Machine learning is making waves in the world of marketing as a way to use complex behavioral and contextual data to improve branding efforts and facilitate communication with customers. Machine learning provides a way to intelligently use customer information by drawing conclusions from complex data sets and can make provide insight into content and marketing materials – before the campaign is even launched.
The benefit of machine learning is the rapid optimization of predictive and problem-solving processes. With the right work up-front, a system can be put in place that uses data from previous and ongoing customer behavior to automatically strategize the next steps in a campaign. These changes can happen immediately, incrementally, and with far deeper analysis than a human staff could realistically apply.
While the scope for machine learning is wide, it is important to note the role analytical tools like R plays in it. This is the language used by analysts to teach machines to derive meaning out of data. Programmers will have a hard time manipulating data without having a strong R foundation. There are many R programming courses available online that train you in the practical application of the language. Given that the language is easy to learn, this is a small barrier for programmers that can be easily overcome.
From Theory to Execution
What are some of the ways that this works in practice? Firms have successfully used machine learning programming to differentiate between customer types in order to tailor the content, method of delivery, and time of delivery to be specific and motivating to the specific customer. These predictions and decisions can happen along multiple different access; for example, there may be a trend in the data that correlates certain commenting behavior with late-night shopping sprees. These people will receive your sales e-mail at midnight instead of noon.
In another case, imagine a scenario in which the data shows that readers who don’t have profile pictures are more engaged by fact-based headlines than they are by emotional appeals. Instead of testing different headlines to try and engage the average user, the subject line of your e-mail or headline for this user will use the “fact” option rather than the “emotion” option.
A great example of this is a type of complex machine learning in action is happening at Netflix. For each title they have, Netflix has several different images and multiple descriptions of the title. Based on a huge amount of data, they can reasonably predict that people who watch action movies, during daytime hours are most likely to engage with certain styles of language, key phrases, actor names instead of actress names, or even just with pictures containing the color red. This use case is expanded on further in an article on RTinsights.
A New Way to Approach Content
Even without registered users or behavioral data, general content and prose can be vetted by the right machine learning program. Using millions of learned data points, a content producer can use aggregated feedback on the success of their new blog post or article – before they even publish it. Write the same article twice but replace every instance of “he” with “he or she,” run it through a trained program, and get valuable insights and predictions on the success of that content. Or better yet, the success of that content on different media platforms – go with “he” when you take your ad to Facebook, “he or she” on your company blog.
This provides companies with unprecedented feedback and agility when tailoring communication styles to reach the desired audience. The way a brand communicates its message has already become optimized for social media trends and search engine optimization. The next step is for it to become optimized towards how, when, and where customers are best communicated with.
Another growing area of interest where machine learning is used is in emotion analysis or sentiment mining. Analysts use algorithms to understand emotions by studying the words used on social media platforms. This is a complex field of study because concepts like sarcasm and irony are difficult to program into a computer.
A Great Time to Dive In
Successful marketers, content curators, brand ambassadors, and online writers have already learned that keeping up with technology trends can provide more web traffic, customer buy-in, and followers. Now that the potential of machine learning is being realized in the world of branding and communication, the next practical step for these professionals is to get some in-depth knowledge and a head start on the coming paradigm for intelligent content. Learning programming tools like the R language will help simplify your quest to dig deep into the world of complex analysis. This platform is widely used in the field of machine learning and gives analysts an opportunity to gain valuable insights in a short amount of time.
The applications of machine learning for developing branding techniques and marketing plans are going play a big role in the future of marketing – in fact, they are already playing a big role in the most successful digital communicators today.