Overview

Text Analysis helps organizations understand and extract insights from open-ended feedback collected through surveys. While metrics like NPS or CSAT provide a numerical snapshot 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 at scale.

When to use it:
Use Text Analysis when you want to uncover the reasons behind customer scores and turn large volumes of feedback into actionable insights.

Combine Text Analysis with NPS or CSAT data to understand both what customers feel and why they feel that way.

Key Use Cases

• Understand reasons behind customer ratings (NPS/CSAT)
• Identify common themes in open-ended feedback
• Detect recurring issues or concerns at scale
• Analyze sentiment across different feedback topics
• Prioritize improvements based on customer voice

How to Use Text Analysis

Understanding the Challenge with Open-Ended Responses

Most surveys include both quantitative and qualitative questions. While scores provide a quick overview, open-ended responses reveal the actual reasons behind customer opinions.

For example, questions like “What did you like the most?”, “How can we improve?”, or “Why did you give this rating?” provide deeper insights that numbers alone cannot capture.

However, analyzing this feedback manually becomes increasingly difficult as the volume grows. A few responses can be reviewed easily, but hundreds or thousands require significant time and effort, making the process inefficient and prone to errors.

Manual analysis becomes impractical at scale, especially for organizations receiving large volumes of feedback daily.

How Text Analysis Solves This Problem

Text Analysis automates the process of understanding and organizing open-ended feedback. Instead of manually reviewing each response, the system analyzes comments and categorizes them into meaningful themes or tags.

This allows teams to quickly identify patterns, understand sentiment, and detect recurring issues without spending time on manual classification. As a result, organizations can focus on solving problems rather than identifying them.

How Text Analysis Works

Text Analysis works through a process called tagging, where responses are categorized into relevant topics such as Pricing, Customer Support, Product Features, Delivery Experience, or Service Quality.

Initially, users train the system by tagging a set of responses. Once enough data is available, the system begins automatically predicting tags for new responses.

Over time, as more responses are tagged, the system becomes more accurate, continuously improving its ability to classify feedback.

The accuracy of automated tagging improves as more responses are used for training.

Insights You Can Gain

Once feedback is categorized and analyzed, Text Analysis helps uncover meaningful insights such as common topics customers talk about, areas generating positive or negative sentiment, and the main reasons behind low satisfaction scores.

It also helps identify emerging issues that may impact customer experience. For example, if multiple responses highlight login issues, teams can quickly act on this insight and resolve the problem.

Key Benefits of Text Analysis

Text Analysis enables organizations to understand customer feedback at scale without manual effort, making it easier to identify key concerns and prioritize improvements.

It supports faster decision-making by providing structured insights from unstructured data and allows teams to collaborate more effectively by sharing relevant findings across departments such as Product, Support, Marketing, and Operations.

Additionally, automated categorization ensures that once the system is trained, new responses can be analyzed efficiently without repeated manual intervention.

Use Text Analysis regularly to track how customer feedback evolves after product or service changes.