Incorporating Machine Learning Into Your Digital Marketing Plan


By Muhammad Ali, digital marketer, Branex

For those who still don’t know what machine learning (ML) is: “It is the field of study that gives computers the ability to learn without being explicitly programmed.” – Arthur Samuel, 1959.

If you are a digital marketer and you don’t understand what machine learning is, it’s high time to learn about this amazing digital technology. Just as artificial intelligence, social automation and DIY tools, programmatic buying, and other latest technologies have revolutionized different aspects of marketing world, machine learning is changing the whole process of marketing – from how marketers handle simple tasks to how they create marketing campaigns and create brand stories.

First, it’s important to clear up the most common misunderstanding: Machine learning is entirely different from artificial intelligence (AI). Rather than developing the cognitive competences for your competitors or surpass human intelligence, machine learning focuses on problem-solving processes.

Machine learning is an advanced tool that could improve things because of its efficiency and ability to handle complex tasks. Data is the most critical aspect of any digital marketing strategy, and machine learning can effectively dovetail complex data. Since the best opportunities for digital marketing revolve around data, marketers are now leveraging machine learning to produce better results.

Some businesses and digital marketing agencies still may not realize the real power of ML, but done right, it can do wonders for your marketing campaigns. That is why marketers are now building more effective and result-driven digital marketing strategies using ML technology.

Throwback to the history of machine learning

Machine learning is not a new tool; it’s something that has advanced over time and recently gained significant new strengths and potential. Let’s look at the example of spell-check tools: They are based on ML algorithms to figure out spelling and grammatical errors. Although these tools are not perfect, they utilize basic data to identify potential errors.

Nowadays, some brands are using ML for some online product recommendations and data to make refined and relevant suggestions to consumers. The Google search box also follows a machine-learning algorithm to make sense of search terms, for example, when they contain spelling errors or typos.

Most importantly, this highly efficient, robust technology is accessible to non-tech folks as well. Brands like Sift Science uses machine learning technology to figure out online scams, while IBM Watson Solution combines it with natural-language metrics to help across a wide range of services. Quick and easy access to data has cleared the way for developing innovative mobile applications, enabling brands to take full advantage of machine-learning tools.

Read the source article at Digitalist Magazine.