Last Updated on May 17, 2021
Digital marketing is in a constant state of flux, continuously adapting itself to rapid changes in technology. In the past several years, for example, visual content has taken over text-based content, with infographics, videos and interactive media becoming the norm for successful content strategies.
Marketing is also increasingly influenced by artificial intelligence, and more specifically by the use of machine learning.
Artificial Intelligence Vs Machine learning
Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, much to the chagrin of data scientists. In fact, machine learning is a subset of the broader field of AI.
While AI indicates the use of computers to mimic human cognition, machine learning applies to machines using and analyzing data to learn on its own, often changing algorithms, ie the rules of the road, as they learn.
In this sense, understanding and properly applying machine learning is a key condition for the development of AI-based technology.
Machine learning is increasingly important for digital marketers, too, as it helps make sense of large volumes of data. As information continues to grow, this trend will only become more important. In fact, 97% of marketing influencers expect the future of digital marketing to “involve marketers working with machine learning powered automation.”
Adapting digital marketing
ML tools are rapidly growing, used in processes from data to content gathering to better understand and target different audiences. Here are some examples of applying machine learning processes in digital marketing.
Consider social networks like Facebook, Pinterest, or Quora, where news feeds are a crucial source of traffic and engagement. ML algorithms are designed to determine relevant content that appears first on the feed, taking into account the user’s previous activity and interests. Users have come to expect apps to learn about them and provide useful content.
One of the recent successful applications of machine learning algorithms has been Google Chrome’s Articles For You feature, which suggests articles when the Chrome browser is opened. Although the feature was introduced with next to no fanfare, it managed to become a silent force for traffic – growing a whopping 2,100 % in 2017.
This much growth is even more impressive when you consider that this data was tracked only for Android users and not iOS– which means the ceiling hasn’t even been reached yet.
The power of this innovation lies in the fact that it uses accumulated browsing data to personalize articles. It also doesn’t hurt that Articles for You is a default feature that pops up on every new tab, and that Chrome is the web’s most used browser.
And while users CAN turn it off, it’s not given as an immediate option and requires a bit of research plus steps to do so.
The rise of programmatic advertising has also naturally brought about data analysis on digital media spend. Programmatic advertising refers to automating the digital media buying process and has been a growing area for advertising dollars. According to eMarketer, $46 billion in ads will be bought programmatically in 2018, about 80% of all digital ads.
Companies like Topix have figured out how to forecast additional revenue from programmatic advertising so that their news site is consistently profitable. Ensuring that content is always paid for is a fundamental building block for sustainability.
ML algorithms can help advertisers assess which ads perform better with select audiences, which ads are most likely to get “hidden” or blocked by users, and accordingly change their advertising strategy. With optimization comes reduced advertising costs.
Campaign analysis and reporting have become easier in automation and content creation is next. A majority of senior marketers interviewed by IDC believe optimized message targeting and real time personalized advertising are key areas where machines will deliver business benefits by 2020.
Email has long been a tried and tested tool in the digital marketing space. With machine learning technologies, marketers can predict when to send a given email using data from the customers’ current activities online, so that the email comes at the right time and frequency.
ML also helps marketers experiment with copy and test different versions of the same email in order to personalize the message and make it more likely to be opened. Machine learning algorithms provide better segmentation of data, which enables marketers to group their target audiences not just by age or gender, but by different types of behaviors.
Google’s algorithms are becoming increasingly AI-powered, which shifts the whole paradigm of content marketing. When the “RankBrain” algorithm was released in 2015, Google tried to use AI to understand user search better.
The future of SEO is being shaped by technology that determines content visibility based on the concrete needs of the person executing the search. All this makes content marketing more about quality than keywords.
Although the concept of automation is not new, there has been a rapid growth in the industry in the recent years because of AI development. Personal assistant Siri was launched in 2010, Alexa in 2014, Google assistant in 2016, Facebook used over 100,000 Messenger bots in 2017
Built around AI and natural language processing (NLP) technologies, chatbots are flourishing. The reason for this is simple: chatbots help marketers to automatically collect customer feedback and personal information, which can then be used to refine marketing strategies and improve customer service and satisfaction.
The smartphone has been a cornerstone of the latest digital revolution. Especially after Google launched mobile first ranking, fully integrating mobile into digital strategy is unavoidable.
Smartphones got more opinionated with the recent introduction of Google’s new Android 9 Pie. Billed by the Verge as “the predictive OS”, this Android operating system tries its best to understand the user.
The simplest functions have personalization elements, for example extending the phone battery by turning off unused applications or reducing the capacity of other apps.
Another feature enables third-party content or apps to appear at the moment the user is searching for something similar. Even the icons that appear at the bottom of the Overview screen are automatically determined. These “guess what I will do next” features can be surprisingly accurate.
At the heart of machine learning is the ability to examine large amounts of data that provide crucial, accurate analytics. As data sets grow having the tools to do this quickly and efficiently will help to drive better returns on marketing investments.