Machine Learning and Digital Marketing: Seizing the Opportunity + Infographic

We already spoke about generational marketing and the need to segment audiences according to their age. That is not a concept that came about with the digital revolution, but the digital revolution took it to a completely new level. The same thing happened to many other marketing activities.

Technology does not like to stand still for long. Consequently, the whole world is changing quickly. Digital marketing of today is a landscape where terms like artificial intelligence and machine learning are more than just buzzwords. They are the ways things are done in digital marketing today. They are the technologies that drive change.

Artificial Intelligence and Machine Learning — Which Is Which?

The concept of artificial intelligence and artificial beings was present throughout Antiquity. The term entered widespread public use through science fiction many years before artificial intelligence became a reality. At that point, however, artificial intelligence became easily confused with many other terms for technologies that stem from it. Artificial intelligence and machine learning are not two terms that should be used interchangeably, even though they are related.

Artificial intelligence is a concept that is hard to boil down to a single definition. However, at its core, artificial intelligence is the process of giving machines the capability to perform actions we humans would deem as “intelligent.”

Machine learning is one of the possible ways machines can achieve artificial intelligence. It is a concept that enables a machine can learn on its own, given enough data, without the need for a human being to teach it anything.

AI and machine learning have many applications in the digital marketing industry. Machine learning is especially important for marketers because of its data-crunching abilities, which are essential for support and enhancement of an array of marketing activities. Let us see what type of changes it will bring, or has already brought, to digital marketing.

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How Does Machine Learning Affect SEO?

Search engine optimization, in 2018, boils down to making content and web pages look good to Google’s crawlers. Google has around 74% search engine market share. Its closest competitor, Baidu, has slightly less than an 11% share. With Google’s total domination over the market, it makes sense that the things it does have the biggest effect on the search engine optimization industry. That is why, when Google hands over a part of its search results ranking algorithm to machine learning, the world takes notice.

What Is RankBrain?

RankBrain is a machine learning addition to Google’s search algorithm. It seems to be one of the best examples of using machine learning in marketing. No one can tell for sure how exactly it affects search engine rankings, except that it is in the top three factors that determine the quality of a page. The most educated guesses say that it will diminish the influence of the other two most important ranking factors right now: keywords, and links.

Changing the Way Search Engines Work

Search engines and the algorithms they use are not the most transparent pieces of tech. We cannot say for sure which factors they take into account when determining the rankings of a page. However, the SEO community has a pretty good idea of how important keywords and links are. They are the things algorithms look at when trying to determine how well a page answers a query. Using right keywords and links is a straightforward strategy in SEO, developed to the finest details.

Machine learning has the power to change that. By using it, the algorithm can determine how well the content delivered in search results matches the searcher’s needs simply by monitoring the searcher’s engagement. When the algorithm does it enough times, it can adjust the ranking of a page according to its performance. This makes it much harder for search engine optimizers to do their jobs because it adds a load of unknown variables.

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What About Voice Search?

In the next four years, we can expect to see voice-enabled smart speakers in more than a half of American households. Voice search and voice-first interfaces seem to be the way in which search is moving. The main thing about voice-enabled smart speakers, though, is that they will not support ads. That means that most marketers will have to rely on optimization of non-advertising content to reach customers.

Voice search is also powered by machine learning technology, at least partially. Optimizing for voice search at this moment means optimizing for locally relevant content because most of the searches are performed on mobile devices. And it is a safe bet that machine learning will help searchers get the best possible results when using voice search.

Machine Learning Changed the Content Game

Machine learning is one of the technologies that stand behind the machine-created content. We are seeing some advances in the area, but marketers should not fire their copywriters yet — machines are still far away from producing high quality, engaging content users love to read.

But machine learning is influencing the world of content creation, management, and analysis nonetheless. The very fact that it affects search engine optimization means that content creation is somewhat guided by the preferences of machine learning algorithms. Content marketers will need to think about the metrics they use when gauging the success of their content.

The Rise of Personalization

Statistics show that 81% of consumers want their favorite brands to know them. 74% of customers get frustrated when faced with content that is not personalized. More than 78% of consumers will not engage with offers unless they have been personalized according to past interactions with the brand. And the list goes on — the bottom line is that consumers want to be known and recognized for who they are. That is the reason why personalization has been the most important marketing trend of the past couple of years.

Machine learning technology helps with personalization in a couple different ways. Predictive search, as well as a search that delivers recommendations, are among the most widely used applications of machine learning for personalization.

