Coding Open-Ended Questions: A Guide for Market Researchers
by
Bill Widmer
January 12, 2025
Market research thrives on deep insights, and open-ended questions are one of the best ways to gather detailed, qualitative data that’s truly useful.
But coding and analyzing hundreds or even thousands of open-ended responses is no small task. Manually coding open-ended questions can be time-consuming and labor-intensive, often leading researchers to avoid including them in surveys entirely.
This is a shame.
Not including open-ended questions in customer surveys will limit the depth and richness of the insights, missing out on valuable opportunities to understand customer opinions, preferences, and motivations.
So how do you code open-ended questions without pulling your hair out or hiring someone?
In this guide, we break down the seven steps to coding open-ended questions — and a trick the pros use to make manual coding easier, faster, and more accurate. Let’s dive in!
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Understanding Open-Ended Responses (And The Importance of Proper Coding)
Open-ended responses are a type of survey response that allows respondents to answer in their own words, providing a deeper understanding of their thoughts, feelings, and opinions.
For example, let’s say you have the survey question, “What do you think about our new product line?”
The open-ended response might be something like this:
“I really like the new design and the variety of options you offer, especially the eco-friendly packaging. However, I think the prices are a bit too high compared to similar products on the market. It would be great if you could offer more affordable alternatives without sacrificing quality.”
This is a detailed, qualitative response that covers several themes:
Design
Product variety
Eco-friendliness
Price
Quality
In order to analyze qualitative feedback at scale researchers often need to turn those open ended responses into quantitative data so they can slice and dice it in a spreadsheet, put it in a statistical software (like SPSS or R), or turn it into a dashboard in a BI system,
To do that, open-ended responses need to be coded.
Let us walk you through it…
7 Steps to Coding Open-Ended Questions
Coding open-ended survey questions comes down to seven steps:
Familiarize Yourself with the Qualitative Data
Develop Initial Codes
Assign Codes to Responses
Refine Codes
Validate Coding
Interpret the Data
Report Findings
To help you understand how to code open-ended survey responses, we’ll go through each step with an example.
Let’s say a credit company wants to improve its Pixar themed credit cards. So, they ask the open-ended question, “Why do you recommend the Pixar credit card to others?” and they receive a few hundred open ended responses.
How would you code this data?
Let’s start with…
Step 1: Familiarize Yourself with the Qualitative Data
Start by thoroughly reading all the responses from your survey respondents to your open-ended questions. This first reading helps you connect with the data, understand the general sentiment, and identify key areas of focus.
It’s also important to consider the research question and survey objectives, as well as the target audience and the context in which the survey was conducted.
After reading through everything, take a coffee break. It helps to step away in between initial review and developing your codes.
Step 2: Develop Initial Codes
Re-visit the responses and identify recurring phrases, words, and concepts through survey coding. Create a list of these key elements, forming the basis of your codes.
Codes are usually short phrases or single words that capture the essence of a theme but can be more complex if necessary. This process is called verbatim coding.
When developing your codes, you can take one of two approaches:
Top-down (deductive): Codes are predetermined based on your research framework.
Bottom-up (inductive): Codes emerge organically from the data.
Top-down (deductive) coding is ideal for well-defined research questions where the data is expected to fit into pre-existing categories. It involves using a pre-existing set of codes to categorize and analyze the data.
Bottom-up (inductive) coding, on the other hand, involves developing codes from the data itself, making it better suited for exploratory research where you expect to find new insights and patterns.
For more details on these methods, explore these resources:
Remember to consult with your end customer to ensure that your coding framework aligns with their research goals.
There’s no right or wrong answer here; the 'correct' code frame depends on the research question and objectives, the target audience, and the context in which the survey was conducted.
Here are some code examples for the Pixar survey:
Take your time. A well-constructed code frame will help you systematically categorize and analyze your survey data, making it much easier to draw meaningful conclusions.
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Now, go through each response from your open-ended survey questions one-by-one, classify relevant sections of text with the codes you’ve developed. Some responses may require multiple codes.
