Customer Surveys – Using Text Analytics to Isolate the Reasons Behind Customer Dissatisfaction

Our Client Knew Their Customers Were Unhappy, They Just Didn’t Know Why

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Our Client Knew Their Customers Were Unhappy, They Just Didn’t Know Why
Our Client Knew Their Customers Were Unhappy, They Just Didn’t Know Why

Our Client Knew Their Customers Were Unhappy, They Just Didn’t Know Why

Analyzing customer surveys for trends and themes is a challenge, especially if there are a number of questions that allow a customer to describe or add comments. This type of unstructured text is critical because it can provide additional context or explanation for certain expressed preferences or opinions. It’s also very hard to organize and analyze, especially if you have a large volume of submissions. This was exactly the situation a hospitality company was facing when we were asked to help them analyze their customer surveys for more precise insights. The company knew their hotel guests weren’t happy with their stay, they just didn’t know why. Their existing analytics strategy had been sufficient for identifying large concepts (customer were unhappy with their stay) but they needed more robust analysis to uncover the why (what about their stay was dissatisfying to guests).

We applied our semantic analytics technology to the collection of surveys and were able to isolate specific attributes from consumers responses related to dissatisfaction with their hotel rooms and other features of the property, such as the spa and onsite restaurants. We were then able to identify and dissect specific root causes such as cost, convenience and comfort.

This example highlights the true value of text analytics. It applies an additional level of scrutiny to unordered, unstructured content and reveals insights that more traditional research or manual methods may be unable to surface. If you’re interested, you can read the entire case study about text analytics and customer surveys.