Machine learning in marketing: A practical overview

It’s one thing to say you’re moving toward data-driven marketing. But it’s quite another to actually set yourself up for data-driven success.

Despite the fact that 64% of marketing leaders see data-driven decisions as crucial to success, 87% of teams see data as their most underutilized asset.

Using machine learning in marketing is how we can change this trend. More and more, marketers are turning to machine learning to improve all kinds of strategic efforts, from enhancing customer experience to increasing customer satisfaction, identifying product development opportunities, reducing churn and beyond.

There’s just one problem—machine learning is incredibly complicated. You can’t just wake up one morning, decide to implement machine learning in marketing, and expect to optimize results overnight.

If you’re just starting out with machine learning in marketing, you need to cut through all the hype and figure out how to make the most of this technology. (Hint: it’s all about having the right data, developing the right technology stack, and finding ways to apply it to your operations.)

What is machine learning in marketing?

According to Karen Hao, artificial intelligence reporter for MIT Technology Review: “Machine-learning algorithms use statistics to find patterns in massive amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you. If it can be digitally stored, it can be fed into a machine-learning algorithm… Frankly, this process is quite basic: find the pattern, apply the pattern. But it pretty much runs the world.”

Marketers have always had the potential to collect a large amount of data. You have so much customer data at your fingertips and machine learning in marketing is all about finding ways to generate more insights from that information.

With the help of analytical techniques like data mining and predictive analytics, machine learning algorithms help you build autonomous marketing.

By identifying the patterns in your customer data and continuously adapting to those patterns, machine learning replaces hundreds of traditional, manual marketing models that once kept you from scaling your operations.

No matter your specific strategy, marketing success comes down to surfacing the right offer, at the right time, on the right channel to each customer. Machine learning in marketing is the key to finding that success—but only if you’re able to fuel algorithms with the right data.

How data inputs impact machine learning in marketing

Machine learning in marketing is very much predicated on the “garbage in, garbage out” concept. It doesn’t matter how much money you’ve invested in machine learning solutions and customer data. If the data inputs for your machine learning algorithms are inaccurate, low-quality, or otherwise flawed, your outcomes won’t lead to data-driven marketing success.

This is especially problematic in marketing. We might have more data available to us than ever before, but how are we doing when it comes to preparing that data for machine learning? One study found that 92% of marketers struggle to make the most of customer data due to challenges of access, unification and analysis.  

The problem is that we have so many unique data inputs to manage. Customer data comes from all angles, including:

  • Records updated in the CRM
  • Point of sale and eCommerce transactional data
  • Marketing automation campaign engagement
  • Customer service ticket information
  • Social media interactions
  • Website visitor browsing data
  • Pay-per-click advertising information

Machine learning in marketing requires you to unify all of these channels to give algorithms an opportunity to identify larger behavioral patterns. But from a technical perspective, this has always been easier said than done.

This is why it’s so crucial to find the right technology stack. Verifying data accuracy, unifying all of your marketing channels and generating actionable insights with machine learning algorithms becomes significantly easier when you’re not trying to do it all manually and on your own.

Solving challenges with data input should be your first step when implementing machine learning in marketing. Then, once you have your technology stack, you can start to think about the specific use cases you’ll want to make the most of.

4 use cases for machine learning

You’ve cleaned up your data inputs. You’ve chosen solutions to implement machine learning in your marketing efforts. And now you need to know what you can actually do with those new solutions.

Building autonomous marketing operations with machine learning has plenty of benefits. But the following 4 use cases are the most common ways to take advantage of this technology:

1. Improve personalization and targeting:

Instead of blasting marketing messages to anyone and everyone, machine learning algorithms will help identify which customers are most valuable to you.

And not only that, but machine learning can also generate insights to help you personalize messages to specific segments of customers and prospects.

2. Reduce customer churn:

It’s far more cost effective to retain existing customers than to constantly acquire new ones. Marketers have always tracked churn, but machine learning offers the ability to identify patterns in when customers tend to leave, what behaviors lead to churn, how churn impacts performance, ways you can prevent churn and more.

3. Understand customer lifetime value:

Machine learning allows you to find patterns in behavior that indicate greater customer lifetime value (CLTV) and gain deeper understanding of what factors contribute to customer value.

4. Optimize customer attribution:

With so many channels to track and analyze, it’s harder than ever to understand customer attribution. Machine learning goes beyond simple customer journey mapping to analyze patterns in chains of events that lead to a customer taking your desired action (sale, email sign-up, application, etc.). While machine learning isn’t without bias, it can take a lot of human error out of traditional attribution tracking.

These are just a few of the ways that you can take advantage of machine learning in marketing. However, once you’ve built a foundation with the right data and the right technology stack, you can start to apply machine learning in unique and innovative ways.

Right now, there is so much hype surrounding machine learning in marketing that it can be difficult to find a practical path forward. 

If you want to learn how you can put human interaction at the center of your marketing (and power it with machine learning technology), contact us today and find out how we can help.