4 Key Uses of Machine Learning for Marketers

4 Key Uses of Machine Learning for Marketers

It is pretty hard to escape the trend around machine learning. Every industry and business is talking about it.

Machine learning explains the process by which computers develop recognition or the ability to learn from and make decisions based on data and then make changes without being specifically designed to do so. It is a way for machines and devices to analyze and act on certain information and continue to learn, as well as, improve over time.

One example of machine learning algorithms is facial recognition which is an area we are seeing improvements every day. iPhone users unlock their smartphones with their faces. Google Photos allow users to organize photos by the people within them. Law enforcement uses facial recognition to catch criminals and detect fraud activity. The algorithms may not have been accurate in the past, however, they have been trained over time thanks to machine learning.

Machine learning isn’t human intelligence but programmed learning and its applications go beyond facial recognition. For example, marketing. Today’s marketers are focusing on delivering a message to their customers and while people can’t communicate with lots of customers individually, machines can.

Here are four of the key uses of machine learning for marketing:

1.Find and Act on Potential Problems

The Content marketing campaigns generate a lot of information. Just think of the emails your company sends each day, the number of people who visit your online store, etc. All of these interactions generate large volumes of data. There is so much data than a human can’t check it on time.

It may not be obvious to you when something is wrong, for example, when a promotional code doesn’t work or a link is broken, but algorithms can go through all of that data, analyze it, predict what should happen, and inform you if something doesn’t seem right.

Let’s suppose it is Black Friday and one of your emails has an incorrect link. The algorithms can predict the CTR (click-through-rates) or conversion rates that should be expected from the offer and inform you right away if the reality is lower than normal. With that information, you can take action before too much damage has been done on such a busy day of the year.

2. Recommend the Most Relevant Products/Content

Product and content recommendations have been used by marketers for years now. In the past, these recommendations were manually created by a human. They have usually been driven by algorithms that display recommendations based on what other visitors have purchased or seen.

Machine learning can deliver improvements over simple algorithms. It can include all the information you have about a specific buyer, for example, current web behavior, past purchases, location, email interactions, demographics, industry, etc. to determine his interests and choose the best products. Machine learning-driven recommendations learn which products or product attributes, categories, styles, price points, etc. are most relevant to each person based on his engagement.

The best part is that algorithms keep improving and machine learning-driven recommendations aren’t designed to provide products and content only. You can recommend absolutely anything – brands, categories, topics, reviews, authors, and etc. Using machine learning allows you to create a relevant website that shows your visitors that you understand them and helps them find the products they prefer.

3. Know How to Communicate with Each Person

How do you decide where and when to communicate with a customer? Does this customer prefer email, text or push notifications? How often should you talk to your prospects? These are all important questions that machine learning algorithms can answer.

Instead of a boring approach, you can use a predictive score generated by an algorithm to determine if sending an email, for example, will cause the person to ignore, click or unsubscribe. If you are not satisfied with the score, you can wait until you have something better to offer.

It is important to decide how to communicate with each potential buyer.

Know How to Communicate with Each Person

4. Move From A/B Testing to Delivering Personal Experiences

Testing is another area that can be significantly improved with machine learning. If you are familiar with the traditional A/B testing you know that it allows you to run a test between two digital experienced, find the best option, and use that experience in the future.

This is valuable, however, a one-size-fits-all solution is not helpful sometimes. It requires you to choose only one experience to show everyone. This means that many people will not see the experience that is best for them. Luckily, machine learning changes the game.

Rather than manually setting up the test between two experiences, waiting until the test is done and choosing a winner, you can provide the same experiences to a machine learning algorithm. The algorithm will choose the experience at the moment and deliver the best results for each person based on the information available. It will learn from the interactions to inform the decision it makes.

The same approach can be used with offers and promotions. Instead of giving a 20% discount, machine learning can allow you to show the discount only to those who need extra motivation to purchase. For those who don’t need reasons to shop, machine learning can choose another experience, for example, promoting new products in their favorite category.

Machine learning offers a chance for marketers to analyze and act on information. Take the time to discover how your business could benefit from machine learning. Drip your toes in the water with these four areas and go from there.

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FUTURE MARKETING is a platform where you can find not only useful professional tips and reviews on digital marketing, but also useful tips from our research in areas such as Artifical Intelligence, Machine Learning.

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