York-based startup called Persado offers a “cognitive content platform” that uses math, data, natural language processing, emotional language data and machine learning systems to serve the best copy and images to spur prospects into action. It does this by analyzing all the language data each client has ever interacted with and serving future prospects with the best possible words or phrases. An A/B test could never achieve this at the same scale.
Think this is a joke? With over $65 million in venture capital and a reported average conversion rate uplift of 49.5% across 4,000 campaigns, Persado’s business model is no laughing matter.
Still, there is no replacement for a supremely personalized piece of content delivered straight to your client’s inbox — an honest call to action from one human to another.
Recently Unbounce’s Director of Campaign Strategy, Corey Dilley, sent an email to our customers. It had no sales pitch, no call to action button. It was just Corey reaching out and saying, “Hey.”
Corey’s email had an open rate of 41.42%, and he received around 80 personal responses. Not bad for an email written by a human!
Sometimes it’s actions — like clicks and conversions — you want to elicit from customers. Other times the goal is to build rapport. In some cases we should let the machines do the work, but it’s up to the humans to keep the content, well, human.
There is no replacement for personalized content and an honest ask from one human to another.
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Machine learning for churn prediction
In the SaaS industry, churn is a measure of the percentage of customers who cancel their recurring revenue subscriptions. According to Tommy, churn tells a story about “how your customers behave and feel. It’s giving a voice to the customers that we don’t have time or the ability to talk to.”
Self-reporting methods such as polls and surveys are another good way to give a voice to these customers. But they’re not always scalable — large data sets can be hard for humans to analyze and derive meaning from.
Self-reporting methods can also skew your results. Tommy explains:
The problem with things like surveys and popups is that they’re only going to tell you what you’ve asked about, and the type of people that answer surveys are already a biased set.
Machine learning systems, on the other hand, can digest a larger number of data points, and with far less bias. Ideally the data is going to reveal what marketing efforts are working, thus leading to reduced churn and helping to move customers down the funnel.
This is highly relevant for SaaS companies, whose customers often sign up for trials before purchasing the product. Once someone starts a trial, the marketing department will start sending them content in order to nurture them into adopting the service and become engaged.
Churn models can help a marketing team determine which pieces of content lead to negative or positive encounters — information that can inform and guide the optimization process.
Ethical Implications of Machine Learning in Marketing
We hinted at the ethical implications of machine learning in marketing, but it deserves its own discussion (heck, it deserves its own book). The truth is, machine learning systems have the potential to cause legitimate harm.
According to Carl Schmidt, Co-Founder and Chief Technology Officer at Unbounce:
Where we are really going to run into ethical issues is with extreme personalization. We’re going to teach machines how to be the ultimate salespeople, and they’re not going to care about whether you have a compulsive personality… They’re just going to care about success.
This could mean targeting someone in rehab with alcohol ads, or someone with a gambling problem with a trip to Las Vegas. The machine learning system will make the correlation, based on the person’s internet activity, and it’s going to exploit that.
Another dilemma we run into is with marketing aimed at affecting people’s emotions. Sure copywriters often tap into emotions in order to get a desired response, but there’s a fine line between making people feel things and emotional manipulation, as Facebook discovered in an infamous experiment.
If you aren’t familiar with the experiment, here’s the abridged version: Facebook researchers adapted word count software to manipulate the News Feeds of 689,003 users to determine whether their emotional state could be altered if they saw fewer positive posts or fewer negative posts in their feeds.
Posts were deemed either positive or negative if they contained at least one positive or negative word. Because researchers never saw the status updates (the machine learning system did the filtering) technically it fell within Facebook’s Data Use Policy.
However, public reaction to the Facebook experiment was generally pretty scathing. While some came to the defense of Facebook, many criticized the company for breaching ethical guidelines for informed consent.
In the end, Facebook admitted they could have done better. And one good thing did come out of the experiment: It now serves as a benchmark for when machine learning goes too far, and as a reminder for marketers to continually gut-check themselves.
For Carl, it comes down to intent:
If I’m Facebook, I might be worried that if we don’t do anything about the pacing and style of content, and we’re inadvertently presenting content that could be reacted to negatively, especially to vulnerable people, then we would want to actively understand that mechanism and do something about it.
While we may not yet have a concrete code of conduct around machine learning, moving forward with good intentions and a commitment to do no harm is a good place to start.
The Human Side of Machine Learning
Ethical issues aside, the rise of machines often implies the fall of humans. But it doesn’t have to be one or the other.
“You want machines to do the mundane stuff and the humans to do the creative stuff,” Carl says. He continues:
Computers are still not creative. They can’t think on their own, and they generally can’t delight you very much. We are going to get to a point where you could probably generate highly personal onboarding content by a machine. But it [will have] no soul.
That’s where the human aspect comes in. With creativity and wordsmithing. With live customer support. Heck, it takes some pretty creative data people to come up with an algorithm that recognizes faces with 98% accuracy.
Imagine a world where rather than getting 15 spam emails a day, you get just one with exactly the content you would otherwise be searching for — content written by a human, but served to you by a machine learning system.
While pop culture may say otherwise, the future of marketing isn’t about humans (or rather, marketers) versus machines. It’s about marketers using machines to get amazing results — for their customers and their company.
Machine learning systems may have an edge when it comes to data sorting, but they’re missing many of the things that make exceptional marketing experiences: empathy, compassion and a true understanding of the human experience.
Editor’s note: This article originally appeared in The Split, a digital magazine by Unbounce.
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Marketing Machines: Is Machine Learning Helping Marketers or Making Us Obsolete?