What is text analytics used for?

Nov 25, 2021 | Text analytics

Quantitative text data is extremely common in online user feedback (e.g., measures like Net Promoter Score, Customer Effort Score, and Customer Satisfaction), but there’s still a lot to be said about qualitative data (e.g., open answers), or what’s known as unstructured data. 

While quantitative data, such as NPS, is simple to measure and display in charts or an Excel sheet, qualitative data is more difficult if you don’t have the tools to analyze it. This is where Text Analytics comes in.

 

What is text analytics?

The act of mechanically turning large amounts of unstructured text into numerical data in order to find insights, trends, and patterns is known as text analytics. This technique, when combined with data visualization tools, allows businesses to comprehend the story behind the numbers and make better decisions.

 

Text Analysis vs. Text Mining vs. Text Analytics

Text mining, text analysis, and text analytics are all terms that refer to the process of examining unstructured text in order to extract insights. Text analytics, on the other hand, gathers these results and turns them into something that can be quantified and represented through charts and reports, whereas text mining (or text analysis) provides qualitative insights.

All of these concepts apply to partial Natural Language Processing (NLP), which has the purpose of retrieving specific information from a text in the most practical way possible rather than fully understanding it. 

This entails striking a fair balance between the time and effort required to design and maintain the analytical pipeline, as well as its computational cost and performance (e.g., how much memory it requires and how long it takes to process a single document). Recall (completeness of the extraction), precision (quality of the extracted information), and combined metrics like the F-Score are used to assess the latter.

 

Guide to Text Analysis vs. Text Analytics

Text analysis and text analytics are frequently used in tandem to provide a comprehensive knowledge of all types of text, including emails, social media posts, surveys, and customer service tickets, among other things. For example, text analysis techniques can be used to determine how people feel about a company on social media (sentiment analysis) or to comprehend major issues in product evaluations (topic detection).

Text Analysis also refers to the act of computationally analyzing texts and aids in the translation of a text into the language of data. Text Analytics refers to a set of techniques and methodologies for converting textual material into data, which may subsequently be mined for insights, trends, and patterns. Text Analytics helps make sense of this data while text Analysis prepares the content.

Text analytics also uses the results of text analysis to identify patterns, such as a spike in negative comments, and provides actionable insights you can use to enhance your product, such as resolving a defect that’s causing your consumers to be frustrated.

 

How can text analytics help companies?

Every minute, massive volumes of unstructured data are generated. Internet users send millions of emails as well as hundreds of thousands of new tweets and comments on Facebook, which makes organizing and analyzing data to discover key insights a complex and time-consuming  task. 

Thankfully, businesses can use text analytics to automatically extract meaning from unstructured data such as social media postings and emails, as well as live chats and surveys, and turn it into quantitative insights. They can also increase customer happiness (by understanding what their customers like and hate about their products), detect product flaws, conduct market research, and monitor brand reputation, among other things, by using text analytics to spot trends and patterns.

Text analytics offers several advantages: it is scalable, which means you can analyze massive amounts of data in a short amount of time, and it allows you to get real-time information. As a result, you can not only acquire insights that will help you make informed decisions, but you will also be able to handle challenges efficiently.

 

Text analytics example

Topic Analysis

A topic analysis is a text analysis approach that tags NPS replies based on predetermined categories like Feature Request and Customer Service. Listed below are a few examples of topics extracted from feedback comments:

  • “I wish there was a way to export the data” –  Request for a Feature Topic
  • “Customer service is extremely helpful and nice!” – Customer Support Topic

After you’ve categorized each NPS response, you can use text analytics to identify patterns and insights throughout the dataset and display the results in charts or reports.

 

Customer Service

Customer service is another fascinating application of text analytics in business, in addition to customer feedback analysis. Text analytics, for example, can be used to examine the content of support requests to better understand your customers’ needs, motivations, and expectations, as well as provide suggestions into how to rethink your customer experience approach. Text analytics can be used to extract key data from various customer support channels like email, chat, and social media, in addition to support tickets.

