So, what is machine learning? According to Hewlett Packard, “Machine learning refers to the process by which computers develop pattern recognition, or the ability to continuously learn from and make predictions based on data, then make adjustments without being specifically programmed to do so.” In other words, it’s a way for machines to analyze and act on large volumes of information and continue to learn and improve over time. For an example of machine learning algorithms in action, let’s consider facial recognition -- an area we’re seeing improve by the day. Today’s marketers are striving to deliver a relevant message to their customers. And while humans can’t communicate with large volumes of customers individually at scale, machines can. Machine learning can synthesize all the information you have available about a person, such as his past purchases, current web behavior, email interactions, location, industry, demographics, etc., to determine his interests and pick the best products or the most relevant content. Machine learning-driven recommendations learn which items or item attributes, styles, categories, price points, etc., are most relevant to each particular person based on his engagement with the recommendations -- so the algorithms keep improving over time. Even though machine learning allows you to deliver more individually tailored experiences, segmentation remains a valuable tool for marketers. Move from A/B testing to delivering individually relevant experiences and offers. For example, instead of a batch and blast approach to email where you simply send everyone the same email every day, you can use a predictive score generated by machine learning to determine if sending this next email to this particular person will cause them to open, ignore, click or unsubscribe.
Interpret and act on large amounts of information in a scalable way.
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It’s hard to escape the buzz around machine learning. Practically every industry is talking about it.
So, what is machine learning? According to Hewlett Packard, “Machine learning refers to the process by which computers develop pattern recognition, or the ability to continuously learn from and make predictions based on data, then make adjustments without being specifically programmed to do so.” In other words, it’s a way for machines to analyze and act on large volumes of information and continue to learn and improve over time.
For an example of machine learning algorithms in action, let’s consider facial recognition — an area we’re seeing improve by the day. Today, iPhone users unlock their phones with their faces. Law enforcement uses facial recognition to spot fraud activity and catch criminals. Google Photos allows users to sort photos by the people within them. These algorithms may not have been incredibly accurate in the past, but they have been trained over time with machine learning.
This isn’t human intelligence, it’s programmed learning, and its applications extend beyond facial recognition and across industries. Take marketing, for instance. Today’s marketers are striving to deliver a relevant message to their customers. And while humans can’t communicate with large volumes of customers individually at scale, machines can. Not sure what that looks like in practice? In this article, I’ll explain five of the key uses of machine learning for marketing.
1. Recommend the most relevant products or content.
Product and content recommendations have been used by digital marketers for many years. In the past — and occasionally today — these recommendations were manually curated by a human. For the past 10 years, they have often been driven by simple algorithms that display recommendations based on what other visitors have viewed or purchased.
Machine learning can deliver substantial improvements over these simple algorithms. Machine learning can synthesize all the information you have available about a person, such as his past purchases, current web behavior, email interactions, location, industry, demographics, etc., to determine his interests and pick the best products or the most relevant content. Machine learning-driven recommendations learn which items or item attributes, styles, categories, price points, etc., are most relevant to each particular person based on his engagement with the recommendations — so the algorithms keep improving over time.
And machine learning-driven recommendations are not limited to products and content. You can recommend anything — categories, brands, topics, authors, reviews vs. tech specs etc. Using machine learning in this way allows you to create a relevant site or email experience that shows visitors that you truly understand them and helps them find the things they like.
Related: How Machine Learning Is Changing the World — and Your Everyday Life
2. Automatically spot important customer segments.
Even though machine learning allows you to deliver more individually tailored experiences, segmentation remains a valuable tool for marketers. With segmentation, you create groups of prospects or customers based on meaningful differences to better understand those groups. Humans can spot the obvious…
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