7 Benefits of Text Analysis for Commenters (2023)

Customers are people and people are passionate. So if they're talking about a new oneCustomer serviceExperience or a product they love or hate, open ended questions and reviews allow customers to go beyond a score or rating and express their passion (or lack thereof) for a brand and what it offers. and thecustomer feedback- condirect or indirect- is exactly what you need to make a real improvementCustomer Experience (CX).

Fortunately for companies, little can be kept secret in the digital age. When your customers buy or inquire about your products and services, they make them available to youcustomer signsin different ways. And as long as you collect feedback and use that data ethically, consumers will be set.

When collecting feedback data, there is one area you should pay special attention totext analysis. When done right, it greatly improves an organization's understanding of its target audience and enables it to better serve customers.

The benefits of text analysis

In addition to improving decision making, text analytics offers benefits such as increased processing speed, big data integration, better consistency, and cost savings. This is the reason why the global text analytics market is growing rapidly. This is expected by 2026Market cap of $14.84 billion, up from $5.46 billion in 2020. This represents a CAGR of 17.35% for the period.

Read on to learn how to take advantage of text analytics andTake your organization to the next level.

#1. Increase information with fewer questions

customer surveysthey are a great way to get feedback. However, they are sometimes monotonous and exhausting, which leads to research fatigue. As a result, customers may feel less motivated to take future surveys. That's what the data suggests.Only 9% of respondents complete longer surveys.

However, shorter surveys result in better response rates. To get at least the same level of knowledge with fewer questions, organizations need to ask more open-ended questions, such as "What else do we need to know?" — that generate text-based responses.

#two. Getting to the bottom of the cause

While comment scores and ratings are a barometer of your comments, scores don't usually reveal the "why" behind them. Follow-up ranking questions can deepen your understanding of "why," but typically not as well as text-based feedback. Open customer feedback provides the details needed to identify the root cause of a problem so teams know how and where to improve.

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#3. find out in time

Pick aCustomer Experience Management (CEM)Software platform that offers native text analysis so there are no delays in obtaining information. Systems with non-native text analysis force you to wait until you understand the root cause of a problem: the system collecting the feedback has to send the data to a text analysis system, which in turn takes time to analyze the feedback. data first. The additional time not only delays insights and improvement actions, but can also lead to more dissatisfied customers.

#4. Identify emerging trends

Most feedback programs and even review sites have a specific set of questions that they ask customers. have a visionCustomer Satisfaction (CSAT)Over time, these questions rarely change and are limited to scores and leaderboards. While responses may show trends in opinion on the topics of the questions, they may not show new and emerging trends that are not addressed in the questions.

Text comments close this gap. If a customer can't find a question that allows them to give feedback, for example, if the questions refer to hotel check-out time, but the guest wants to complain about the accuracy of the account, open-ended questions allow customers to they continue to give their feedback, only in text format.

By using text analytics, you can identify emerging issues and respond to them before they escalate.

#5. understand customer needs

Text analysis helps you understand customer needs by identifying keywords, themes, andFeelingin the feedback comments. Feedback collected from customers also reveals trends and insights. With this wealth of actionable information, you'll understand the strengths and weaknesses of your business.

For example, if you manage a hotel and several guests complain about the lack of room service, you can use this knowledge to improve their experience. But if guests are still raving about your staff or comfortable beds, you can focus on those areas.

In general, text-based feedback analysis helps a business understand the wants, needs, and expectations of its consumers so that it can successfully personalize and improve products or services.consumer loyaltymiretentionhow preferences are met.

#6. Make data-driven decisions

One of the main benefits of text analytics is that it allows you to make important business decisions based on data. For example,unstructured dataotherwise, invisible customer requirements and preferences can be revealed from open-ended survey questions and ratings.

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Text analytics uncovers key themes and sentiments in consumer feedback and tracks changes over time. For example, you can gauge customer sentiment after introducing a new offer or making changes to an existing product or service based on customer feedback.

This data drives product development and strategic customer service decisions. Prioritize improvements based on their impact on customer satisfaction and loyalty, and track your progress over time.

