Published on

January 8, 2024

Digital Marketing

Machine Learning and Digital Marketing – the Perfect Match

ML offers digital marketers the ability to dig deeper into data and identify patterns to help them better target their customers.
Craig Wisneski
Co-Founder & Head of G&A, Akkio
Digital Marketing

In marketing, no matter what industry you’re in or how long you’ve been there, the challenge remains the same: Find new ways to stay ahead of the competition, get more eyes on your product and/or service, and drive brand recognition – with the ultimate goal of increasing sales.

With that in mind, wouldn’t it be wonderful to know which of your website visitors were most likely to make a purchase so you could focus your time, energy, and email marketing on them? If you could deliver personalized experiences and make perfect product recommendations in real time? And wouldn’t it also be wonderful if you could locate negative reviews quickly and easily so you could address the issues, build trust, and enhance your brand’s reputation? 

Predicting purchase behavior and text processing (also known as natural language processing) are just two of the many tasks at which machine learning (ML) technology excels. As a digital marketer, when you’re able to automate marketing campaigns with ML, you empower the humans on your team to be more effective, and probably happier too.

What Is Machine Learning?

Most people are already familiar with machine learning, they just might not know it. For example, Amazon and Netflix use ML to recommend movies based on what viewers have previously watched. And Spotify uses ML to select new songs based on what you’ve listened to in the past. 

To do this, these companies had to create Machine Learning models that identify patterns in people’s behavior. That’s the most important thing to understand about ML models: At their core, they are pattern-matching machines. 

Of course, those models have to learn how to spot and then match a particular pattern. That’s the learning part of machine learning, and it’s what differentiates ML from “traditional” rules-based systems. With rules-based workflows, a programmer has to say, “If this happens, then do that.”

With our Netflix example, that means the if/then programming would say something like, “If a customer watched ‘When Harry Met Sally,’ show that customer ‘Sleepless in Seattle’ and ‘You’ve Got Mail.’” Considering that there are thousands of movies, it would take years for humans to do “if/then” programming to facilitate this kind of pattern-matching. But computers can do it fast, once we teach them how to spot the pattern.

Can I Make Machines Learn?

Machine learning used to be the exclusive domain of companies with deep pockets—and plenty of time—to invest in technology. But now, there are several platforms on the market with simple user experiences that make it really easy for “non-coders” to use ML, including marketers, salespeople, and customer success teams. Non-techies can use these platforms just like any other software-as-a-service (SaaS). They’re intended to be as user-friendly as Mailchimp or SurveyMonkey, for example, and they can be quite affordable, too.

To use one of these platforms, all you need is data that contains the pattern you’d like the ML model to spot. For instance, if you want to predict which of your website visitors are most likely to make a purchase, you’ll need to train an ML model on past website purchasers’ behavior. This could include things like how many times a person visited before buying, which pages they viewed, whether they downloaded any freebies or signed up for your email list, and even what they bought. 

Likewise, if you want to identify negative reviews across social media without having to pour over a bunch of websites or wade through an inbox full of Google alerts, you can train a Machine Learning model to do that mind-numbing work for you. It starts by teaching the ML model the difference between positive and negative feedback specific to your brand.

Will My Data Work?

Truth be told, you probably already have the kind of data you need to train an ML model. The most important thing is that the data actually contains the pattern you are hoping to predict or identify. 

Also, each record in your dataset will need to be correctly labeled or tagged. If your data is not tagged, you will need to tag it (manually or via an auto-tagging service) before you can start training the ML model.

Finally, you will need enough data to train the model. How much is enough depends on the complexity of the problem, and the results you’re trying to achieve. But as a rule of thumb, more data usually means better results. 

If you have a relatively straightforward classification problem, like identifying positive vs. negative feedback, you probably can get away with as few as 100 records, according to our experience. On the other hand, if you are hunting for a needle in a haystack—such as detecting credit card fraud that has an incidence rate of 1 in 1,000 transactions—you will need 50,000 to 100,000 records to train the ML model. 

Adding Machine Learning to Your Marketing Toolkit

Machine Learning offers digital marketers the ability to dig deeper into their data and identify patterns that will help them better target their customers and prospects. Armed with this information, they’re able to create more effective campaigns and drive a greater return on their overall content marketing efforts.

In the digital age when it’s so important to reach targets better and faster than the day before, there isn’t a marketer alive that wouldn’t want to have this information in their back pocket. Adding ML to their engagement plans is the way marketers will take the lead over their competition – in 2022 and beyond. 

Let's consider 7 ways machine learning will create better marketing:

1. Increased lead conversion rates
2. Reduced churn
3. Optimized marketing content
4. Higher customer LTVs
5. Bigger deal sizes
6. More renewals
7. Higher order values

Augmented lead scoring

The ability to identify when a lead is “hot” or “cold” has been the Holy Grail of marketing for years. Machine learning can help you automatically score leads based on their behavior, so you know which ones are more likely to convert.

Predictive churn prevention

Churn is the silent marketing killer. It’s responsible for billions of dollars in lost revenue each year, and once someone stops using your product it’s very hard to win them back. Machine learning can help you predict which customers are at risk of churning, so you can take steps to prevent it before it happens.

Content optimization

The ability to automatically test and optimize your marketing content is another huge benefit of using machine learning. Instead of relying on gut instinct or A/B testing, which can be slow and expensive, you can let the data tell you what works best.

Improved customer LTVs

Lifetime value (LTV) is the measure of how much a customer is worth to your business over their lifetime. Obviously, the higher your LTV, the better. With machine learning, you can segment your customers based on their past behavior and target them with customized messages that will keep them engaged and spending money with your company.

Bigger deal sizes

When you have a better understanding of your customer’s needs and pain points, you can sell them more effectively. That means bigger deal sizes and a higher rate of close.

More renewals

The key to any successful subscription business is keeping your customers happy enough to stick around for the long haul. With machine learning, you can identify at-risk customers and take steps to keep them engaged. That could mean proactively sending them helpful content or giving them a personal call from a member of your team. Whatever it takes to keep them happy and paying month after month.

Higher order values

If you can upsell or cross-sell effectively, you can boost your order values and increase your profits without having to acquire new customers. With machine learning, you can target current customers with personalized product recommendations based on their past behavior.

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