Image default
Guest Articles

Think Machine Learning doesn’t have a place in your marketing plan? Think again.

Jon Reilly, Co-founder and COO of Akkio talks about the ways in which ML and data can be integrated to get the most out of your marketing strategies!

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 and energy on them? 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 are just two of the many tasks at which machine learning (ML) excels. As a marketer, when you’re able to automate these kinds of tasks 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 models 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 ML 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 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 online without having to pour over a bunch of websites or wade through an inbox full of Google alerts, you can train an ML 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 ML to Your Marketing Toolkit

ML offers 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 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 2021 and beyond.

Related posts

How AI-powered Data Virtualization Will Drive Automation in Data Integration and Management

Ravi Shankar

Equipping Enterprises with Cyber Threat Intelligence in COVID Times

Anup Nair

The Evolution of AI-powered Telematics

Sid Nair