What is Textual Data and Why Does It Matter to Customer Support Teams?

Understanding & using customer support textual data is key to providing the best possible customer experience.
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Manually analyzing hundreds of unstructured text-based data sources is tedious and time-consuming. What's worse is that since it's in text format, you know this process can be made easier, but you're unsure how to do it.

If you’re in a data-driven company that relies on such data sources to make critical decisions to optimize and improve your customer experience processes, keep reading.

“The syndrome of Data-Rich Information-Poor (DRIP) is often encountered when a business tracks a lot of Key Performance Indicators (KPIs), and so, in theory, they have a lot of data on the operations of the business, but in practice, this KPI tracking does not lead to sustainable process improvements.” says Daniel Shapiro, CTO & co-founder of

And he's right. Too often, we get stuck in the DRIP syndrome where we're happily generating data—but can't generate meaningful insights from them. Also, when you consider that 80% to 90% of data is unstructured, there’s so much potential waiting to be unlocked.

In this article, we’ll discuss the concept of textual data and how you can use it to extract valuable insights for your customer support operations.

What is textual data?

Textual data is information that is stored and written in a text format. It can be anything from emails to blog posts to social media posts and online forum comments. In short, it's any data that has been expressed in words. 

As more people communicate online, the amount of textual data available grows exponentially—offering unprecedented opportunities for businesses and researchers alike. For instance, a customer support agent can extract real-time insights into their customers' wants by analyzing email tickets, chatbot conversations, and social media feedback. They have to process using a machine learning-powered (ML) tool that can do this for them.

The advantages and disadvantages of textual data

How is textual data related to dark data?

Gartner defines dark data as "Information assets organizations collect, process and store during regular business activities, but generally fail to use for other purposes (for example, analytics, business relationships, and direct monetizing)."

Drawing a parallel from dark matter in Physics, it means that there's a lot of untapped potential in this type of data—but organizations fail to realize it. It usually happens when they don't have the resources to analyze this type of unstructured data.

But how does textual data fit into this? Here are a few examples:

  • Internal data: Internal data, like product data, daily logs, user search data, etc., can provide actionable insights into how customers interact with the product or service. You can spot trends such as common issues, user preferences, and correlations between products or services that may not have been apparent before.
  • Multimedia sources: You gain valuable insights into how customers interact with their products or services using text-based data such as chatbot transcripts. For example, teams can identify improvement areas and customer sentiment by analyzing conversations between customers and agents to optimize the customer experience.
  • Contact center data: support-related data sources like email transcripts, service desk tickets, call transcripts, etc., are helpful because it allows professionals to get a better understanding of consumer sentiment and behavior. You can consolidate feedback from multiple channels to extract data indicating sentiment at any given time.

Techniques to analyze textual data

Here are a few ways to analyze and extract critical insights from textual data:

Natural Language Processing (NLP)

  • Text categorization: Also known as text classification, it's an automated process that analyzes text documents and classifies them into predefined topics or categories. It assigns labels accurately to unstructured data based on semantic content, enabling machines to comprehend and process natural language. 
  • Sentiment analysis: This task evaluates the sentiment expressed in internal or external data sources (like customer reviews). It detects positive or negative sentiment from structured and unstructured data, allowing businesses to track changes in customer attitudes over time.
  • Dependency Parsing: Dependency parsing is responsible for analyzing the grammatical structure of a text by detecting which words depend on each other and how they relate to each other semantically. 
  • Word Sense Disambiguation (WSD): This technique aims to identify the meaning of a word by looking at its context within a sentence. WSD can be used in language translation applications to improve machine learning models by reducing ambiguity. 

Textual Data Mining

  • Feature selection: The process of choosing the most relevant features from a given dataset to inform ML models. By selecting the most critical features, models can predict outcomes with fewer false positives or negatives.
  • Named Entity Recognition (NER): This sub-task of entity recognition focuses on identifying proper nouns that refer to specific entities such as people, locations, or organizations. For instance, it can recognize your customer's name or where they're from in your customer support tickets—adding more context.

