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.
How brands are using machine learning in their marketing strategies
Some brands have already started using machine learning to drive insights and metrics that go well beyond text. Let’s consider the example of SailThru, which uses machine-learning technology to make its email marketing efforts more efficient and lead-driven. ML smartly examines consumer behaviors to check when email distribution will likely drive engagement and ultimately conversions. By utilizing machine learning in its email marketing strategy, SailThru has seen an increase in engagement and lead generation.
As brands now can get access to behavioral and contextual data, machine-learning technology has made it convenient to better use that critical information. And it makes great sense to use this valuable data in context and to bring insights from that critical information. Machine-learning technology processes data at a huge volume and leverages that useful information to produce promising insights.
ML enables organizations to drive insights, target massive consumer bases, analyze larger sets of shopping histories, and present analytics far beyond what a human can manage. Since machine learning is able to manage the workload, it allows ecommerce brands to drive vast and marketable consumer insights.
How machine learning is improving marketing campaigns for brands
While machine learning is being used in digital marketing campaigns across the world, there remains room for improvement. As Adobe mentions, machine learning will quickly move from the individual consumer level to a greater role that involves larger groups as well as additional external factors and internal data. Adobe points out that competitive and weather analysis, along with a wide range of other factors, will also be considered.
insideBIGDATA, a predictive analytics publication, believes that machine learning will transform brand storytelling strategies, along with data-driven storytelling, to represent the upcoming trend of analytics applications. That is already available to digital marketers, although in a restricted form. Data can identify the subject line, but the story must still be created by digital marketers.
Moreover, machine learning not only detects the subject matter of available content, it also has the ability to analyze the tone, medium, and other critical aspects of how brand stories are told. It can even drive impressive experiences based on interactive, engaging content. And some basic machine-learning features are built on interactive content to create a personalized experience that leverages a perfect combination of company data and user feedback to utilize real-time data on a one-to-one level.
A short quiz or survey can also help clearly define consumers’ preferences, gender, location, and profession to refine recommendations quickly and accurately. Ultimately, machine learning could be used as a tool to drive brand stories or to evaluate and maintain a clear brand voice.
However, some important business operations and decisions go beyond the capabilities of machine-learning tools and algorithms. Balancing technological investment and managing human intelligence to maximize business results provide great challenges and unlimited opportunity for online marketers.
For more on digital marketing strategies that get results, see Platform Economy: Putting Customer Value First.