How Can Machine Learning Transform Segmentation?


In pretty much every article you read about how publishers can earn more audience engagement, the word segmentation crops up somewhere along the line. It’s easy to see why; by segmenting your audience, you can gain information about certain parts of your audience and act accordingly, like what topics they are particularly interested in, what platforms they usually read your content on, at what times they usually do this, plus so much more. By doing this, publishers can make the reader’s experience more personalised and thus more valuable to them, meaning they are much more likely to continue reading your content, and enjoy their experience doing so.

The chances are you’ve heard why segmentation is so important and all that good stuff before. But the reason people keep saying it is because even though a lot of publishers segment their audience data now, a surprisingly low number of them aren’t making the most of it.

There are numerous reasons for this, but one of the primary ones is that there is often so much data to work with that it’s incredibly difficult for humans to fully get to grips with all of it. This is where machine learning can really help the segmentation process, the reasons for which are explained below.


As mentioned above, publishers have so much data at their disposal that often it is hard to know where to start, and even if you do, it’s hard to effectively analyse it all. In order to analyse and segment the vast and ever-increasing amount of data, something like machine learning, which is more advanced, more accurate and quicker than humans is required.

Having more advanced segmentation enables publishers to gather finer details about smaller groups of readers (for example, a certain age range in a certain area) and even to the individual.

The benefits of this are abundantly clear. Not only will the reader be shown only the most relevant articles to them, at the most appropriate time and via the most appropriate means, but it is also likely to lead to increased profitability through more personalised advertising (which is more likely to be clicked on) and a more reader-orientated experience, which is more likely to retain readers due to the feeling of being more valued.


Another key aspect of segmentation and data analysis is to identify upcoming trends or changes in behaviour, either with your audience generally or with far smaller segments. From this, you can better predict what topics or issues should be discussed in your upcoming content, and at whom you are primarily aiming your content at.

The ability to be more proactive rather than reactive with your marketing and content means that building all-important relationships with your audience becomes much easier, as they will feel that you are in touch with what they want to read about and discuss. Having your readers’ experience tailored to them is a vital component of growing and maintaining your audience, and thus increase profitability, in such a competitive market.


It’s all very well knowing using data can help you gain a better understanding of how best to target your audience, but doing this is pointless if you are not completely certain of who your audience actually is (or want it to be). Data can also help here, potentially showing what potential audiences aren’t getting what they want from competitors, and therefore who you could aim for with your content without the maximum amount of competition.

But where can machine learning help here? Humans can (and do) gather this information, and very effectively in many cases. What machine learning can bring to the table here that humans may not necessarily see is what audiences can be potentially targeted in the future. Having a long term plan of who you want to aim your content at is vital in knowing what resources and platforms to prioritize in the future, as well as what sort of content they will want to read. Having a clear idea of what your future plans are is without doubt a huge advantage of machine learning in segmentation, not only because competition is so fierce, but you can devote more time towards creating high quality content and other important aspects of the process.


Machine learning can transform segmentation in many positive ways for publishers. Not only does it make the process faster and more accurate, meaning both time and resources are saved and more efficiently used, but profitability is also more likely to increase through more relevant content and more personalised advertising. That’s all good, but it can also provide insights into what possible paths should be taken in the future, meaning that such vital future decisions can be made not without risk, but certainly very calculated and well backed-up ones.

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