Text feedback is the closest we ever get to a 1:1 conversation with every customer, every citizen, and every employee. In free text, our customers get to tell us what they really care about and why, unconstrained by the questions we decided to ask them. It’s where the customers get to decide what’s most important.

Internalizing ten thousand pieces of feedback is roughly equal to reading a novel and categorizing every sentence. It’s time-consuming, laborious, and hard to make text actionable. Most of the time, increasing the number of customer survey responses you get is a positive thing for your customer experience (CX) program. When it comes to open-text feedback that may not be the case; it means you need to scale either your team reading feedback or use a text analytics tool to surface the most important pieces and themes of feedback.

Text analytics can help you answer two core questions:

  1. How are you performing on the topics you know about like wait time, service reliability, and cost?
  2. What’s lurking out there that you didn’t even think to look for like bugs in software, confusing onboarding process, or flaw in your product?

A powerful text analytics program can answer both of these – at scale – while keeping you connected to the voice of your customer and the next actions to take.

Make Sense of Open-Text Feedback to Baseline and Improve

open text feedback and sentiment analysis

Having your customer experience management (CXM) platform and text analytics software integrated means that you can use the outputs from your text analytics of customer feedback throughout your program to drive change throughout the organization.

  • Include text visualizations in reports to trend, baseline, and identify key drivers
  • Deeply analyze text data, such as topic and sentiment tags, alongside other quantitative measures from statistical analyses to find clusters and root causes of desired behaviors
  • Automatically deliver role-based dashboards that include relevant text insights in Customer Experience and Employee Experience dashboards
  • Trigger ongoing action items based on topic and sentiment to close the loop with upset and at-risk customers
  • Benchmark topic categories and sentiment ratings to set goals for the future

Discover Hidden Causes of Customer Behavior

topic models examples text analytics
The second important use case for text analysis is to uncover previously unknown themes lurking out there that you never knew to look for. Text analytics uses sophisticated machine learning models to discover blind spots that are lurking in free text comments, leading you to uncover customer pain points you never knew to look for.

  • Machine learning-based term analysis exposes themes and clusters of words used in similar ways that are unique to your dataset
  • Automatic term associations help to provide tailored expansions to the themes you were already tracking
  • Industry-specific best practices topic models assure you that you’re tracking the themes relevant to your industry and use case

Conclusion

Using open-text responses in your CX data can be overwhelming without the right text analytics tool to make sense of it all. Open-text is a great way to discover pain points you didn’t know about, provide specific context to why a customer respondent left a negative NPS score, and prepare your customer service teams with the background needed to close the loop with the customer.

At Qualtrics, we believe that feedback is a gift, and with our text analytics tool Text iQ you get to use every piece of text feedback you collect to learn, improve, and change. To learn more about text analytics, check out our webinar.