Linguistic Analysis Explained

Editor’s note: This post was originally published on Ascribe and has been updated to reflect the latest data

Figuring out what humans are saying in written language is a difficult task.  There is a huge amount of literature, and many great software attempts to achieve this goal.  The bottom line is that we are a long way off from having computers truly understand real-world human language.  Still, computers can do a pretty good job at what we are after. Gathering concepts and sentiment from text.

The term linguistic analysis covers a lot of territory.  Branches of linguistic analysis correspond to phenomena found in human linguistic systems, such as discourse analysis, syntax, semantics, stylistics, semiotics, morphology, phonetics, phonology, and pragmatics. We will use it in the narrow sense of a computer’s attempt to extract meaning from text – or computational linguistics.

Linguistic analysis is the theory behind what the computer is doing.  We say that the computer is performing Natural Language Processing (NLP) when it is doing an analysis based on the theory.  Linguistic analysis is the basis for Text Analytics.

There are steps in linguistic analysis that are used in nearly all attempts for computers to understand text.  It’s good to know some of these terms.

Here are some common steps, often performed in this order:

1. Sentence detection

Here, the computer tries to find the sentences in the text. Many linguistic analysis tools confine themselves to an analysis of one sentence at a time, independent of the other sentences in the text.  This makes the problem more tractable for the computer but introduces problems.

“John was my service technician.  He did a super job.“

Considering the second sentence on its own, the computer may determine that there is a strong, positive sentiment around the job.  But if the computer considers only one sentence and individual word at a time, it will not figure out that it was John who did the super job.

2. Tokenization

Here the computer breaks the sentence into words. Again, there are many ways to do this, each with its strengths and weaknesses.  The quality of the text matters a lot here.

“I really gotmad when the tech told me *your tires are flat*heck I knew that."

Lots of problems arise here for the computer. Humans see “gotmad" and know instantly that there should have been a space. Computers are not very good at this.  Simple tokenizers simply take successive “word" characters and throw away everything else.  Here that would do an OK job with flat*heck → flat heck, but it would remove the information that your tires are flat is a quote and not really part of the surrounding sentence.  When the quality of text, syntax, or sentence structure is poor, the computer can get very confused.

This can also pose a problem when new words are introduced, or there are multiple meanings of words in one response or group of responses.

3. Lemmatization and cleaning

Most languages allow for multiple forms of the same word, particularly with verbs. The lemma is the base form of a word. So, in English, was, is, are, and were are all forms of the verb to be. The lemma for all these words is be.

There is a related technique called stemming, which tries to find the base part of a word, for example, ponies → poni.  Lemmatization normally uses lookup tables, whereas stemming normally uses some algorithm to do things like discard possessives and plurals.  Lemmatization is usually preferred over stemming.

Some linguistic analysis attempt to “clean up" the tokens.  The computer might try to correct common misspellings or convert emoticons to their corresponding words.

4. Part of speech tagging

Once we have the tokens (words) we can try to figure out the part of speech for each of them, such as noun, verb, or adjective. Simple lookup tables let the computer get a start at this, but it is really a much more difficult job than that.  Many words in the English language can be both nouns and verbs (and other parts of speech).  To get this right, the words cannot simply be considered one at a time.  The use of language can vary, and mistakes in part of speech tagging often lead to embarrassing mistakes by the computer.

Common Linguistic Analysis Techniques Explained

Most linguistic analysis tools perform the above steps before tackling the job of figuring out what the tokenized sentences mean.  At this point, the various approaches to linguistic analysis diverge.  We will describe in brief the three most common techniques.

Approach #1: Sentence parsing

Noam Chomsky is a key figure in linguistic theory.  He conceived the idea of “universal grammar", a way of constructing speech that is somehow understood by all humans and used in all cultures.  This leads to the idea that if you can figure out the rules, a computer could do it, and thereby can understand human speech and text.  The sentence parsing approach to linguistic analysis has its roots in this idea.

A parser takes a sentence and turns it into something akin to the sentence diagrams you probably did in elementary school:

At the bottom, we have the tokens, and above them classifications that group the tokens.  V = verb, PP = prepositional phrase, S = sentence, and so on.

Once the sentence is parsed the computer can do things like give us all the noun phrases.  Sentence parsing does a good job of finding concepts in this way.  But parsers expect well-formed sentences to work on.  They do a poor job when the quality of the text is low.  They are also poor at sentiment analysis.

Bitext is an example of a commercial tool that uses sentence parsing.  More low-level tools include Apache OpenNLP, Stanford CoreNLP, and GATE.

