Would You Like To Play A Game? Using Machine Learning to Predict Customer Behavior

The Next Move

So let’s talk about machine learning and marketing.

Thanks to machine learning and AI, marketers are able to digest a ton of information and data and use that to make smarter decisions. No, you obviously don’t have to be a NORAD computer to understand how to make the next move, but a machine could help avoid a marketing strategy that’s a real bomb.

Why did machine learning make such a huge impact on marketing? Consider the development of marketing in the past few decades. Marketing has always been about finding customers’ wants and desires (or creating those wants and desires). Traditional avenues of achieving that included focus groups and surveys. While those are both still used, we now can get real-time information on customers and their habits thanks to the rise of online tracking.

With technological progress, marketers have been able to gather more and more data on customers and their behaviors. However, we’re talking about a metric ton of data coming in at every moment. Consider all the sources of information marketers can now rely on:

• Email marketing
• Social media behavior
• Search behavior
• Call tracking
• App usage
• UTM codes
• And more!

More so than at any other point in human history, marketers know exactly what people want. In fact, thanks to behavioral analysis, sometimes marketers know what people want before the customers even realize it!

So how do marketers sort through all this information to make decisions? They use machine learning and AI to make this process manageable!

Master of Games

More and more often machine learning is helping marketers make better choices on the next move. While humans are undoubtedly still calling the shots, machine learning helps them make better decisions.

Consider a Facebook ad campaign. You can target by interests and manually choose which population to target. This can be helpful, but thanks to machine learning advances, you can also target things like lookalike audiences. In this scenario, lookalike audiences are similar to people who visit your website. Facebook looks at things like interests, behaviors and more to establish this audience, expanding your reach for qualified leads.

Another great example of machine learning is automatically generated music playlists, like the ones put together by Spotify and Pandora. In fact, automatically suggesting music based on your preferences is one of the things that has kept Pandora around for so long. It’s hard to remember these days since it seems like every competitor has a similar service, but Pandora was a pioneer in music recommendations because it used machine learning to dissect music, categorizing everything from a song’s tempo to the instruments used. It then took this information and recommended music based on what the user specified they enjoyed. Spotify’s custom daily playlists also use a similar system to recommend music.

Then there is Pega. Pega takes machine learning to a whole new level, allowing marketers to reach individual customers with personalized marketing plans. What information to present to the customer, what products to market, what content to show is all determined in real-time. Using machine learning, this individualized marketing solution is constantly updated and recalculated by machine learning depending on shifts in customer behavior and market changes.

In other words, Pega could probably run circles around that NORAD computer from WarGames. When it comes to thermonuclear war, yes, the best way to win is to not play, but when it comes to marketing? If you’re not using machine learning, you might as well not be playing!

Interested in learning how Pega can be used to take your machine learning-based marketing solutions to the next level? Contact Hoverstate today! We are an experienced Pega partner and have helped businesses big and small benefit from the power of machine learning and AI.