#7. Improve customer and employee experience

Along with great product quality, you need to offer great CX andEmployee Experience (EX)for your brand to grow. you can solvecustomer journeyPain points by studying consumer feedback and improving product features, customer service, website design, etc.User experience (UX).

Text analysis also shows improvements in the employee experience. Analyze employee data, such aseffort grade,Commitment,satisfaction, zFeelinghelps you discover and solve common problems. Some areas that can help you improve include training and development, work culture, andemployee onboarding.

Improving CX and EX creates a virtuous cycle that increases customer satisfaction, loyalty, and employee engagement and retention. Gains and expansion may result.

Studies show that companies that focus on the customer and employee experience outperform their competitors in terms of revenue growth and profitability. Therefore, text analysis can increase both customer and employee satisfaction.

Possible challenges in text analysis

As with all types of technology, text analysis has obstacles that can be encountered. Therefore, to reap the full benefits, you need to understand and mitigate the potential challenges of text analysis.

Here are some of the challenges you may encounter in text analysis and how to deal with them.

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#1. data quality

Text analytics data must be accurate and of high quality to be successful. Bad data leads to misleading perceptions and unfortunate decisions. Factors that can affect data quality include duplication of data, missing data, inconsistent formatting, and human error.

To improve data quality, perform data cleansing. This process includes removing unnecessary or duplicate data, correcting formatting errors, and adding missing data. Data cleaning can increase data quality and ensure reliable analysis results.

The combination of automated and human data processing improves data quality. Manual data processing allows analysts to analyze and verify the accuracy of the data, while automated data processing processes large amounts of data quickly. By solving data quality problems, you improve understanding of text analysis and decision making.

#two. Integration with existing systems

For best results, text analytics should work well with your existing systems. However, integrating text analytics can be challenging. You need customer feedback, social media, support ticket data, and more. However, this data is often dispersed across platforms, formats, and locations, making it difficult to search without thebest CEM software platform.

Therefore, IT, data science, and customer service teams must work together during the integration. Data sources are identified, data is extracted, standardized, and fed into the software platform used for CX.

When integrating, make sure that the data is also protected. This is accomplished through encryption and access restrictions to protect sensitive data.

Despite the hurdles, text analytics must be properly integrated to gain complete insight into customer behavior and preferences and make data-driven decisions.

#3. lack of standardization

Another area where text analysis can face challenges is consistency. This is because unstructured text data is difficult to analyze and understand. Also, the language, grammar, and spelling are not standardized, which could affect accuracy.

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As a result, jargon and terminology can make it difficult to apply text analytics across organizations and domains.

#4. complexity of the analysis

As you can imagine, text analytics is a complicated science that requires technological expertise to analyze unstructured data. Additionally, cleaning, organizing, and converting data for analysis requires time and resources. Therefore, the analysis of unstructured text data requires sophisticated software and algorithms.

Text analysis is commonly usedNatural Language Processing (NLP)Methods that can be complicated and computationally intensive. In addition, these systems have difficulty understanding idioms, sarcasm, and irony.

The complexity of analysis requires technical skills, software tools, and resources. Many companies may need to hire or train data analysts with NLP and unstructured data skills. A text analytics platform with built-in NLP and powerful algorithms can make analytics easy even for non-technical users.

Extend your feedback program with text analytics

There is no doubt that text analysis offers information to improve a feedback program. However, taking advantage of it is not an easy task. So instead of starting from scratch or doing the work manually, partner with a software vendor ready to unlock the value of text analytics.

Look for a software provider with experience enabling top brands across industries to collect feedback, analyze data, and deliver actionable insights, using text analytics to sift through massive amounts of text-based data. Your platform should enable you to quickly and continuously analyze vast amounts of feedback data, identify patterns and trends, and improve customer experience.

Let's goMedallia Text AnalysisHere's what you can expect:

  • ChallengingArtificial Intelligence (AI)mimachine learningTechniques for evaluating unstructured data
  • Analysis of feedback data in real time to help companies face new challenges
  • Dashboards and reports customized for easy viewing and sharing with stakeholders across the organization
  • integration withCustomer relationship management (CRM)and other systems allow you to improve procedures without affecting workflow.

