4 Ways Modern Marketers Are Leveraging Machine Learning

Four Ways Marketers Can Use Machine Learning

For examples of real-life applications of the benefits of Machine Learning, let’s look at four specific ways Machine Learning can help marketers put data to use for more effective—and efficient—marketing results: segmentation, lead scoring, pay-per-click advertising, and dynamic pricing.  

1. Segmentation

Even in the days when direct mail dominated, list segmentation mattered. Not everyone should get the same message, and marketers have to segment their lists to ensure the right message gets to the right person. With the vast amounts of data, we have today; segmentation is theoretically easier. We can know where people live, how old they are, whether or not they own a home, the kind of car they drive, if they’re married or even expecting a baby, their income range, and much, much more. And Machine Learning can turn all of that data into insight that leads to targeted marketing messages.  

But there’s more: Machine Learning can go deeper to distinguish between high-value low-value customers. It can find clusters of your best customers. It can determine which products to recommend to which customer groups. It can help you predict buying behavior based on criteria you would never have thought to consider. And it can do much more with all your data than you ever could on your own.

2. Lead Scoring

In addition to getting to know your prospects and customers on a deeper level so you can segment, so too must marketers be able to score leads to know how to proceed. This is especially true in the B2B space. And Machine Learning can help. As Bernard Marr says in Forbes, “Not only can machine help with the original gathering of information of lead generation, AI can analyze unstructured data such as emails, phone calls, and social posts to then determine patterns and define who is a good prospect. This info is vital for effective marketing campaigns.”

3. Pay-per-click Advertising

Successful pay-per-click (PPC) requires starting with a human-defined strategy but then changing bids as needed based on data.

“If there is one area that machines dominate, it’s bidding,” said top AdWords influencer and author, Brad Geddes, in his Simplilearn webinar Man vs. Machine: How to Future-proof Your PPC Job. As Geddes points out, bidding is based on pattern recognition and statistics, making it one of the best uses of Machine Learning in marketing. Machines can also determine how a consumer might interact with an ad based on previous behavior and the statistics for that particular combination of keyword, advertisements, and landing page.

4. Dynamic Pricing

Machine Learning also enables dynamic pricing online. Prices can change in an instant based on factors like supply, demand, competitor pricing, the consumer’s interest level, and prior engagements through previous marketing. Airlines and ride-sharing companies are two examples of business models using dynamic pricing to maximize revenue.

Incorporate Machine Learning into Your Marketing Strategy

To learn more about ways marketers can employ Machine Learning to do more with less and maximize results, download the whitepaper - How Machine Learning Can Make Any Business More Competitive. In it, you’ll find an overview of the types of data you’re probably already collecting, methods for storing those huge amounts of data, and specific ways to use that data for a competitive advantage in business processes, marketing, and customer service.

Regardless of how you put it to use as part of your marketing efforts, Machine Learning will help you save time, money, and resources by doing more than humans alone possibly can and by identifying opportunities as well as pitfalls that you otherwise might not see or act on.