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.
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 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.
Data is the way forward for a lot of businesses. Marketing Guru Neil Patel, talks about a guide to building online courses in his blog, by citing examples of entrepreneurs who gathered data on what works with their audiences. For example, tweaking the timing of their course launches with pricing worked well for one of them. They made the course available for free before the launch of iOS 8 to ride on the buzz and generating 60,000 signups! Post the launch event, they relaunched the course and converted a lot of these customers! How awesome?!
But this was a business strategy that worked, right? Imagine being able to access data about events around the world and tweaking the prices of your product and reaping benefits like this?
That’s what Machine learning is capable of!
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 to 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.
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. While a lot of the history of the field can be learned about with a web search, separating the theoretical aspects from the practical application and tools can be difficult. For a head start on the field, Machine Learning Training can help in understanding algorithms like regression, clustering, classification, and prediction.
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.