Facts vs. Fiction: Artificial Intelligence in Digital Marketing

Imagine that your digital marketing tools can predict the future. What would you do with this crystal ball? And what’s the most likely price to encourage a purchase? Or does each user provide a set of search results that can lead to conversions? Are you recommending a product through a web campaign that can be more effective in promoting engagement? How about choosing the best ad for a particular user with the highest throughput?

This is where AI is best for professional digital marketing marketers.

Unfortunately, much of the online literature that focuses on AI is centuries old, in keeping with the Matrix or the incredible automation that can hardly be qualified as intelligent. The number of times I have found AI in commercial products is impressive, which later turns out to be pure automation.

As you will read below, it is not technically wrong to mention these AI tools. Finally, a wide variety of products and solutions leverage artificial intelligence in innovative and empowering ways. These applications use the most advanced AI algorithms to make a real impact and show results.

Let’s take a look at how today’s marketing leaders can identify and use these great tools.

Artificial intelligence versus machine learning versus deep learning

To begin with, we must first understand the difference between three key concepts that are unfortunately often used interchangeably: artificial intelligence, machine learning, and deep learning.

AI is simply when a machine mimics intelligent behavior such as debugging. Artificial intelligence can occur with the same frequency as an algorithm with a “contradictory” explanation. Frankly, any application or product these days can say it uses artificial intelligence, and that would be fair.

Machine learning is part of artificial intelligence, but it has been around since the early 20th century. Machine learning algorithms are artificial intelligence algorithms that can learn from data. These algorithms are designed to analyze, learn and deliver large amounts of data. These types of algorithms also tend to improve as they are exposed to more and more data. ML algorithms differ in type and purpose. Among the most popular are classification algorithms, cluster algorithms, and regression algorithms.

Deep learning is a subset of machine learning. It is a new entrance to the area that is aging at the beginning of the 21st century. Deep learning algorithms aim to identify and classify patterns in a way that mimics the processes used in the human brain, which is why they are called neural networks. Deep learning is responsible for the recent boom in machine learning over the past decade, from autonomous cars to the first image of a black hole.

What about natural language processing?

Another source of innovation and advancement in AI is natural language processing. In natural language processing, machines apply linguistics to analyze grammatical structures to understand human language. We all do this every day in NLP when we use our voice assistants like Amazon Echo or Google Home. We also find NLP with chatbots on websites and mobile apps, as well as grammar help on our phones and computers to correct mistakes and apply the advice.

Although NLP is also a subset of artificial intelligence, it can experience machine learning and deep learning when we use algorithms to improve a program’s ability to discern human language. For example, a smartphone keyboard would recommend the following words, not only based on the grammatical rules of English but also based on what you typed before.

Digital applications and artificial intelligence

Now that we understand these concepts and how they are used, let’s talk about some cases where digital marketing involves artificial intelligence.


In terms of digital marketing and artificial intelligence, advertising is by far the most successful use case. Two of the most popular platforms in the world, Google and Facebook, depending on most of their revenue. AI is critical for these (and other) platforms because it determines the best audience for a given ad, allowing the advertiser to reach the users most likely to deliver the most conversions. The use of artificial intelligence in advertising has been so successful because it has generated the largest investment and the highest financial return.


Research and artificial intelligence are a perfect match, as research is only effective when the user finds what they are looking for. It sounds like a simple tip, but search engines often strive to provide relevant results to all users. Artificial intelligence can help research tools to make better decisions when ranking results, especially based on search terms and analysis of results. Research AI, also known as cognitive research, has made tremendous progress in recent years and has shown a real impact on content research and e-commerce.

Content management (creation and management)

Today’s AI algorithms are not advanced enough to write the following fictional story or thought-provoking opinion, but that doesn’t mean they can’t write. There are many commercial products used by well-known organizations that assist in writing content, especially if it is managed by a model with basic rules and restrictions. In fact, these types of use cases have a specific NLP segment called NLG (Natural Language Generation).

Artificial intelligence can also be used to gather and personalize content. Artificial intelligence can help users map and provide relevant and personalized experiences after learning from user behavior and historical data. In fact, a machine learning algorithm can go even further than providing personalized content and personalized paths for users, which is considered the Holy Grail for marketers.

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