Personalized Content

The same information the machine learning algorithms use to predict search results or recommend products can also be harnessed to create personalized content. Using highly specialized machine learning tools, you can improve your content be relying on the insights, editing advice, and scheduling advice these tools provide. People will appreciate the effort. “Companies like Facebook frequently gather consumer data to improve their offerings,” says Neil Patel in a blog post. “In a way, they’re masters of personalization. They understand that people want to see things that are relevant. I know I do.”

This practice can be incredibly important for email marketing. There is a number of email marketing automation tools marketers use to finely tune their targeting. However, these tools only work when marketers are able to deliver the content that targets specific segments of their audience. That is where machine learning comes in with analysis and guidance for the content creation process.

The Shifting World of Social Media and Machine Learning

Social media is an important channel for customer engagement and content distribution. The old mantra of marketing says that marketers go where the consumers are. In the 21^st century, that means going to social media. More than 2.6 billion of people around the globe use social media. Marketers cannot afford to ignore that fact.

Social Media Landscape

In 2018, the world of social media is about to undergo some changes. There has been plenty of buzz around the changes Mark Zuckerberg announced early in the year. There had to be because the post included the phrase “by making these changes, I expect the time people spend on Facebook and some measures of engagement will go down.” But marketers are still not willing to migrate away from the biggest social network in the world. They will need to diversify their social media presence, though, and invest more in building a diversified social media plan.

The types of content social networks use has changed, with more emphasis on the visuals. Every social network has at least one video product. We are also seeing competition stiffen as social networks poach each other’s content products. And as the number of social media users increases so does the number of brands who use social media to reach customers. It is a very crowded space.

Better Engagement

In the increasingly competitive atmosphere, social media managers are faced with customers who have increasing expectations of engagement. Customers expect their favorite brands to reply in a timely manner when addressed, especially when customers have important questions or complaints. Chatbots are becoming the machine-learning powered solution for immediate engagement, even though they are still unable to perform completely on their own.

Another important concern for brands and customers alike is safety and community management. Social media conversations can quickly turn toxic, and community managers can use every bit of help with policing their social channels. We can expect very soon to have artificial solutions for this as well, and it will be another application of machine learning that will swoop in to save the day.

Machine Learning Changed Paid Advertising, Too

Paid search marketers have their own machine learning tools to help them achieve better results. And they work. One report showed that machine learning tools for paid search campaigns help increase conversions by 71%, decrease cost-per-click by 7%, and increase clicks by around 15%. That is a measurable benefit of using machine learning in paid search marketing.

Google Comes to Aid

Google is also doing its part of help. Early in 2017, Google rolled out in-market audiences for search ads, a feature that helps marketers reach audiences who have already shown interest in the type of products the marketers are advertising. This feature alone is bound to slowly increase every important paid search marketing metric.

That is not the only machine learning application Google offered to paid search marketers. There is also Google Smart Bidding. It allows marketers to use several different strategies to reach a target CPA, maximize conversions, and bid. It might take some time for the feature to learn, but it should deliver results if given enough time to learn.

There is also the Smart Display Campaigns options — a campaign that is fully controlled by machine learning. Marketers only need to set a couple of parameters and let the algorithm do its part. Just like Smart Bidding, this feature will need some time to learn.

What to Expect from Machine Learning in the Future?

If there is one thing we have learned from technological advancements so far, it has to be that the list of impossible things is shrinking every day. Machine learning technology will have significant implications for many different fields.

A report by the Human Resources Professionals Association and Deloitte Canada outlined the influence of AI and machine learning on the workforce. The findings prompted the two organizations to call on the government of Canada to start addressing the disruption caused by these technologies and start preparing the workforce for the future of work. Job loss is one of the major concerns of future machine learning development.

Those changes will affect marketers, as well. “The most conservative estimate is that AI-driven changes are expected to replace 25% of jobs across the world, by 2026,” said Avinash Kaushik, the founder of Market Motive. “My goal: Stay ahead by solving new challenges.” The more we automate and the more we let machines do for us, the less need there will be for some positions. Marketers will continue to squeeze out every bit of value they can from machine learning, so we can expect to see even more applications of the technology. Apart from that, its future is riddled with high-tech terms such as quantum computing, hyper-personalization, and even more predictive capabilities.

Ever since the digital revolution kicked off, disruption has become the norm. We are seeing technology change the way marketers do things on regular basis. The great differentiator between marketers who succeed and those who do not is their ability to quickly adapt and adopt new technologies. It has been like that with email, social networks, and every advancement we have seen so far. The same is happening right now with machine learning. The marketers who want to stay ahead of the pack will need to find ways to extract value from it. And they need to do it quickly.

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