March 14, 2017

Why Tag Clouds and Topic Wheels Hold You Back

One of the most common forms of data visualization among social listening softwares is the tag cloud. The graphic representation of which topics are the most common organized by word, size and color is easy for the layman to decipher so it is dangled at the end of the software company’s string like top sirloin.

But it’s just a chicken nugget. Or, more aptly, just the breading around the chicken nugget.

Tag Cloud

Topic wheels are a bit more informative methods of data visualization. They enable you to see subtopics easily. But the data visualization is still just a superficial layer around the insights your data contains. They help you see one layer down.

Topic Wheel

But insights are seldom found one layer down. Understanding of the conversation — the why behind the emerging topics and themes — means drilling down deeper.

Take our study of Dirt Devil, for instance. We may notice looking at Tag Clouds and Topic Wheels of data visualization that durability is an issue that surfaces in the negative conversations around the brand. But why does Dirt Devil have durability issues? To know that, you have to drill down into the negative, then into the durability topic, then analyze and understand the various issues there.

Conversation Research breakdown

The level of detail that can provide a product manager with actual insights to improve the product is not found using a tag cloud or a topic wheel. It’s found by diving in and analyzing and understanding the full context of the conversation. With this information — certainly represented visually for ease of understanding — we can tell the product manager that there are structural issues in quality of construction, weakness in the unit handles and motor issues, particularly when used for pet hair. These insights give the product team direction so they can either A) Ask deeper questions in further research or B) Focus on the opportunities to improve the product.

The overarching point is that if you’re relying on visualizations of your data rather than analysis of it, you’re missing a lot. In fact, we would surmise you’re missing everything.

We would love to help understand your data. Want to know more about what customers say about your brand? Your products? What you can do better? Drop us a line. We can help.


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