Approach #2: Rules-Based Analysis

Rules-based linguistic analysis takes a more pragmatic approach.  In a rule-based approach, the focus is simply on getting the desired results without attempting to really understand the semantics of the human language.  Rules-based analysis always focuses on a single objective, say concept extraction.  We write a set of rules that perform concept extraction and nothing else.  Contrast this with a parsing approach, where the parsed sentence may yield concepts (nouns and noun phrases) or entities (proper nouns) equally well.

Rules-based linguistic analysis usually has an accompanying computer language used to write the rules.  This may be augmented with the ability to use a general-purpose programming language for certain parts of the analysis.  The GATE platform provides the ability to use custom rules using a tool it calls ANNIE, along with the Java programming language.

Rules-based analysis also uses lists of words called gazetteers.  These are lists of nouns, verbs, pronouns, and so on.  A gazetteer also provides something akin to lemmatization.  Hence the verbs gazetteer may group all forms of the verb to be under the verb be.  But the gazetteer can take a more direct approach.  For sentiment analysis the gazetteer may have an entry for awful, with sub-entries horrible, terrible, nasty.  Therefore, the gazetteer can do both lemmatization and synonym grouping.

The text analytics engines offered by SAP are rules-based.  They make use of a rule language called CGUL (Custom Grouper User Language).  Working with CGUL can be very challenging.

Here is an example of what a rule in the CGUL language looks like:

#subgroup VerbClause: {

 (

   [CC]

   ( %(Nouns)*%(NonBeVerbs)+)

   |([OD VB]%(NonBeVerbs)+|%(BeVerbs) [/OD])

   |([OD VB]%(BeVerbs)+|%(NonBeVerbs)+ [/OD])

   [/CC]

 )

 | ( [OD VB]%(NonBeVerbs)[/OD]   )

}

At its heart, CGUL uses regular expressions and gazetteers to form increasingly complex groupings of words.  The final output of the rules is the finished groups, for example, concepts.

Many rules-based tools expect the user to become fluent in the rule language.  Giving the user access to the rule language empowers the user to create highly customized analyses, at the expense of training and rule authoring.

Approach #3: Deep learning and neural networks

The third approach we will discuss is machine learning.  The basic idea of machine learning is to give the computer a bunch of examples of what you want it to do, and let it figure out the rules for how to do it.  This basic idea has been around for a long time and has gone through several evolutions.  The current hot topic is neural networks.  This approach to natural language machine learning is based loosely on the way our brains work.  IBM has been giving this a lot of publicity with its Watson technology.  You will recall that Watson beat the best human players of the game of Jeopardy.  We can get insight into machine learning techniques from this example.

The idea of deep learning is to build neural networks in layers, each working on progressively broader sections of the problem.  Deep learning is another buzzword that is often applied outside of the area intended by linguistic researchers.

We won’t try to dig into the details of these techniques, but instead, focus on the fundamental requirement they have.  To work, machine learning and artificial intelligence need examples.  Lots of examples.  One area in which machine learning has excelled is image recognition.  You may have used a camera that can find the faces in the picture you are taking.  It’s not hard to see how machine learning could do this.  Give the computer many thousands of pictures and tell it where the faces are.  It can then figure out the rules to find faces.  This works really well.

Back to Watson.  It did a great job at Jeopardy.  Can you see why?  The game is set up perfectly for machine learning.  First, the computer is given an answer.  The computer’s job is to give back the correct question (in Jeopardy you are given the answer and must respond with the correct question).  Since Jeopardy has been played for many years, the computer has just what it needs to work with: a ton of examples, all set up just the way needed by the computer.

Now, what if we want to use deep learning to perform sentiment and language analysis?  Where are we going to get the examples?  It’s not so easy.  People have tried to build data sets to help machines learn things like sentiment, but the results to date have been disappointing.  The Stanford CoreNLP project has a sentiment analysis tool that uses machine learning, but it is not well regarded.  Machine learning today can deliver great results for concept extraction, but less impressive results for sentiment analysis.

BERT

Recent advances in machine learning language models have added exciting new tools for text analysis.  At the forefront of these is BERT, which can be used to determine whether two phrases have similar meanings.

BERT stands for Bidirectional Encoder Representations from Transformers.  This technique has been used to create language models from several very large data sets, including the text from all of Wikipedia.  To train a BERT model a percentage of the words in the training data set are masked, and BERT is trained to predict the masked words from the surrounding text.  Once the BERT model has been trained we can present two phrases to it and ask how similar in meaning they are.  Given the phrases, BERT gives us a decimal number between 0 and 1, where 0 means very dissimilar and 1 means very similar.

