At BENabs, cluster analysis is utilized to understand audience behaviors on social networks by grouping together creators, channels, and videos according to audience engagement patterns.
We use it, and we talk about it, all the time. But what exactly is cluster analysis?
Cluster analysis groups large data sets by similarities, allowing for the categorization of data based on shared characteristics.
An example of its application is on YouTube, where watching and liking videos by certain creators can lead to the formation of audience clusters, showcasing how content connects based on viewer interests.
This method goes beyond traditional content categorization (e.g., video upload time or location) by focusing on audience interest and engagement to define clusters.
Cluster analysis reveals complex relationships between different content areas, such as identifying adjacent clusters (e.g., sports and fitness vs. sports and well-being), through audience interactions, offering insights into content dynamics and viewer preferences.
What is cluster analysis? | Ask BENlabs: Video Transcript
(00:00) Cluster analysis is the ability to take lots of data and group it in ways that makes things similar. And so the way that we use it here at BENlabs is to understand specifically audiences. And so what I mean by that is looking at the way that for example a large audience on a social network might be behaving and grouping together the creators, the channels, and the videos that they are watching based on the behavior of that audience.
So for example, if you went onto YouTube and you watched two creators and (00:29) you thumbed up two creators that would pull them together in this audience cluster, for example.
And why that’s really interesting is it because it allows you to start seeing not necessarily just what content is like other content based on, you know, is there a certain color in the background, was it uploaded from Germany, was it uploaded at 2:00 in the morning.
Those are all certainly ways those are structured data points that you can use to be able to cluster content but it’s actually on who’s interested in this, how are they (00:55) engaging with it, what other pieces of content are they interested in.
And from that you can build these really interesting topological maps where you can see, oh look, here’s a sports and uh fitness cluster, but right next to it there might be a sports and well-being or a sports and fashion cluster.
And it’s the interactions of those audiences that really power what we think of when we talk about cluster analysis.
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