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? Recommending 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 artificial intelligence is most effective for digital marketers.
Unfortunately, a large portion of online literature focused on AI 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 artificial intelligence in commercial products that later turns out to be pure automation is astounding.
What’s more, as you will read below, calling these tools AI is not technically wrong. At the end of the day, a wide range of products and solutions leverage artificial intelligence in innovative and exciting ways. These applications are using state-of-the-art AI 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: artificial intelligence, machine learning and deep learning.
Artificial intelligence is quite simply when a machine mimics intelligent behavior such as problem solving. AI could be as mundane as an algorithm using an “if-else” statement. Frankly, any application or product today can say it leverages AI and they would be correct.
Machine learning is a subset of artificial intelligence, but has been around since the end of the 20th century. ML algorithms are AI 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. ML 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 machine learning. 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 machine learning over the last decade, from self-driving cars to the first picture of a black hole.
What About Natural Language Processing?
Another source of innovation and advancement in the AI arena is natural language processing. Natural language processing is when machines apply linguistics to analyze grammatical structure in order to understand the human language. All of us brush up against NLP on a daily basis when we use our voice assistants like Amazon Echo or Google Home. We also encounter NLP with chatbots on websites and mobile apps as well as grammar assistance on our phones and computers to correct errors and apply recommendations.
While NLP is also a subset of artificial intelligence, it can slice across machine learning and deep learning when we use algorithms designed to learn to improve a program’s ability to discern human language. For example, a smartphone’s keyboard will recommend your next words not only based on English language grammatical rules, but based on what you have previously typed.
Digital Applications and Artificial Intelligence
Now that we understand these concepts and how they are used, let’s talk about some of the use cases where digital marketing embraces artificial intelligence.
When it comes to digital marketing and AI, advertising has been the most successful use case by far. Two of the most popular platforms in the world, Google and Facebook, rely on advertising for the majority of their revenue. AI is critical to those (and other) platforms because it determines the best audience for a specific ad, thus helping the advertiser to target users who are most likely to convert. The use of AI in advertising has been so successful because it attracts the most investment and the largest financial returns.
Search and AI are a perfect pairing because search is only effective if the user is given what they are searching for. That sounds like an easy proposition but search tools in general struggle to deliver relevant results for all users. AI can help search tools make better decisions in results ranking especially based on search key term and result analysis. AI in search, often referred to as cognitive search, has made tremendous strides over the last few years, showing true impact on content and ecommerce search.
Content Management (Creation and Curation)
Today’s AI algorithms are still not advanced enough to write the next #1 seller fiction story or a provocative Op-ed column, but that doesn’t mean they can’t write at all. There are plenty of commercial products used by well-known organizations that help write content, especially when guard-railed by a template with some basic rules and limitations. In fact, this type of use case has a dedicated segment of NLP called NLG (natural language generation).
AI can also be used for content curation and personalization. AI can help map and deliver relevant and personalized experiences to users after learning from user behavior and historical data. In fact, a machine learning algorithm can go even beyond personalized content and deliver personalized journeys for users, which is considered a holy grail for marketers.
Marketing automation is another hotbed for new AI investment. Different ML algorithms can examine user data to help deliver the right content, at the right pace, through the right channel and at the right time to cater to the highest engagements. AI-driven marketing automation tools use algorithms to learn from past data to identify patterns that produce the most desirable outcomes. As with all ML initiatives, the more data collected by the marketing automation tool, the more these algorithms will learn, become effective and add value.
Self-service tools for customer service like chatbots have taken the digital industry by storm over the last decade. While not all chatbots have shown to be effective, 79% of users prefer using chatbots for the convenience and response time. These tools can be very successful in providing the right level of assistance to users while proving cost-effective for the merchant and business. Ultimately, if done correctly, customers appreciate the immediate availability, the lack of wait time and the quick response of chatbots.
Digital marketers have a wide variety of tools and products that leverage AI available to them. Selecting the right tool could be the difference between buying your very own crystal ball or nosediving into a public relation nightmare.