What is Text Analysis & Its Role In Analyzing Customer Feedback?

Text Analysis 101: A Guide for Customer Experience Teams
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What is Text Analysis?

Text analysis, also known as text mining or text analytics, is the process of deriving meaningful information and insights from unstructured textual data

It involves using keyword-based categorization (Boolean logic), natural language processing (NLP) and machine learning techniques to extract relevant patterns, sentiments, and topics from large volumes of text.

The two most widely used techniques are sentiment analysis and topic detection. In the former, you can identify the underlying sentiment of text responses, and in the latter, you can group texts under specific categories like “customer refunds.”

This analysis helps organizations make data-driven decisions, understand customer behavior, improve processes, and gain a competitive advantage. When it comes to improving customer service, there are several benefits to investing in a tool that does this. Let’s look at how you can leverage text analysis in your organization.

What are the applications of text analysis? 

  • Analyze open ended survey responses 
  • Analyze full-text of customer conversations 
  • Analyze text of online customer reviews or social comments 

Why is text analysis important for CX teams?

Customer expectations are rising as the core competencies of companies increase. In the bargain, CX teams face a huge challenge in making sense of these demands.

Problem #1 - Low Survey Response Rates 

Traditional methods of gathering customer feedback through post-interaction surveys have proven limited and unreliable. Only a tiny fraction of customers, often less than 5%, complete these surveys. And with the growing phenomenon of survey fatigue, the response rates are declining.

The feedback offered on post-call surveys is isolated from the experience that led to the customer's perception. For instance, it may not consider the context of the preceding voice call or chatbot conversation, leaving crucial gaps in understanding the customer journey and sentiments.

And when you consider how open-ended survey responses require manual efforts to sift through and find useful insights, the entire ordeal is cumbersome.

Problem #2 - Difficulty Understanding the Full Customer Journey

When we spoke to dozens of CX leaders, they all had one thing to say: they could not stitch data from qualitative sources to get the full picture.

For example, Medallia and Qualtrics surveys only offer a single dimension of customer feedback data. You can’t connect them to other data sources, making it hard to get contextual insights. Plus, they either have a very basic text analytics tool or one that comes at an additional cost and service fees—separate from the already expensive contract you’ve signed up for.

With Dashbot, this process is simpler. All you have to do is connect your data sources and let the software handle the rest. It'll clean, tag, and extract information using proprietary algorithms—creating a complete picture of your customer data.

Text analysis solutions solve these issues using keyword-based categorization and increasingly more advanced artificial intelligence (AI) powered technologies. It enables CX teams to extract meaningful insights from the vast amount of unstructured textual data generated as a result of  customer interactions.

It provides a detailed view of the entire customer journey by automatically processing and categorizing customer communications in one place. This bridges the gap between survey data and the actual customer experience.

How can you analyze text data using artificial intelligence?

AI brings in a deeper level of analysis as it processes unstructured data automatically, without required manual input from your end. Here’s how it works:

1. Bring all your data sources together

The first step in text analysis is to collect the relevant data sources. These can include customer interactions, such as post-call surveys, SMS messages, voice call transcripts, chatbot conversations, emails, social media comments, and more. 

The data sources may contain structured information (like survey ratings) and unstructured text data (like open-ended responses and chatbot logs). For example, Dashbot offers multiple integrations into various data sources: 

Chat Transcripts

  • Live Chat
  • Chatbots


  • SMS/Text Messages
  • WhatsApp

Call Center

  • IVR (interactive voice response)
  • Call Transcripts
  • Email Messages
  • Website Forms

Customer Surveys

  • Post Purchase
  • Website Experience
  • Customer Interviews

Social & Customer Care

  • Facebook Messenger
  • Instagram Messages
  • Twitter Direct Messages

Social Mentions

  • Twitter Replies
  • Twitter Mentions
  • Facebook Page Comments
  • Instagram Account Comments
  • TikTok Comments

Online Communities

  • Customer Community
  • Reddit Community


  • Product Reviews
  • Location Reviews 
  • App Reviews 
  • Employee Experience 

Use our API's direct integrations or upload any conversational data using a CSV, JSON, or text format. This brings all your customer data into one place—Dashbot’s data platform—and creates a single source of truth.

2. Transform the data using Dashbot

Clean the data to remove irrelevant or sensitive information, standardize the text, and handle special characters as it prepares the data for analysis. The analysis revolves around annotating data based on categories. This may be done manually or using text analytics software. Examples of these categories include: topics mentioned by customers, sentiment or tone of feedback, reasons why customers provided feedback, or making predictions regarding how customers would rate their satisfaction or frustration.

Here’s how it works:

  • Topic modeling: It lets you identify core topics or themes discussed in the text. The output of topic modeling is a set of topics, where each topic represents a group of related words and the proportion of each topic in each document.
  • Sentiment analysis: Determine the sentiment (emotional tone) the text expresses. It can be categorized as positive, negative, or neutral. It uses rule-based methods, machine learning models (e.g., Naive Bayes, Support Vector Machines, or neural networks), or pre-trained language models like BERT or GPT.
  • Reasons analysis: Go deeper than topics to categorize data according to the reason customers engaged with the business (e.g., topic: sound system vs. reason: problem with speaker volume)
  • Predicted rating: This predicts a rating based on the raw text of customer feedback you've collected and enriches them with CSAT, NPS, or customer frustration scores (e.g., scoring  from 1 to 10—1 being the biggest points of frustration). Essentially, it predicts what ratings customers would give that specific problem and assigns a specific number.

