Survey text analysis is the process of analyzing open-ended survey responses to gain insights into participants' opinions, feelings, and attitudes.
In other words, it’s how you get real, actionable insights from free-form text survey responses.
Instead of only relying on quantitative responses (like number ratings or yes/no answers), survey text analysis allows you to dig into the qualitative data provided in written responses to open-ended questions.
This is why we created Blix, an AI-powered text analysis tool that makes this process fast and easy.
In this guide, we teach you why survey text analysis is important and how to do it effectively, so you can turn your survey data into actionable insights. Let’s dive in!
Why Survey Text Analysis is Important
Survey text analysis uncovers layers of meaning in feedback that numerical data alone can’t reveal. Here’s why businesses need it:
In-Depth Insights: Text responses provide the context behind quantitative feedback, making customer feedback data essential for understanding why customers feel a certain way. For example, if a customer rates their experience a 6/10, analyzing their written explanation reveals why they feel that way.
Identifying Pain Points: Recurring themes in negative responses highlight key issues. This allows businesses to quickly address complaints or prioritize fixes.
Customer Segmentation: Different segments of respondents—such as new versus long-term customers—may express different sentiments. Text analysis helps identify these variations, helping you to understand what different segments of your audience care about.
Trend Tracking: By analyzing survey responses over time, you can monitor shifts in sentiment or detect early signs of emerging issues.
Discovery and Innovation: Written feedback can uncover topics or ideas you hadn’t anticipated, providing valuable input for future research or product development. For example, customers might suggest adding a subscription option for discounts, and perhaps you hadn’t thought of that idea.
In other words — when done right, survey text analysis transforms scattered responses into a treasure trove of insights for improving products, services, and customer experiences.
Definition and Explanation of Survey Text Analysis
Survey text analysis is the process of delving into the written responses from customer surveys to uncover their true opinions, feelings, and attitudes towards a product, service, or brand.
Unlike numerical data, which can only tell you so much, text analysis digs deeper into the “why” behind customer feedback. This process employs various techniques, including natural language processing (NLP), sentiment analysis, and topic modeling, to extract meaningful insights from unstructured text data.
The goal of survey text analysis is to gain a richer, more nuanced understanding of customer needs, preferences, and pain points. By interpreting the free-form text data from customer surveys, businesses can make informed decisions that drive positive change — whether it’s improving a product, enhancing customer service, or innovating new solutions.
Brief Overview of Natural Language Processing (NLP) in Survey Text Analysis
Natural Language Processing (NLP) is a cornerstone of survey text analysis.
As a subfield of artificial intelligence, NLP focuses on the interaction between computers and human language. In the realm of survey text analysis, NLP is used to interpret the meaning behind text responses, analyzing elements such as sentiment, tone, and intent.
NLP algorithms can automatically categorize text data, identify patterns, and detect trends that would be challenging to uncover manually.
For instance, NLP can sift through thousands of customer survey responses to pinpoint common themes or sentiments, providing a clearer picture of customer opinions. This capability makes NLP an indispensable tool for transforming raw text data into actionable insights.
How Survey Text Analysis Works
There are four main steps in survey text analysis:
Step 1: Data Collection
The first step is to gather the responses from open-ended survey questions.
These responses can come from a variety of sources, such as customer feedback forms, employee surveys, product reviews, or focus groups.
Typically, you will gather the data into a spreadsheet for analysis. Once collected, this data is prepared for data analysis to interpret survey responses and understand the impact of various factors on customer experience metrics.
Step 2: Verbatim Coding
Once the responses are collected, the next step is to group similar feedback into categories or themes. In the market analyst world, these themes are called “codes”, and attaching codes to the feedback is called verbatim coding.
For example, a chocolate company ran a survey to come up with new product ideas. Their feedback might be categorized into themes like “energy boosters,” “chocolate obsession,” and “healthy but not really.”
This step helps organize unstructured data into manageable and meaningful clusters, making it easier to identify patterns and analyze the data for faster insights.
Step 3: Sentiment Analysis
Once you understand the themes in the data, the next step is sentiment analysis.
Sentiment analysis is a key component of natural language understanding, which helps in identifying coherent topics and quantifying emotional tones within the data. It’s used to determine the emotions of each response, identifying whether the feedback is positive, negative, or neutral.
For instance, while responses under “healthy but not really” might sound negative, they could actually be talking about something that brings them joy.
Sentiment analysis provides deeper insights into how respondents feel about specific topics.
Step 4: Insights Generation
The last step is to create a report showcasing the actionable insights uncovered from the data.
This can include identifying recurring issues, highlighting standout positive themes, or visualizing trends through word clouds, bar graphs, or pie charts. Blix can generate these reports automatically:
These insights enable decision-makers to act on the feedback with clarity and focus.
