Text Analysis helps organizations understand and extract insights from open-ended feedback collected through surveys. While ratings such as NPS or CSAT scores provide a numerical overview of customer satisfaction, the real value often lies in the written responses provided by customers.
Text Analysis automatically processes these responses, identifies key themes, categorizes feedback, and helps teams understand what customers are actually saying.
Most surveys contain a mix of quantitative and qualitative questions.
Examples of open-ended questions include:
What did you like the most about our product or service?
How can we improve your experience?
Why did you give us this rating?
While numerical scores provide a quick overview of satisfaction levels, open-ended responses reveal the actual reasons behind those scores.
However, analyzing these responses manually becomes difficult when feedback volume increases.
For example:
A few responses can be reviewed manually.
Hundreds or thousands of responses require significant effort.
Large organizations may receive thousands of comments every day.
Reading and categorizing each response manually is time-consuming, repetitive, and prone to errors. As a result, organizations often struggle to quickly identify the key issues affecting their customers.
Text Analysis automates the process of understanding and organizing open-ended feedback.
Instead of manually reviewing every response, the system analyzes comments and categorizes them into meaningful topics or tags.
This allows organizations to:
Identify common themes in customer feedback
Understand positive and negative sentiments
Detect recurring problems or concerns
Prioritize actions based on customer feedback
By transforming large volumes of text data into structured insights, Text Analysis helps organizations focus on solving problems rather than spending time identifying them.
Text Analysis works through a process called tagging.
Users categorize responses into relevant topics or tags such as:
Pricing
Customer Support
Product Features
Delivery Experience
Service Quality
Initially, users train the system by tagging a set of responses. Once enough responses are tagged, the system begins automatically predicting the correct tags for new responses.
The more responses that are tagged during training, the more accurate the automated predictions become.
This learning process allows the system to continuously improve its ability to categorize feedback.
Once responses are categorized and analyzed, organizations can easily identify patterns and insights such as:
The most common topics customers talk about
Areas generating positive or negative feedback
The main reasons behind low NPS or CSAT scores
Emerging issues affecting customer experience
These insights help teams take targeted action to improve products, services, and customer interactions.
For example, if feedback shows that many customers are experiencing issues while logging into an application, the organization can quickly share this insight with the product team to investigate and resolve the issue.
Analyze thousands of open-ended responses quickly without manual effort.
Discover the most common issues or themes mentioned by customers.
Insights from text analysis allow teams to prioritize improvements quickly.
Machine learning helps automatically classify new responses once the system is trained.
Insights can be shared with relevant teams such as Product, Support, Marketing, or Operations.