“Above all else show the data.”
Survey responses. Product reviews. Keyword searches. Forums. As UX practitioners, we commonly scour troves of qualitative data for customer insight. But can we go faster than line-by-line analysis? Moreover, how can we provide semantic analysis to project stakeholders?
Enter Wordle. If you haven’t played with it yet, Wordle is a free Java application that generates visual word clouds. It can provide a compelling snapshot of user feedback for analysis or presentation.
Using Wordle for content strategy
Wordle excels at comparing company and customer language. Here’s an example featuring one of Apple’s crown jewels, the iPad. This text comes from the official iPad Air web page. After common words are removed and stemmed:
Apple paints a portrait of exceptional “design” with great “performance” for running “apps.” Emotive adjectives like “incredible,” “new,” and “Smart [Cover]” are thrown in for good measure. Now compare this to customer reviews on Amazon.com:
To paraphrase Jakob Nielsen, systems should speak the user’s language. And in this case, customers speak more about the iPad’s “screen” and “fast[er]” processor than anything else. Apps don’t even enter the conversation.
A split test on the Apple website might be warranted. Apple could consider talking less about apps, because users may consider them a commodity by now. Also, customer lingo should replace engineering terms. People don’t view a “display,” they look at a “screen.” They also can’t appreciate “performance” in a vacuum. What they do appreciate is that the iPad Air is “faster” than other tablets.
What does your company or clients say in its “About Us,” “Products,” or “Services” web pages? How does it compare to any user discussions?
Using Wordle in comparative analysis
Wordle can also characterize competing products. For example, take Axure and Balsamiq, two popular wireframing applications. Here are visualizations of recent forum posts from each website. (Again, popular words removed or stemmed.)
Each customer base employs a distinct dialect. In the first word cloud, Axure users speak programmatically about panels (Axure’s building blocks), widgets, and adaptive design. In the Balsamiq cloud, conversation revolves more simply around assets, text, and projects.
These word clouds also illustrate product features. Axure supports adaptive wireframes; Balsamiq does not. Balsamiq supports Google Drive; Axure does not. Consider using Wordle when you want a stronger and more immediate visual presentation than, say, a standard content inventory.
Beyond comparative analysis, Wordle also surfaces feature requests. The Balsamiq cloud contains the term “iPad” from users clamoring for a tablet version. When reviewing your own Wordle creations, scan for keywords outside your product’s existing features. You may find opportunities for new use cases this way.
Using Wordle in iterative design
Finally, Wordle can compare word clouds over time. This is helpful when you’re interested in trends between time intervals or product releases.
Here’s a word cloud generated from recent Google Play reviews. The application of interest is Temple Run, a game with over 100 million downloads:
As you can see, players gush about the game. It’s hard to imagine better feedback.
Now let’s look at Temple Run 2, the sequel:
Still good, but the phrase “please fix” clearly suggests technical problems. A user researcher might examine the reviews to identify specific bugs. When comparing word clouds over time, it’s important to note new keywords (or phrases) like this. These changes represent new vectors of user sentiment.
Wordle can also be tested at fixed time intervals, not just software versions. Sometimes user tastes and preferences evolve without any prompting.
Wordle is a heuristic tool that visualizes plaintext and RSS feeds. This can be quite convenient for UX practitioners to evaluate customer feedback. When seen by clients and stakeholders, the immediacy of a word cloud is more compelling than a typical PowerPoint list. However, keep the following in mind when you use Wordle:
- Case sensitivity. You must normalize your words to lower (or upper) case.
- Stemming. You must stem any significant words in your text blocks.
- Accuracy. You can’t get statistical confidence from Wordle. However, it essentially offers unlimited text input. Try copying as much text into Wordle as possible for best results.
- Negative phrases. Wordle won’t distinguish positive and negative phrasing. “Good” and “not good” will count as two instances of the word “good.”
That’s it. I hope this has been helpful for imagining text visualizations in your work. Good luck and happy Wordling.