Once you’ve received a bunch of responses, you can use word clouds to see what they mention most often and how they mention it. Some workers might feel “discouraged”, “confused”, “optimistic”, or “supported”, for example. This can be particularly interesting to assess the impact of a recent change or initiative in the workplace. You might also send out surveys to find out what employees like/dislike about working at your company. Try out our free online sentiment analyzer to get a feel for how it works. When it comes to sentiment analysis, word clouds are a useful starting point, but you can get more accurate and insightful results by performing a deeper sentiment analysis of your data. Words like “love”, “best” or “easy” suggest a positive view, while terms like “error”, “bugs” or “confusing” may alert you to potential issues. If words like “price”, “customer service”, or “features” appear bigger in your word cloud, you can get a sense of the aspects that matter the most to your customers.Īlso, you can gauge sentiment by noticing positive and negative terms. Word clouds are a great starting point when analyzing qualitative data, to see which topics are mentioned most often.Ĭreating a word cloud from a series of survey responses, for example, can help you detect relevant themes and pain points.
However, while quantitative data (like stars, likes, NPS scores, or yes-no questions) are easy to process, qualitative data (such as open-ended responses) requires more effort.
Surveys, product reviews, and social media are just some examples of customer feedback that businesses collect. Let’s take a look at how businesses are using word cloud generators: Analyzing Customer FeedbackĬustomer feedback allows companies to understand what clients like and dislike about their products. Not only are they visually attractive, but they also provide quick insights to help you identify trends and patterns, and compare the main words in different qualitative datasets. You can create word clouds from all types of text, including tweets, product reviews, and excel data.
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