Want to unlock the value of customer insights? download ourOfficial Text Analysis Brochureto learn more about our solutions.

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What are the benefits of text analysis? ›

The benefits of text analysis in research include: Text analysis allows you to compare and analyze vast amounts of data. Text analysis can be used to identify trends, predict outcomes, and make decisions based on your findings. It helps you to quickly answer questions about your data.

What are the four functions of text analysis? ›

There are four major approaches to textual analysis: rhetorical criticism, content analysis, interaction analysis, and performance studies.

What are the benefits of sentiment analysis? ›

This provides you an edge over your competitors. For instance, when your competitor launches a new product or service, you can use sentiment analysis to understand what customers like and dislike about their offering and use that feedback to improve your own products or services.

What are the results of text analysis? ›

Text analysis delivers qualitative results and text analytics delivers quantitative results. If a machine performs text analysis, it identifies important information within the text itself, but if it performs text analytics, it reveals patterns across thousands of texts, resulting in graphs, reports, tables etc.

What are the 5 elements of text analysis? ›

The elements to be analyzed are plot, setting, characters, point of view, figurative language, and style.

What are the key elements of text analysis? ›

They might analyze many different aspects of the text:
  • Word choice.
  • Design elements.
  • Location of the text.
  • Target audience.
  • Relationship with other texts.
Nov 8, 2019

What are 4 characteristics of text? ›

Characteristics of literary text include characters, setting, plot (problem/solution), and sequence. These characteristics help the reader understand who is in the story, where and when the story takes place, what happens in the story, and how the events happen, etc.

What are the main goals of sentiment analysis? ›

Sentiment analysis is target-oriented, aiming to identify opinions or attitudes towards topics or entities (e.g., product, movie). Emotion recognition, on the other hand, focuses on recognizing either the emotion expressed in text or evoked by the text, with no attachment to a specific target.

What are three important components of sentiment analysis? ›

Feelings, trends and value: Three key elements of sentiment analysis.

What are the benefits of sentiment analysis in education? ›

Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce.

Why is text analysis important in reading? ›

Analyzing is a vital skill for successful readers. Analyzing a text involves breaking down its ideas and structure to understand it better, think critically about it, and draw conclusions.

What are the three steps of text analysis? ›

The common focus across all methods used in QTA is that they can be reduced to three basic steps: firstly we need to define a corpus from the texts we want to examine; secondly we need to determine what our unit of analysis will be; and finally we need to put a document feature matrix together.

What are examples of text analysis? ›

Text analysis is really the process of distilling information and meaning from text. For example, this can be analyzing text written in reviews by customers on a retailer's website or analysing documentation to understand its purpose.

What are the big 5 analysis? ›

The five broad personality traits described by the theory are extraversion (also often spelled extroversion), agreeableness, openness, conscientiousness, and neuroticism.

What is the Big 5 analysis technique? ›

The big 5 assessment is a handy way for recruiters to determine the personality of job-seeking candidates by assessing five key traits —agreeableness, neuroticism, extraversion, openness to new experiences and conscientiousness.

What are the 5 main text structures? ›

There are thought to be five common text structures: description, cause and effect, compare and contrast, problem and solution, and sequence (Meyer 1985).

What are the 7 elements of text? ›

These elements are character, plot, setting, theme, point of view, conflict, and tone. All seven elements work together to create a coherent story. When you're writing a story, these are the fundamental building blocks you should use. You can approach the seven elements in any order.

What are the 7 elements of text structure? ›

  • Comparison.
  • Cause and effect.
  • Problem and solution.
  • Sequence.
  • Description.

What are the 4 key features of writing a textual analysis essay? ›

W-6a Key Elements of a Textual Analysis
  • A summary of the text. Your readers may not know the text you are analyzing, so you need to include it or tell them about it before you can analyze it. ...
  • Attention to the context. ...
  • A clear interpretation or judgment. ...
  • Reasonable support for your conclusions.