Given the phrase “I love cats", BERT will tell us the phrase “felines make great pets" is similar, but “it is raining today" is very dissimilar.  This is very useful when the computer is trying to tell us the main themes in a body of text.  We can use tools such as sentence parsing to partition the text into phrases, determine the similarity between phrases using BERT, and then construct clusters of phrases with similar meanings.  The largest clusters give us hints as to what the main themes are in the text.  Word frequencies in the clusters and the parse trees for the phrases in the clusters allow us to extract meaningful names for each cluster.  We can then categorize the sentences in the text by tagging them with the names of the clusters to which they belong.

Summary

Linguistic analysis is a complex and rapidly developing science.  Several approaches to linguistic analysis have been developed, each with its own strengths and weaknesses.  To obtain the best results you should choose the approach that gives superior performance for the type of analysis you need.  For example, you may choose a machine learning approach to identify topics, a rules-based approach for sentiment analysis, and a sentence parsing approach to identify parts of speech and their interrelationships.

If you’re not sure where to start on your linguistic and semantic analysis endeavors, the Ascribe team is here to help. With CXI, you can analyze open-ended responses quickly with the visualization tool – helping to uncover key topics, sentiments, and insights to assist you in making more informed business decisions. By utilizing textual comments to analyze customer experience measurement, CXI brings unparalleled sentiment analysis to your customer experience feedback database.

Selecting the right market research survey tool is crucial for market research firms and corporate research teams aiming to gather meaningful insights efficiently. The right platform can streamline data collection, enhance analysis, and empower teams to make data-driven decisions with confidence. Whether you’re conducting large-scale studies or niche market analysis, choosing a tool that aligns with your research objectives, workflow, and organizational needs can significantly impact the quality and accuracy of your findings

In this blog, we’ve compiled a list of 15 best software for market research to help you find a market research tool that fits your needs and suits your budget.

But first, let’s take a closer look at some key concepts related to market research.

What is Market Research?

Market research is the process of gathering, analyzing, and interpreting information about a specific market, industry, or target audience to support informed business decisions. Market research results are used to make informed decisions regarding product development, pricing strategies, marketing campaigns, distribution channels, and overall business planning.

It is a crucial marketing and business strategy component, helping organizations understand their customers, competitors, and the overall business environment.

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Choosing the Right Market Research Tool Matters.

Market researchers benefit greatly from using tools specifically designed for their needs because these platforms are built to handle the complexities of collecting, analyzing, and interpreting data at scale. Specialized tools offer features like advanced survey design options, robust sampling methods, real-time data collection across multiple channels, and sophisticated analytics — all crucial for extracting meaningful insights.

For Market Research Firms: For a market research company, using a survey platform offers several key benefits that help optimize resources, improve efficiency, and deliver high-quality insights. Here’s a closer look at the advantages:

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1. Cost-Effectiveness:

• Reduces the need for expensive custom-built solutions or manual processes.
• Offers scalable pricing plans that fit smaller budgets while providing essential features.

2. Time Efficiency:

• Automates survey creation, distribution, and data collection, saving valuable time.
• Provides pre-built templates and question libraries to speed up project setup.

3. Enhanced Data Quality:

• Built-in tools like skip logic, randomization, and validation rules help ensure clean, reliable data.
• Reduces errors associated with manual data entry and processing.

4. Access to Advanced Analytics:

• Offers real-time data visualization and analysis, making it easier to identify trends quickly.
• Includes tools for deeper insights, like text analysis and segmentation, without needing separate software.

5. Improved Reach and Response Rates:

• Supports multi-channel distribution (email, web, SMS, etc.) to reach diverse audiences.
• Automates reminders and follow-ups to increase response rates.

6. Professional Presentation:

• Provides polished, branded surveys that enhance credibility and respondent engagement.
• Allows for customization to align with the firm’s branding.

7. Scalability and Flexibility:

• Adapts easily to different project sizes, from small-scale studies to more complex research.
• Offers flexibility to conduct diverse types of research, like customer satisfaction or product testing.

8. Competitive Edge:

• Gives small firms access to the same powerful tools used by larger agencies, leveling the playing field.
• Allows faster turnaround on projects, improving client satisfaction.

9. Collaboration and Client Management:

• Facilitates teamwork with multi-user access and role-based permissions.
• Provides sharable reports and dashboards for transparent communication with clients.
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Now, let's look at some of the best market research tools & software.

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List of the 10 Best Market Research Tools & Software

1. Voxco Survey Software for Market Research

Market researchers choose Voxco because it offers a powerful, multi-channel platform that combines flexibility, scalability, and deep analytics to deliver high-quality insights. Voxco stands out with its multi-channel survey capabilities, allowing researchers to reach respondents anytime, anywhere — whether online, by phone, or face-to-face. Its intuitive design tools, robust sampling options, and real-time reporting empower researchers to streamline their workflow while ensuring data accuracy and reliability. With enterprise-grade security, seamless integrations, and expert support, Voxco equips market researchers worldwide with everything they need to conduct complex studies, uncover meaningful insights, and make data-driven decisions with confidence.

