Interpreting customer feedback is essential for companies to make focused improvements to their products and user experiences. In order to utilize and decipher the massive troves of text data collected from product reviews, user surveys, client conversations, social media postings, and other sources, we use Quid to easily identify key pain points customers may have and determine the best ways to address them.
Last year, we implemented a live chat feature within our own platform to enable our users to ask questions and receive assistance in real time. We decided to analyze our chat transcript data using Quid to understand where users have the most questions and how we can enhance our customer experience.
To analyze our live chat data, we started by uploading 688 transcripts into Quid. We discovered that our customers have the most questions regarding the very first step in a Quid analysis, Searching, which makes up 25% of our total chat topics. This is followed by Navigating a Network (16%) and Saving & Exporting Views (13%).
Quid can further break down these three prominent themes to see where exactly our customers have questions and how we can address them. Within the Searching cluster, we analyzed 5 subclusters to find the most common questions that users have during the search phase of a Quid analysis. While Debugging Error Messages is one of the leading subclusters, the subsequent subclusters of Using Filters/Operators to Refine a Search and Making a Search Specific to a Topic together make up 61% of all customer issues with a Quid search.
Each of these subclusters highlights the larger interest from our customer base in crafting more effective, refined searches. To improve the search function for our customers, we plan to implement in-app walkthroughs to address frequent questions and update our resource center with more detailed guides on developing an effectual search query.
Shifting to the second largest source of our customer queries, Navigating a Network, Quid found that half of all questions had to do with how to manipulate clusters by moving and deleting nodes. An important feature of any Quid analysis, this allows users to clean the data prior to sharing insights with a broader audience. Customers also had frequent questions about generating relevant visualizations within their analysis. By diving in and examining specific subclusters within the larger topic of network navigation, we can pinpoint the main uncertainties with this feature and provide more clarity during user training sessions and in our resource center.
Similarly, Quid found 5 distinct subclusters within the Saving & Exporting Views topic. Customers primarily had questions about how to export multiple views as opposed to a singular view, and how to customize the output of different visualizations. Seen below, Quid separates these subclusters by similarities in language but still manages to classify them into the same overall theme. On the left, Exporting to Various File Formats, Saving Views, and Including Labels in Exports focus more on the actual export process, while Changing the Background Color and Cleaning Up a Network for Export have more to do with customizing data visualizations prior to export.
By using Quid to analyze our live chat data, we can easily categorize hundreds of customer questions and develop actionable insights as to how our customer experience can be improved even further. Here is what we are hoping to do in 2019 to accomplish this:
Improve our training process to give users a head start in understanding the many use cases of Quid
Add more in-app assistance if users come across roadblocks in their analysis
Update our resource center with more detailed training documents and videos to help with day-to-day questions
We encourage Quid users to continue to use live chat whenever questions come up while using our platform. Look for the Ask a Question chat bubble on Quid’s interface to connect with our customer success team in real time.
Learn how your company can develop an analysis of customer questions like this one by reaching out to email@example.com
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