What are the 4 main types of text? ›

The main types of text types are narrative, descriptive, directing, and argumentative. However, there can be different types of text in a text type: the boundaries of text types are not always clear. According to some, we are increasingly confronted with texts that contain a wide variety of text types.

What are the four 4 types of informational text features? ›

There are four basic informational text types: literary nonfiction, expository writing, argumentative writing (also known as persuasive writing), and procedural writing.

What are the four main steps of sentiment analysis? ›

Getting started with sentiment analysis: the four main steps
  • Step 1: data gathering. First of all, we need the data that we will later analyze. ...
  • Step 2: text cleaning. Text cleaning tools will allow us to process the data and prepare it for analysis by: ...
  • Step 3: analyzing the data. ...
  • Step 4: understanding the results.
Sep 9, 2022

What is the difference between text analysis and sentiment analysis? ›

Text analytics extracts relevant information from unstructured text to give it meaning. In turn, sentiment analysis deciphers the emotions expressed by a body of text.

What are the key words for sentiment analysis? ›

Sentiment analysis tools categorize pieces of writing as positive, neutral, or negative. Positive sentiment may be expressed using words such as “good”, “great”, “wonderful”, and “fantastic”. Negative sentiment may be expressed using words such as “bad”, “terrible”, “awful”, and “disgusting”.

What are the two basic techniques for sentiment analysis? ›

Sentiment analysis uses machine learning and natural language processing (NLP) to identify whether a text is negative, positive, or neutral. The two main approaches are rule-based and automated sentiment analysis.

What is the best method for sentiment analysis? ›

  • Lexicon-based Methods. Lexicon-based, also known as knowledge-based approaches, are pre-developed manually and refer to analyzing semantic and syntactic (i.e., patterns in grammatical syntax) patterns. ...
  • Automated/Machine Learning Methods. ...
  • Hybrid approaches.
Aug 8, 2022

Why does sentiment analysis matter? ›

Sentiment analysis allows you to identify and target customers with highly positive or negative feelings about your brand or product. Extreme feedback can be useful for creating targeted marketing strategies and campaigns.

How might you use sentiment analysis to improve your performance? ›

What is Sentiment Analysis Used For?
  1. Track customer perception. “It comes down to how your customer experiences the brand – and how that brand makes a person feel.” ...
  2. Step-up customer service. ...
  3. Plan Product Improvements. ...
  4. Prevent an upcoming crisis. ...
  5. Gain competitor insights.
Nov 22, 2019

Why is social sentiment analysis important? ›

Social media sentiment analysis helps businesses identify when and how to engage with their customers directly. Publicly responding to a negative sentiment and solving a customer's problem can do wonders for your brand's reputation.

What is the purpose of a text analysis essay? ›

The purpose of a literary analysis essay is to carefully examine and sometimes evaluate a work of literature or an aspect of a work of literature. As with any analysis, this requires you to break the subject down into its component parts.

How does text analysis help us in real life cases? ›

Common use cases for Text Analysis

You can also use it to detect disease outbreaks by discovering cases in social media data. Research: Researchers use Text Analysis with AI to explore pre-existing literature to identify trends and patterns - or categorize research survey answers by topic or sentiment.

What is 3 types of text in content analysis? ›

Content analysis is a widely used qualitative research technique. Rather than being a single method, current applications of content analysis show three distinct approaches: conventional, directed, or summative.

What does analyzing a text mean? ›

When you analyze a text, you give it meaning beyond what the text tells you directly. What is analysis? When you analyze a text, you ask questions about it so that you can offer an interpretation of the text.

How important is literary text analysis? ›

The purpose of a literary analysis is to demonstrate why the author used specific ideas, word choices, or writing structures to convey his or her message.

Why is text analysis important in education? ›

Textual analysis techniques in education have been successfully applied to analyze students' answers and make better judgment on their performance [23] , to extract interesting and high-quality information from unstructured text [24] and mainly to topic modeling for different purposes, such as discovering important ...

What is the primary purpose of analysis? ›

An analysis uses facts of the story to support logical conclusions about the story, such as whether the central character is static or dynamic.

What are the 3 purposes of literary text? ›

The three basic purposes are to inform, to persuade, and to entertain.


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