• Multi-channel data collection (online, phone, face-to-face)
• Advanced analytics and real-time reporting
• Secure, scalable, and highly customizable

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2. Ascribe Text Analytics

Market researchers use Ascribe because it transforms open-ended feedback into meaningful insights with speed and accuracy. Ascribe's powerful text analytics and coding tools help researchers efficiently process large volumes of unstructured data — from survey responses to social media comments — identifying key themes, sentiments, and trends. The platform streamlines manual coding, offers AI-driven automation, and ensures consistency across projects, saving time while enhancing data quality. With Ascribe, researchers can dig deeper into qualitative data, uncover the "why" behind the numbers, and deliver richer, more actionable insights that drive better business decisions.

• AI-powered text analytics and coding 
• Extracts themes, sentiments, and trends from open-ended responses
• Enhances qualitative data analysis for deeper insights

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3. Remesh  

Remesh is an AI-driven market research tool that allows researchers to conduct live conversations with large groups of participants. It uses AI to analyze responses in real time, identifying trends and themes from qualitative feedback. Unlike traditional survey tools, Remesh enables dynamic audience engagement, making it ideal for gaining deep consumer insights.

• AI-powered real-time conversation analysis
• Engages large groups interactively
• Ideal for qualitative research and in-depth insights

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4. Qualtrics

Qualtrics is a leading market research platform that offers a wide range of features, including survey creation, data collection, and analysis. While it provides powerful capabilities for enterprises, it is often considered expensive, especially for small teams or organizations with limited budgets. The pricing structure can be complex, and additional features may require costly upgrades.

• Enterprise-grade survey and analytics platform
• Customizable surveys with advanced logic
• Expensive, with frequent price increases and complex pricing structures

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5. SurveyMonkey

SurveyMonkey is a popular survey tool that is easy to use and affordable. It offers a variety of survey templates and features, making it a good option for businesses new to market research, but it lacks the depth of advanced statistical tools, segmentation, and data modeling that market researchers often need.

• Simple and user-friendly survey builder
• Affordable for small businesses and startups
• Lacks advanced analytics for in-depth research

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6. Brandwatch

Brandwatch is a leading social listening tool that helps market researchers analyze online conversations, brand mentions, and consumer sentiment. It collects and processes data from social media, news, blogs, and forums, allowing researchers to track trends, identify emerging issues, and understand consumer perceptions.

• AI-powered social listening and sentiment analysis
• Monitors brand perception across multiple online channels
• Ideal for competitor analysis and trend spotting

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7. Typeform

Typeform is a visually appealing survey tool that captures attention. It offers a variety of templates and features, making it a good choice for businesses that want to create engaging surveys. Typeform’s interactive format improves response rates, making it particularly useful for customer feedback and product testing.

• Interactive, conversational-style surveys
• Visually appealing and mobile-friendly
• Best for customer feedback and brand research

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8. Alchemer

Alchemer, formerly SurveyGizmo, is a powerful market research platform that offers a wide range of features, including survey creation, data collection, and analysis. It is a good choice for businesses that need a more complex solution with advanced customization and workflow automation.

• Highly customizable survey logic and workflows
• Advanced reporting and automation features
• Suitable for businesses needing complex research capabilities

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9. Toluna Start

Toluna Start is an end-to-end market research platform that combines survey software with an integrated respondent panel. Researchers can quickly collect insights from targeted demographics, making it a strong option for brands looking to validate product concepts, measure brand perception, or track consumer behavior.

• Integrated global respondent panel for fast insights
• Combines surveys with real-time analytics
• Great for concept testing and brand tracking

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10. QuestionPro

QuestionPro is a user-friendly market research platform that offers a variety of features, including survey creation, data collection, and analysis. It is a good choice for businesses that want a cost-effective solution with enterprise-grade functionalities.

• Affordable and easy-to-use survey platform
• Strong customization and analytics options
• Ideal for mid-sized businesses and market researchers

How to choose the best software for market research?

When choosing a market research software, it is important to consider your budget, the features you need, and the size of your business. Here are some additional things to consider when choosing market research software:

1. The size of your business: If you are a small business, you may not need all of the features offered by a large enterprise solution.

2. Your budget: Market research software can range in price from free to hundreds of dollars per month.

3. Your needs: What specific features are you looking for in market research software? Do you need to create surveys, collect data, or analyze results?

4. Your level of experience: If you are new to market research, choose software that is easy to use.

5. The type of market research you need to conduct: There are many types of market research, such as surveys, focus groups, and interviews. Choose software that is designed for the type of research you need to conduct.