 

Benefits of text analytics

Helps identify the source of an issue (or source of dissatisfaction)

Customers can explain what is or isn’t to their satisfaction by completing surveys that contain open-ended questions. If multiple visitors are leaving things in their shopping carts without purchasing them, your text analytics will most likely highlight this trend using repeated words like shopping cart, purchase, or checkout, as well as the sentiments behind such words, etc.

 

Allows new trends to be discovered

You can see whether your NPS is increasing or decreasing, but you can’t explain why unless you have an explanation from your customers. Text analytics assists users in visualizing and resolving trending feedback categories in a timely manner.

 

Prioritization of issues can be done swiftly and efficiently

You’ll know immediately which areas you need to focus on or which areas you’re succeeding in if you identify the most often used words or employ word pairing. This ensures that problems are resolved quickly.

 

A better digital experience

Opinions and thoughts that you might not have heard otherwise are brought to the surface, allowing you to give the best possible customer service and keep your clients satisfied.

 

Text analytics techniques and use cases

To analyze text and unstructured data, software companies leverage a variety of methodologies. Here are a few of the most well known:

 

Word Count

This is text analytics in its most basic form, where subjects (such as pricing, service, account, and so on) are counted and prioritized depending on how frequently they are mentioned. This is a great way to rapidly find popular topics and issues among your visitors.

 

Word Groups

A collection of words can often give you more information than a single phrase. When phrases like “charges,” “expensive,” and “monthly” are combined, you can reasonably deduce that many clients believe the monthly costs for one of your products or services are excessively high. However, you can always open the individual comments to get a closer look.

 

Sentiment Analysis

You now know how frequently specific terms appear and how they are classified after using the previous strategies, but is this feedback positive, negative, or neutral? Fortunately for you, your consumers are likely to supply you with input on topics about which they are passionate, so gauging sentiment shouldn’t be a problem if you have the correct tool in place.

Sentiment analytics (also known as Opinion Mining) is a branch of Natural Language Processing (NLP) that evaluates feedback.  Most words do not have a positive, negative or neutral connotation per say, positive or negative classification is made based on in depth analysis and interpretation of the feedback.

 

Tagging Feedback

When sorting through feedback, it’s also crucial to have a system in place that allows you to filter and locate feedback based on the contents of open comments. This can be accomplished through manual tagging or by using -, which does the tagging and classification for you.

The latter requires the user to begin by manually marking feedback items. To improve this process, the user can give feedback to the machine on whether or not the comments were properly classified. This is accomplished through Supervised Machine Learning, which is essentially a method of teaching a system to do what you want it to do by giving it with examples from which to learn.

 

Want to get started with text analytics? 

Text analytics enables businesses to extract useful information from a wide range of data sources, including customer support requests and market interactions. Text analytics can reveal patterns, trends, and actionable insights that you can use to make data-driven decisions by combining the findings of text analysis and using business intelligence tools to convert numbers into insightful reports.

You can use text analytics tools to detect opportunities for improvement and adapt your product or service to your clients’ needs and expectations after analyzing customer feedback (like product reviews or NPS responses) or examining the content of customer support tickets with text analysis tools. Text analytics can be simple to get started with because there are many online tools available to perform text analysis. For one-time or ongoing text analysis projects like producing word clouds, Semeon is your ally of choice. 

Here are some of the features we provide:

  • You may drag and drop data from any public or private source into Semeon’s feedback platform, including survey results, social media content, blogs, and support tickets.
  • Semeon easily separates the positive, negative, and neutral ideas obtained from your data using green, red, and gray color coding.
  • Semeon saves you hours of time by extracting completely articulated concepts such as “Problem shipping to Canada,” rather than single-word phrases such as “Problem”.

Our team is made up of data visualization experts who help organizations turn static data into dynamic and informative visualizations, charts, and tables. If you wish to improve your data analysis and marketing skills, feel free to contact us or check out our professional services.

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