You can use the text analysis and categorization survey to quantify open-ended questionnaire responses into common patterns and themes for an in-depth review of what respondents want to say. Administrators can create tag categories based on patterns or words they wish to analyze. It will measure total tags and show a graph to analyze the most and least popular categories based on the total number of tagged survey responses.
Consider a restaurant is doing a customer experience survey to see how satisfied and pleased their customers are with the service provided. They could create three text tags titled good, neutral, and bad. Comments and phrases with positive statements such as very nice service, loved the food, excellent taste, etc., can go into the first bucket. The neutral tag can have phrases or words such as fine, alright, could be better, etc. The third tag titled bad can have words or phrases that say bad, dissatisfied, poor service, etc. Using these tags you can clearly check how many respondents had a good, neutral, or bad experience and what the reasons were. Text analysis helps isolate the reasons for poor ratings and lets brands work on them to improve their customer operations and service.
You can categorize your survey respondents’ open-ended answers into aspects that are important to your research. This analysis lets you segment your data into clear categories using which you can analyze the reasons behind their responses. This lets you take corrective measures instantly and improve your processes, operations, and product or service.
To use manual text tagging and analysis in surveys, read our help files on how to set up text tagging and analysis.