6. The features offered by the software: The software you choose to conduct market research should have some non-negotiable features that we mentioned earlier in this blog. These features include survey templates, tools for data analysis, visual dashboards, etc.

7. The customer support offered by the software: Make sure the software provider offers good customer support in case you need help using the software.

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We recommend reading software reviews on credible sites like G2 and comparing options before deciding. Hence, you get an unbiased opinion of the market research tool you’re considering.

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Ready to choose the best software for market research?

Now that you’ve reviewed the list and compared the 10 best market research tools & software, choosing the one that fits your requirements shouldn’t be difficult.

No matter which market research tool you choose, conducting market research will push you closer to gaining the correct insights for your project. Remember to consider the factors that affect the choice of your tool. Be wise and choose right!

Understanding Text: A Broader Perspective

When we think of "text," many of us immediately picture words on a page or messages on our phones. However, in academic terms, "text" encompasses much more. It refers to any form of communication that carries meaning—whether it's written, spoken, visual, or even non-verbal. This includes everything from books and articles to movies, music, advertisements, and even body language.

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What is Textual Analysis in Research?

Textual analysis in research is the process of analyzing the language, symbols, and content of texts to reveal deeper meanings, patterns, and structures. Researchers use this method to interpret how specific texts communicate ideas, influence perceptions, and shape social, political, or cultural discourse.

Take, for instance, a political speech. Textual analysis can help researchers explore the rhetoric used, the ideological messages conveyed, and the potential impact on the audience's perceptions. By examining the text in detail, researchers can draw conclusions about the social, historical, or cultural implications of the speech.

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Key Methods of Textual Analysis

Several textual analysis methods are used across different disciplines. Let’s explore the most common approaches:

1. Rhetorical Criticism

Rhetorical criticism focuses on understanding how texts persuade and influence their audience. Researchers systematically analyze the purpose, context, and strategies used in the text to evaluate its effectiveness. The process typically involves:

• Purpose: What is the goal of the message?
  
  
• Context: How do historical, cultural, and social factors shape the text?
  
  
• Impact: How does the text influence society or its audience?
  
  
• Theory: Can the analysis contribute to broader theoretical insights?

This method is particularly useful in media, political, and public discourse studies.

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2. Content Analysis

Content analysis involves systematically identifying and quantifying specific elements within a text, such as recurring themes, words, or phrases. This can be done either qualitatively (analyzing the meaning behind the occurrences) or quantitatively (counting the frequency of specific elements).

• Qualitative Content Analysis: Focuses on understanding the deeper meanings embedded in the text.
  
  
• Quantitative Content Analysis: Involves counting occurrences of specific words or themes to identify patterns in large datasets.

While this method is often used to analyze large volumes of existing data, it can be a powerful tool for uncovering trends or shifts in discourse over time.

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3. Interaction Analysis

Interaction analysis takes a broader view by examining both verbal and non-verbal communication. This approach is often used in studies of conversations, group dynamics, or media content. Researchers analyze:

• Linguistic Features: Word choice, sentence structure, and tone.
  
  
• Non-Verbal Communication: Gestures, facial expressions, and body language.
  
  
• Contextual Factors: Social, cultural, and situational influences on communication.

This method is particularly valuable in communication studies, social sciences, and qualitative research where understanding human interaction is key.

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4. Performance Studies

Performance studies focus on the expressive and aesthetic elements of text. Researchers using this approach might perform the text themselves or observe how others interact with it. This process involves:

• Select: Choosing a text to examine.
  
  
• Play: Experimenting with different vocal and physical expressions.
  
  
• Test: Concluding how the text influences behavior or perceptions.
  
  
• Choose: Selecting the most valid interpretation of the text.
  
  
• Repeat & Present: Refining and presenting findings based on the analysis. 

This method is commonly used in literary studies, theater, and cultural studies, where the performance of the text plays a critical role in understanding its meaning.

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Where is Textual Analysis Applied?

Textual analysis finds applications in a wide variety of fields. Below are a few key areas where this method is particularly valuable:

1. Cultural and Media Studies

In media and cultural studies, researchers analyze a wide range of media—such as music, videos, advertisements, and images—as texts. By examining these forms, they can uncover the social and cultural contexts behind their creation and consumption. For instance, in a television commercial, textual analysis might explore how language, imagery, and symbolism influence consumer behavior.

2. Social Sciences

In the social sciences, textual analysis is often used to study interviews, focus groups, and other forms of communication. Researchers may employ quantitative textual analysis to count word occurrences or qualitative analysis to interpret the social meaning behind the text. This method helps sociologists, anthropologists, and psychologists uncover insights into human behavior, group dynamics, and societal trends.