What is textual data visualization?

Textual data visualization is the process of representing text data visually. It makes it easier to find patterns, trends, and similarities between different pieces of text. It uses topic modeling, linguistic analysis, and sentiment analysis techniques to analyze the data and create visual representations depending on the context. 

For instance, Dashbot's Conversational Data Cloud helps you analyze thousands of customer conversations to discover how your customers feel about your product, what prompted them to purchase, and what issues they face. Using that data, you can avoid problems while visualizing the entire customer journey in one place.

An example of how Dashbot analyzes customer support data and creates comprehensive visualizations
An example of how Dashbot analyzes customer support data and creates comprehensive visualizations  

Use cases for textual data

Identify important or trending topics from email tickets

Tools like Dashbot can use textual data such as email tickets to track customer trends, issues, and inquiries. Doing this lets you stay one step ahead of customer needs and problems by quickly identifying trending topics. Plus, it gives customers the impression that their needs are being taken seriously, resulting in increased satisfaction and brand loyalty.

Dashbot's Magic Window shows major topics and their respective channels

Show the sentiment of conversations by analyzing the text

You can also use Dashbot to understand customers' feelings and deal with negative conversations more strategically. For instance, if many customers are having an issue with a specific feature, the software will highlight that and the sentiment associated with it. Based on that, you can create specific resolution guides or allot more agents to tackle the incoming tickets. It also allows you to tailor the conversation in real-time and provide personalized solutions.

Dashbot's Magic Window shows emerging topics and their sentiment
Dashbot's Magic Window shows emerging topics and their sentiment

Analyze the tone and sentiment of conversations to train agents

You can use sentiment analysis to understand your customers and can uncover critical issues to take action on them. You can do this via the Key Activities feature, where topics are categorized by source, type of conversation, resolution rate, and sentiment. It helps you train support agents with concrete data—resulting in improved customer service and experience.

Dashbot's Magic Window correlates tone and sentiment with Key Activities
Dashbot's Magic Window correlates tone and sentiment with Key Activities

Discover new trending support topics in real-time

Dashbot can keep you ahead of the curve with its sophisticated unstructured data extraction capabilities. This means it continuously monitors customer support data to determine customer issues in real time. It ensures your team won't get caught off guard—and they'll be prepared for any sudden increase in user inquiries.

Dashbot's Magic Window shows you emerging topics from analyzed data
Dashbot's Magic Window shows you emerging topics from analyzed data

Analyze your textual data using Dashbot

Textual data helps organizations unlock their dark data insights, make better decisions, and gain real-time visibility. However, extracting valuable insights from textual data is a complex process. It requires advanced technological solutions such as AI and ML models to analyze it. 

Additionally, employing textual data visualization tools ensures that findings are presented in easy-to-understand visualization charts and graphs so you can share them across departments or organizations. You can use a tool like Dashbot to analyze and visualize it in one dashboard—making it an invaluable asset for any organization with access to it.

Interested in seeing how you can use Dashbot to analyze textual data? Book a demo with us today!


  1. What is textual data?

Textual data refers to any information or message conveyed in written or printed form. It can include everything from books, articles, and surveys to social media posts, chat conversations, and emails. It is processed using NLP techniques that analyze the text's words, syntax, and context to extract meaningful insights.

  1. How do you collect textual data?

Several ways to collect textual data include surveys, chat transcripts, and social media monitoring. Here's how they work:

  • Surveys: Gather input from people directly with open-ended questions allowing for free-form text answers. 
  • Social media monitoring tools: Track mentions of specific keywords or topics on various platforms and collect relevant text data automatically. 
  • Chat/audio transcripts: Gather customer data from live conversations through chatbots or phone calls.
  1. What is an example of textual analysis?

An example of textual analysis is when a researcher analyzes written communication, such as books, magazines, or websites, to identify themes and patterns in the text. For instance, a tool like Dashbot can analyze customer conversations from multiple sources (chat transcripts, audio calls, support tickets) and highlight sentiment, trending topics, and critical issues using its machine learning-powered model.

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