Machine Learning and Content Marketing

Machine Learning and Content Marketing

Will machine learning take over content personalization in the near future? Machine learning is not a new concept. It has been around since the 70’s, but just recently started proliferating many areas of human activity. That increase in utilization is due to computing capabilities finally catching up to the level required to support complex machine learning computations. What is machine learning?  Machine learning is an algorithm that operates on a large set of data and over time “learns” how to answer certain sets of questions in an increasingly precise way. There are two types of machine learning – supervised and unsupervised.

  Supervised learning (also called classification) involves processing large amounts of data in order to answer simple questions with precise answers. One example of supervised learning is answering questions about the projected price of a house. We could “feed” algorithms with large amounts of data on previously sold houses, including price based on square feet, location, number of bedrooms, etc. There could be hundreds of attributes that figure into the price of the house. Supervised machine learning is very good at digesting previous data and accurately predicting what the sale price should be for any house. Unsupervised learning is significantly more complex and closely resembles how we, as humans, think. The goal here is to feed large amounts of data, have algorithms find patterns in that data, and then group different sets of data into these patterns. The most frequent uses of unsupervised learning are predictive analytics and autonomous driving. So how can machine learning be used in content marketing? In the age of personalization, marketers look at analytics and personalize content based on user parameters. It’s a huge step forward compared to a static content model, but it requires creating “personas” and personalizing content based on users fitting a certain profile. Although that approach works, it greatly simplifies the view of the user that comes to visit your site. With proliferation of big data, we capture many attributes about customers interacting with your site. It is an opportunity to create content micro-targeting utilizing machine learning. This is the next step in simple, persona based content targeting. Supervised learning and algorithm effectiveness will continuously improve and adjust with new users coming to your site and interacting with content. It seems to be a no brainer to implement content targeting that is based on machine learning everywhere. The question is: why hasn’t it happened yet? The challenge of implementing machine learning algorithms lies in its complexity. As of now, there are not many tools that facilitate machine learning and each implementation starts from square one. Marketers have to work with machine learning specialist to identify appropriate user attributes for personalization and then a fairly complex implementation ensues. As of right now, several startups as well as established content management software vendors, are working on integrating machine learning capabilities into their standard set of tools. When that happens, marketers need to be prepared to utilize it and have an understanding of the power they are gaining in machine learning content targeting.

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Vadim Dolt has been solving business problems using technology for over 15 years. He started his career as a software engineer / architect and now has complete responsibility for the technology department of Agency Oasis.