3. Literary Studies

In literary studies, textual analysis is crucial for interpreting written works, including novels, poems, and plays. Researchers focus on how literary devices, such as metaphors, symbolism, and narrative structure, create meaning and convey deeper insights into human experience.

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Key Takeaways:

Textual analysis provides researchers with a powerful tool to critically examine and interpret the vast array of texts that shape our world. Whether it's understanding political rhetoric, analyzing media representations, or interpreting literary works, textual analysis helps researchers draw meaningful conclusions from the texts we encounter daily.

For businesses, organizations, and academic researchers, this method unlocks deeper insights into how communication shapes perceptions, behaviors, and societal trends. By applying textual analysis methods across various fields, organizations like Voxco and Ascribe can leverage data to drive better decision-making, improve audience engagement, and enhance content strategy.

In research, open-ended feedback often holds the richest insights—but it can be difficult to analyze at scale. That’s where thematic analysis becomes essential.

Whether you're a social researcher studying behaviors, a market researcher analyzing customer sentiment, or a corporate insights team uncovering employee concerns, thematic analysis helps transform qualitative data into structured, actionable insight.

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What Is Thematic Analysis?

Thematic analysis is a method used to identify and interpret recurring patterns (or “themes”) in qualitative data. It’s especially useful for analyzing open-ended survey responses, interview transcripts, product reviews, and other text-based sources.

By organizing raw text into themes, researchers can surface what matters most—without being limited to numeric data. Today, AI-powered tools like Ascribe make this process faster, more consistent, and scalable across datasets.

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How Thematic Analysis Works

At its core, thematic analysis in research follows these six steps:

1. Familiarization – Reading through your data to get a high-level sense of the content.
   
   
2. Coding – Highlighting keywords or ideas and assigning short labels ("codes").
   
   
3. Generating Themes – Grouping similar codes into broader categories.
   
   
4. Reviewing Themes – Refining themes to make sure they accurately reflect the data.
   
   
5. Defining & Naming Themes – Giving each theme a clear focus and meaning.
   
   
6. Writing Up – Interpreting and presenting findings in a compelling way.
   
   

Example: A software company collects survey responses from customers. Thematic analysis reveals key themes like “usability,” “customer support,” and “integration issues.” These insights guide product and CX improvements.

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Types of Thematic Analysis

There are several types of thematic analysis, depending on your research goals and how you approach the data:

1. Inductive

Themes emerge directly from the data—ideal for exploratory research with little prior theory.
→ Example: Analyzing first-time users’ experiences without preset assumptions.

2. Deductive

Analysis is guided by existing theories or predefined categories.
→ Example: Validating known CX pain points using open-ended survey responses.

3. Semantic

Focuses on the surface-level meaning of the data—what people explicitly say.
→ Best for: Clear, direct feedback (e.g., product reviews or short comments).

4. Latent

Looks beyond what's said to interpret underlying beliefs, assumptions, or emotions.
→ Best for: Deep, qualitative interviews or behavioral research.

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Why Use Thematic Analysis in Research?

• Empowers deeper insight: Understand motivations, perceptions, and pain points that numbers alone can’t capture.

• Highly flexible: Apply it across industries—from market research and social science to UX and HR.
• Compatible with AI: Platforms like Ascribe let you automate thematic analysis at scale, cutting time spent on manual coding while improving consistency.
• Rich storytelling: Thematic analysis supports both qualitative depth and quantifiable patterns—ideal for mixed-method research.

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Best Practices for Thematic Analysis

• Use AI for consistency and scale: Manual analysis can be subjective. AI-enabled tools help you surface key themes faster and more objectively.
  
  
• Review edge cases: Don’t ignore less frequent responses—outliers often reveal emerging trends.
  
  
• Align themes to business goals: Use tags that link back to your research objectives, like customer satisfaction, churn drivers, or service gaps.
  
  
• Visualize results clearly: Pair your themes with visuals (charts, dashboards) to make them digestible for stakeholders.
  
  

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Final Thoughts

Thematic analysis offers a practical way to turn open-ended feedback into meaningful conclusions. Whether you’re exploring consumer attitudes or employee engagement, the ability to uncover and quantify patterns in text is key.

When supported by the right technology, thematic analysis in research becomes not just manageable—but transformative.

Brands commonly use rating scales to collect customer feedback on products or services. Rating scale questions are recognizable and intuitive—respondents often don’t even need to fully read the question. We see smiley ratings or star ratings and immediately understand how to respond.

In this blog, we'll explore the types of rating scales, their practical applications, and best practices for effectively gathering customer feedback.

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What is a Rating Scale?

