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Facts vs. Fiction: AI in Digital Marketing


Imagine if your digital marketing tools had the capacity to predict the future. What would you do with that crystal ball? How about quoting the most likely price to incentivize a purchase? Or providing each user a set of search results that have shown to be the most likely to yield a conversion?

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learningRecommending a product through a web campaign that can be most effective to prompt an engagement? How about selecting the best ad for a specific user with the highest propensity for clicks?

This is where is most effective for digital marketers.

Unfortunately, a large portion of online literature focused on is either fixated on a Matrix-like singularity or mind-numbing automation that can barely qualify as intelligent. In fact, the number of times I am introduced to in commercial products that later turns out to be pure automation is astounding.

What’s more, as you will read below, calling these tools is not technically wrong. At the end of the day, a wide range of products and solutions leverage in innovative and exciting ways. These applications are using state-of-the-art algorithms to make a real impact and are showing results.

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

Artificial Intelligence vs. Machine Learning vs. Deep Learning

To start, we must first understand the difference between three key concepts that unfortunately often get used interchangeably: , and .

Artificial intelligence is quite simply when a machine mimics intelligent behavior such as problem solving. could be as mundane as an algorithm using an “if-else” statement. Frankly, any application or product today can say it leverages and they would be correct.

Machine learning is a subset of , but has been around since the end of the 20th century. algorithms are algorithms that can learn from data. These algorithms focus on analyzing large sets of data, learning from that analysis and providing insight based on that learning. These types of algorithms also tend to improve as they are exposed to more and more data. algorithms vary in their type and purpose. Some of the most popular include classification algorithms, clustering algorithms and regression algorithms.

Deep learning is a subset of . It’s a newer entry to the field, maturing at the onset of the 21st century. Deep learning algorithms focus on identification and classification of patterns in a way that mimics the processes used in the human brain, hence they are referred to as neural networks. Deep learning is responsible for the recent boom in over the last decade, from self-driving cars to the first picture of a black hole. […]

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