The words ‘machine learning’ probably fill most marketers with horror. Most immediately think that it’s far too complicated for them to understand, let alone use. To a certain extent, they’d be right, as it is undeniably complex. But to put it simply, it is the use of computers that learn without being programmed by being given access to data. The more data the machine is allowed to use, the more it can teach itself and come up with more relevant results.

How does this come into play with content marketing or digital conversion strategies? At first thoughts, data isn’t necessary, as you don’t need it to post blogs or sell a service. Where data does come in, though, is when it comes to providing you with the help to decide where and what to focus on. It is all very well devoting time into researching and writing an article about a product specific topic, but if it doesn’t pique the interest of your readers, then the whole process has been a waste. Any content or other marketing strategy needs to stay in touch with the big and popular trends and topics in the relevant areas, which normally requires extensive research (which involves time and energy). Machine learning can’t create the products or creative content for you, but it can help move you in the right direction and make the process of content marketing faster and far less stressful.

One of the key parts of content marketing is the content that you produce and send out. That seems like a stupidly simple statement, but many publishers don’t have a clear idea as to exactly what topics they should be covering, or what the most effective way of doing it is.

Analytics and insight from machine learning can tell you what topics are currently more popular with your target audience, as well as providing an indication as to what topics are increasing in popularity and therefore, what you should be looking to discuss in the future. Providing more relevant and popular content on digital channels such as social media has many advantages, including better reach. Knowing what topics to focus on (and even at what level to write it) can save a lot of time, meaning more effort can be spent on the quality and creativity of the content.

For those trying to monetise or engage with users, by fine tuning algorithms machine learning can adapt the monetisation strategy to one that will convert users at a higher rate without the need of human intervention. Machine learning can help brands scale their engagement operations and provide increasingly relevant experiences. It can also be applied to conversion strategies including advertising where being more targeted means direct increases in revenue. The ability to learn from others behaviours and get smarter based on your individual offering promotes competitive advantage.

 

Machine learning isn’t just about analysing your audience – you can study what your competitors are doing just as easily. Comparing your content marketing strategy to those of competitors is a very easy way of seeing if there are techniques you maybe aren’t currently trying that are popular with your audience (and hence what you should be doing), how you can improve what you are currently doing (if a competitor is having more success with something you’re trying, then you can look at what they are doing differently to you), and what you should refrain from doing. Content marketing requires constant adaptation and analysis. Machine learning encourages experimentation and innovation, but with far less risk.

Ideally, all publishers want their readers experience on their site or app to be as personalised as possible. However, a lot of time and energy is normally required to do this to a good enough standard. If used correctly, machine learning can cut down the resources needed to do this well, as the data of what each individual is reading (both your content and that of others) and doing online is immediately at your fingertips.

Having this knowledge on each individual is vital in the modern publishing industry, as it becomes ever more digital and competitive. Being able to adapt the experience not only to your target audience, but to the individual as well is a significant advantage to have over competitors.

 

Machine learning isn’t simple to get one’s head around, and it only works if you put in the effort to make it work. It can only be beneficial if you provide it with as much data as possible, have a lot of patience, and have a clear idea of exactly what information you want. It is also not all in the machine – people are required to study the information and create or alter strategies based on that information. According to Greg Corrado, a senior research scientist at Google, ‘…machine learning isn’t just a magic syrup that you pour onto a problem and it makes it better.’

What machine learning can provide you with, however, is vital information about what works in your content or commercial strategy and what it should and shouldn’t involve, and most importantly how to improve. There’s a fine but significant difference between people just engaging with your brand and people who are fully engaging with it. Organisations such as publishers need to be on the right side of that fine line, and machine learning is some of the best help you can have in getting you there.