Rating scales are closed-ended questions offering a set of categories as response options. They are among the most common survey question types used for surveys. Rating scales help gather information on qualitative and quantitative attributes.

Common examples include the Likert scale, star rating, and slider. For instance, when shopping online, you might rate your purchase experience using a rating scale. These scales are popular in market research, effectively capturing quantifiable insights into product performance, employee satisfaction, customer service, and more.

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Types of Rating Scales

There are six common types of Rating Scales:

1. Numeric rating scale or NRS

A numeric rating scale uses numbers to identify the items in the scale. In this scale, not all numbers need an attribute attached to them.

For instance, you can ask your survey respondents to rate a product from 1 to 5 on a scale. You can assign ‘1’ as totally dissatisfied and ‘5’ as totally satisfied.

2. Verbal rating scale or VRS:

Verbal scales are used for pain assessment. Also known as verbal pain scores and verbal descriptor scale compiles a number of statements describing pain intensity and duration.

For instance, when you go to a dentist, you are asked to rate the intensity of your tooth pain. At that time, you receive a scale with items like “none,” “mild,” “moderate,” “severe,” and “very severe.”

3. Visual analog scale or Slider scale:

The idea behind VAS is to let the audience select any value from the scale between two endpoints. In the scale, only the endpoints have attributes allotted to numbers, and the rest of the scale is empty.

Often just called a slider scale, the audience can rate whatever they want without being restricted to particular characteristics or rank.

For example, a scale rating ranges from extremely easy to extremely difficult, with no other value allotted.

4. Likert scale:

A Likert scale is a useful tool for effective market research to receive feedback on a wide range of psychometric attributes. The agree-disagree scale is particularly useful when your intention is to gather information on frequency, experience, quality, likelihood, etc.

For example, a Likert scale is a good tool for evaluating employee satisfaction with company policies.

5. Graphic rating scale:

Instead of numbers, imagine using pictures, such as stars or smiley faces to ask your customers and audience to rate. The stars and smiley faces can generate the same value as a number.

6. Descriptive scale:

In certain surveys or research, a numeric scale may not help much. A descriptive scale explains each option for the respondent. It contains a thorough explanation for the purpose of gathering information with deep insights.

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How to Create an Effective Rating Scale Survey

To ensure clarity and maximize insights:

1. Determine the appropriate scale: Align scale type and response options clearly with your research objectives.
2. Implement suitable scales: Choose among the six scale types based on your data needs. Conduct pilot tests if unsure.
3. Maintain consistency: Use uniform ordering of scales (e.g., 1=low, 5=high) throughout your survey.
4. Balance response options: Provide balanced positive and negative options to reduce bias.
5. One idea per question: Avoid mixing multiple ideas in one question to maintain clarity.

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Advantages of Using Rating Scales in Surveys

• Ease of Use: Rating scales are simple and easy to understand for both researchers and respondents.
• Time-Efficient: They require minimal time for respondents to complete.
• Variety of Options: Multiple types of rating scales enable engaging and interactive surveys.
• Effective for Analysis: They provide valuable data for evaluating products, services, and overall marketing strategy improvement.

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Disadvantages of Using Rating Scales in Surveys

• Limited Qualitative Insights: Rating scales do not capture the reasoning behind respondents' answers.
• Lack of Depth: They measure overall perceptions without explaining specific experiences.
• Potential Overestimation: Verbal Rating Scales (VRS) might overstate subjective experiences like pain. Additionally, respondents with limited vocabulary may find verbal 

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Examples of Rating Scale Survey Questions

Here are some examples of rating scale questions: 

1. Customer Satisfaction Rating Scale Questions

• How satisfied are you with the newly launched live customer support chat service on our app?
• How likely are you to refer our podcast app to others?

2. Product feedback Rating Scale Questions

• Rate the quality of our latest product. (1-poor, 5-excellent)
• How easy was it to use the new doc scanner app?

3. Event Experience Rating Scale Questions

• How would you rate the organization of our recent event? 
• How likely are you to attend our summer event in the future?

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High-Level Applications of Rating Scales

Beyond basic feedback collection, rating scales can significantly influence strategic decision-making and organizational improvements:

1. Strategic Decision-Making: Businesses use rating scales to evaluate customer satisfaction over time, providing data-driven insights for strategic decisions. For example, continuous rating-based customer satisfaction surveys can identify long-term trends, guiding investment decisions in product development or service enhancement.

2. Benchmarking and Competitive Analysis: Rating scales enable businesses to perform competitive analysis by collecting comparative data. Companies can benchmark their products or services against competitors, gaining strategic insights into market positioning and potential areas for improvement.

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Conclusion

Rating scales are effective and versatile tools for survey research, providing valuable, actionable data. Remember:

• Clearly label scale endpoints.
• Balance positive and negative options to reduce bias.
• Include neutral points when appropriate.

