How machine learning impacts the need for quality content
As Google continues to invest in machine learning technology to help it better understand and parse user queries, columnist Eric Enge emphasizes the need for marketers to continuously improve content quality and user satisfaction.
Back in August, I posited the concept of a two-factor ranking model for SEO. The idea was to greatly simplify SEO for most publishers and to remind them that the finer points of SEO don’t matter if you don’t get the basics right. This concept leads to a basic ranking model that looks like this:
To look at it a little differently, here is a way of assessing the importance of content quality:
The reason that machine learning is important to this picture is that search engines are investing heavily in improving their understanding of language. Hummingbird was the first algorithm publicly announced by Google that focused largely on addressing an understanding of natural language, and RankBrain was the next such algorithm.
I believe that these investments are focused on goals such as these:
- Better understanding user intent
- Better evaluating content quality
We also know that Google (and other engines) are interested in leveraging user satisfaction/user engagement data as well. Though it’s less clear exactly what signals they will key in on, it seems likely that this is another place for machine learning to play a role.
Today, I’m going to explore the state of the state as it relates to content quality, and how I think machine learning is likely to drive the evolution of that.
Opinions expressed in this article are those of the guest author and not necessarily MarTech Today. Staff authors are listed here.