For example, in the report above you see negative sentiment about how the app is “hard to navigate” and the “buttons are too small”. You could use that feedback to increase the size of the buttons and directly improve the user experience based on real customer feedback.
And that’s how survey text analysis works!
Next, let’s use Blix to quickly and easily run through an example. Feel free to book a demo of the software and follow along once you have an account.
Create a Codebook With One Button
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First, gather your survey data into a spreadsheet, taking care to properly name the columns in the first row.
Here’s an example using a Pixar credit card survey:
Next, upload the spreadsheet into Blix. Log in, click New Project +, name your project, and select the spreadsheet with the survey responses. Select the column(s) with your question(s) and answer(s) and click ImportQuestions.
On the next screen, click Analyze.
You’ll then be asked to fill in the question asked in the survey — if your column name is the question, this will automatically populate. If not, type in the question(s) being asked.
Then, click Generate Themes and, after a short wait, you’ll be asked to “Refine your Codebook”. This is where you can choose to accept or adapt the “themes”, or codes, the AI generated for your responses. You can delete, add, or change the codes to suit your needs.
Once you’re happy with the codebook, you can click Apply tags to all data to have the software run the analysis for you. After a short wait, it provides you with a report of all the responses, categorized by code!
That’s all there is to it! You’ve now done your survey text analysis and gotten actionable insights to make changes to your business.
Next, let’s look at some real-world applications of survey analysis in action…
Survey text analysis has broad applications across various industries and scenarios, providing valuable insights to improve customer experiences, workplace engagement, and product development.
Here are some key use cases:
Customer Feedback Surveys
This is the most common type of survey and the type we’ve covered in the examples throughout this article. Businesses use survey text analysis to understand customer satisfaction, identify pain points, and gather feedback on products or services.
For example, analyzing survey responses might reveal common frustrations with delivery times or highlight features customers love about a product.
Employee Surveys
HR teams leverage text analysis to gauge employee morale, uncover workplace issues, and improve engagement. Open-ended responses can reveal insights into team dynamics, workplace culture, or specific challenges employees face, helping organizations make data-driven improvements.
Product Development Surveys
Analyzing feedback helps companies identify customer needs, frustrations, and desires, guiding the development of better products. For instance, recurring mentions of “ease of use” or requests for specific features can inform product design priorities.
Market Research Surveys
Survey text analysis provides insights into broader trends and consumer preferences, offering a competitive edge. By examining open-ended survey responses, businesses can uncover emerging market demands or test new concepts before launching a product.
Challenges and Considerations
There are some challenges to be aware of when analyzing survey text responses.
Dealing with Large Volumes of Text Data
One of the most significant challenges in survey text analysis is managing large volumes of text data.
As the amount of response data grows, manual analysis becomes impractical. This is where NLP and machine learning algorithms come into play, enabling businesses to process and analyze extensive datasets swiftly and accurately.
However, even with advanced technology, handling large volumes of text data requires meticulous planning, effective data management, and robust analysis strategies.
Luckily, AI-powered tools like Blix make this process easier.
Ensuring Accuracy in Text Analysis
Accuracy is paramount in text analysis to derive reliable insights from customer survey responses. Several factors can impact the accuracy of text analysis:
Ambiguity and Context: Text responses can be ambiguous or context-dependent, making it challenging to interpret their true meaning accurately. For example, a customer saying "It's fine" could mean they are satisfied or disappointed, depending on the situation.
Sarcasm and Irony: Detecting sarcasm and irony is notoriously difficult, often leading to misinterpretation of text responses. When a customer writes, "Oh, great service as always," they might genuinely praise or sarcastically criticize depending on their experience.
Language and Cultural Differences: Variations in language and cultural expressions can affect how customers articulate their thoughts, complicating the analysis. A British customer saying "Not bad" may imply they are satisfied, whereas an American customer might interpret it as lukewarm feedback.
Data Quality: Poor data quality, such as typos or incomplete responses, can skew the results of text analysis. A response like "Gud prdct but shipng bad" may confuse analysis software due to typos and incomplete phrasing.
To ensure accuracy, businesses can employ several techniques:
Use Multiple Algorithms: Leveraging multiple NLP algorithms can help validate results and improve accuracy.
Human Validation: Having human analysts review and validate the results ensures reliability.
Continuous Monitoring: Regularly updating and monitoring text analysis models helps maintain their accuracy and effectiveness over time.
By addressing these challenges and implementing these strategies, businesses can enhance the accuracy and reliability of their text analysis, leading to more actionable insights from customer survey responses.
Make Survey Text Analysis Simple & Easy with Blix
Ready to do your own survey text analysis? Book a free demo of Blix and get access to our powerful AI-driven text analysis software, making your analysis fast and easy.
Verbatim analysis software can unlock deeper insights in market research. Learn techniques, overcome challenges, and see how Blix AI simplifies the process.