Choosing the right scale type depends on your survey’s objectives. With platforms like Voxco, diverse rating scale options can enhance your research, delivering impactful insights.

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The global marketplace puts businesses in a position where you need to compete with organizations from around the world. Standing out on price becomes a difficult or impossible task, so the customer experience has moved into a vital position of importance. Customer loyalty and retention are tied to the way your buyers feel about your brand throughout their interactions. Customer experience analysis tools provide vital insight into the ways that you can address problems and lead consumers to higher satisfaction levels. However, knowing which type of tool to use and the ways to collect the data for them are important to getting actionable information.

Problems With Only Relying on Surveys for Customer Satisfaction Metrics

One of the most common ways of collecting data about the customer experience is through surveys. You may be familiar with the Net Promoter Score system, which rates customer satisfaction on a 1-10 scale. The survey used for this method is based off a single question — “How likely are you to recommend our business to others?” Other surveys have a broad scope, but both types focus on closed-ended questions. If the consumer had additional feedback on topic areas that aren't covered in the questions, you lose the opportunity to collect that data. Using open-ended questions and taking an in-depth look at what customers say in their answers gives you a deeper understanding of your positive and negative areas. Sometimes this can be as simple as putting a text comment box at the end. In other cases, you could have fill-in responses for each question.

How to Get Better Customer Feedback

To get the most out of your customer experience analysis tools, you need to start by establishing a plan to get quality feedback. Here are three categories to consider:

Direct

This input is given to your company by the customer. First-party data gives you an excellent look at what the consumers are feeling when they engage with your brand. You get this data from a number of collection methods, including survey results, studies and customer support histories.

Indirect

The customer is talking about your company, but they aren't saying it directly to you. You run into this type of feedback on social media, with buyers sharing information in groups or on their social media profiles. If you use social listening tools for sales prospecting or marketing opportunities, you can repurpose those solutions to find more feedback sources. Reviews on people's websites, social media profiles, and dedicated review websites are also important.

Inferred

You can make an educated guess about customer experiences through the data that you have available. Analytics tools can give you insight on what your customers do when they're engaging with your brand. Once you're collecting customer data from a variety of sources, you need a way to analyze it properly. A sentiment analysis tool looks through the customer information to tell you more about how they feel about the experience and your brand. While you can try to do this part of the process manually, it requires an extensive amount of human resources to accomplish, as well as a lot of time.

Looking at Product-specific Customer Experience Analytics

One way to use this information to benefit customer loyalty and satisfaction is by analyzing it on a product-specific basis. When your company has many offerings for your customers, looking at the overall feedback makes it difficult to know how the individual product experiences are doing. A sentiment analysis tool that can sort the feedback into groups for each product makes it possible to look at the positive and negative factors influencing the customer experience and judge how to improve sentiment analysis. Some of the information that you end up learning is whether customers want to see new features or models with your products, if they've responded to promotions during the purchase process, and if products may need shelves or need to be completely reworked.

Improving the Customer Experience for Greater Loyalty

If you find that your company isn't getting a lot of highly engaged customer advocates, then you may be running into problems generating loyalty. To get people to care more about your business, you need to fully understand your typical customers. Buyer personas are an excellent tool to keep on hand for this purpose. Use data from highly loyal customers to create profiles that reflect those characteristics. Spend some time discovering the motivations and needs that drive them during the purchase decision. When you fully put yourself in the customer's shoes, you can begin to identify ways to make them more emotionally engaged in their brand support. One way that many companies drive more loyalty is by personalizing customer experiences. You give them content, recommendations and other resources that are tailored to their lifestyle and needs.

Addressing Weak Spots in Customer Retention

Many factors lead to poor customer retention. Buyers may feel like the products were misrepresented during marketing or sales, they could have a hard time getting through to customer support, or they aren't getting the value that they expected. In some cases, you have a product mismatch, where the buyer's use case doesn't match what the item can accomplish. A poor fit leads to a bad experience. Properly educating buyers on what they're getting and how to use it can lead to people who are willing to make another purchase from your company. You don't want to center your sales tactics on one-time purchases. Think of that first purchase as the beginning of a long-term relationship. You want to be helpful and support the customer so they succeed with your product lines. Sometimes that means directing them to a competitor if you can't meet their needs. This strategy might sound counterintuitive, but the customers remember that you went out of your way to help them, all the way up to sending them to another brand. They'll happily mention this good experience to their peers. If their needs change in the future, you could end up getting them back. Customer loyalty and retention are the keys to a growing business. Make sure that you're getting all the information you need out of your feedback to find strategies to build these numbers up.