Example of Manual Text Analysis

  • Reasons Analysis: Delving deeper than just topics, analysts would categorize data based on specific reasons for customer engagement. This often means understanding context, which, while manual methods can provide deeper intuition, also brings in subjectivity and potential for errors.
  • Topic Modeling: Analysts would have to skim through every piece of text, manually identifying recurring patterns, phrases, or words. This meant reading, categorizing, and then grouping related terms to form 'topics'. As you can imagine, this process is labor-intensive and prone to human biases.

Dashbot revolutionizes text analysis by automating these processes. With capabilities like topic modeling, sentiment analysis, reasons analysis, and predicted rating, what took hours or days for CX teams can now be accomplished in mere minutes, and with a level of accuracy and consistency hard to achieve manually.

3. Get a high-level overview of your data with data visualization

After you finish the topic modeling and sentiment analysis process, the data can be visualized to gain a high-level overview of the insights. 

Dashbot creates topic clusters and charts to show you the distribution of topics, sentiment scores, and other relevant patterns. This high-level exploration helps quickly grasp the data's main trends and themes.

Access accurate topical insights based on customer feedback

4. Explore the data to identify positive and negative outcomes

Analyze specific topics or sentiments driving the most significant positive or negative outcomes. For example, identifying the key pain points for customers or understanding the reasons behind positive feedback. 

Dashbot's Data Exploration module lets you access a sunburst view where the filters are interactive. Here, you can drill down further into each category and tally the data with the specific customer ticket—showing you exactly why the data tells this particular story.

Sunburst view of unstructured customer feedback data

This level of granular feedback lets you devise a strategy that aligns with real customer issues. CX teams can make informed decisions to address issues, improve processes, and enhance customer experiences.

Example of text analysis

Let’s say you’re running customer feedback through Dashbot for your telecommunications company. Dashbot automatically creates categories and analyzes the text slot individual feedback into these categories.

Based on that, it'll show you a complete overview of the data. And that includes the following information:

  • Total number of sessions 
  • Key Activities
  • Sentiment 
  • Topic Insights
  • User Journey Flows

Each category shows you common themes based on sentiment and how many queries you get for those issues. For example, we can see that more customers are demanding information about data usage and plans. But fewer customers are asking for assistance with mobile device troubleshooting—indicating that it works well.

You can use this information to get a bird's eye view of common areas of satisfaction and improvement—and hone in on individual feedback contributing to this outcome.

Text analysis use cases for customer service teams

Here’s how you can use text analysis internally: 

Connect the dots between your customer conversations

You can analyze and gain insights from customer conversations across various channels like emails, chat transcripts, social media interactions, etc. Many customer interactions are multi-channel and it's important to see conversations leading up to critical customer interactions.

Automatically process and categorize these interactions using NLP and machine learning—identifying key topics, sentiments, and trends. This helps you understand what customers discuss, their issues, and how they feel about the products or services. 

Identify high-value customers and common customer problems

Identify high-value customers expressing loyalty or showing signs of being potential advocates by looking at what they say about your brand. Recognizing and prioritizing these customers allows you to engage in proactive relationship management and offer personalized incentives or rewards to strengthen customer loyalty.

It also detects customer problems or complaints at an early stage. By monitoring sentiment scores and specific keywords or phrases, swiftly address issues before they escalate, preventing potential churn and negative word-of-mouth. Today this is typically only done through surveys which are delayed and do not offer opportunity to address customer concerns in a timely manner.

Streamline customer support workflows 

Automate ticket routing, prioritization, and tagging so that your CX and support teams have more time to support customers, not waste time on redundant tasks. 

Automatically classify incoming customer inquiries based on their topics using text analysis and redirect tickets to the right department or agent. This reduces response times customers receive timely assistance.

Plus, sentiment analysis can help prioritize critical or urgent issues by identifying emotionally charged messages that require immediate attention. Tagging customer interactions based on their content (e.g., "technical issue," "billing problem") allows for better categorization and easier retrieval of historical data for future analysis.

Voice of customer (VoC) reporting

Consolidate and summarize customer interaction to produce actionable VoC reports. These reports help decision-makers understand customer expectations and frustrations, make data-driven decisions, and prioritize initiatives that enhance customer satisfaction and loyalty.

Automate text analysis with Dashbot

It's high time companies could stitch their unstructured and structured data to get a complete picture of their CX outcomes. 

Text analysis offers this capability as it extracts data, categorizes it, and summarizes it in a way that makes it easier for you to connect the dots. Ultimately, you can make better decisions and connect with your customers more effectively.

And you don't have to do this on your own. Automated text analysis tools like Dashbot let you make informed decisions by leveraging real customer feedback to improve your CX strategy.

Interested in learning how Dashbot can help you analyze your text data? Book a demo with us today.


  1. What is text analysis in data analysis?

Text analysis in data analysis refers to the process of extracting meaningful insights from textual data. It uses AI-powered techniques like sentiment analysis, topic clustering, and predicted rating to analyze and understand the content and context.

  1. What are the advantages of text analysis?

Companies can understand customer feedback, market trends, and sentiments toward their products or services. This helps them make informed decisions and improve their offerings. They can also automate and streamline processes involving analyzing written content, such as customer support. This saves time and resources while ensuring accuracy and consistency.

  1. What is the difference between NLP and text analysis?

While natural language processing (NLP) is a technology that allows computers to interpret human language, text analysis is a type of NLP that focuses on analyzing textual data. The former includes multiple types of techniques like text analysis and sentiment analysis.

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