There are two ways to represent the data from coded responses — Binary or Categorical.
Binary coding is when you assign a code to a verbatim response using a “1” to indicate the code is attached to a response, or a “0” to indicate that it is not attached.
It looks like this:
In this example, the first row is coded with “Nostalgic and Fun” and “Card Design and Aesthetics”.
Categorical code is where you assign a code ID number to each code, then place the corresponding number next to each verbatim response to indicate which codes are assigned to it.
It looks like this:
In this example, code ID 12 is “Card Design And Aesthetics” and code ID 10 is “Nostalgic And Fun” and those code IDs are assigned to the first row’s response. This method is easier and more common when manually coding, but you do have to remember all the code IDs or constantly check it against the codebook.
If you want to make coding easier, give Blix a try. Our coding software assigns codes to the survey responses and extracts interesting verbatim to represent that code automatically using AI-powered text analysis. It makes survey coding exponentially easier.
Here’s how it works:
Step 4: Refine Codes
After coding a sample of responses, review your codes for clarity and relevance. Are any codes too broad or too specific? Are some overlapping? Did you cover all the data? Have any missing codes emerged? Adjust as needed—this is an iterative process.
You may need to combine or split codes for better accuracy.
For example, let’s go back to our question, “Why do you recommend the Pixar credit card to others?” You find these two codes:
"Nostalgic and Fun"
"Conversation Starter"
If most responses mention both nostalgia and starting conversations together, it could make sense to combine them into a broader code like "Memorable and Engaging." This captures the card's appeal as a fun and notable item to share with others.
On the other hand, suppose you have a code labeled "Good Customer Service" covering a wide range of comments, such as:
"The representatives are very polite."
"I always get quick responses."
"They helped me resolve an issue quickly."
These comments cover unique aspects of customer service. Splitting this into more specific codes—such as "Polite Representatives," "Fast Response Time," and "Effective Problem Resolution"—provides more accuracy, enabling each aspect of the service experience to stand on its own. This way, you can better address and highlight each component based on the detailed insights.
Step 5: Validate Coding (Optional)
For extra rigor, if time and resources allow, consider having another researcher independently code a portion of your data to check for consistency. Comparing coding decisions can help identify discrepancies and improve the reliability of your analysis.
Again, coding is often an iterative process. Multiple rounds of refinement may be necessary to ensure that your codes accurately capture the depth of the data.
Step 6: Interpret the Data
Once your data is fully coded and organized into themes, it’s time to interpret the results.
What story does the data tell? How do these findings relate to your initial research objectives?
Draw clear connections between the patterns you’ve identified and the purpose of your research.
For our credit card example, we found that the most prominent themes included “Perks And Benefits” (38%) and “Card Design And Aesthetics” (29%).
These themes suggest that customers value the design and discounts. These insights align with our initial objective: understanding whether the card’s usability and branding would attract customer loyalty.
Interestingly, the theme “Conversation Starter” (7%) highlighted the card’s social appeal, suggesting it sparks interactions and positive associations with Pixar, which may not have been an initial focus but adds depth to the brand’s value.
Overall, these themes tell a story of a credit card that appeals to both practicality and sentiment, enhancing brand loyalty through ease of use and memorable design. This insight provides direction for future marketing strategies, emphasizing user-friendly features and the brand’s nostalgic charm.
Step 7: Report Findings
Describe each theme and include examples from the data that support your analysis. Discuss the implications of these findings for your research, whether they affirm or challenge your original hypotheses.
For example, here is the report we generated using the Pixar data:
The graph shows you the frequency of each code so you can see what factors are key reasons for to customers when recommending the card (the design and discounts) and what's less important (customer service).
Below that, it gives you a summary of each code and examples of responses connected to those codes so you can see first-hand what survey respondents actually said.
Code Open-Ended Questions Faster & Easier Using Automated Coding AI Software
Manual coding is a valuable skill, but it can also be time-consuming and subject to human bias.
AI-powered tools like Blix speed up the process while increasing accuracy, especially when dealing with large datasets.
Machine learning algorithms can automate coding tasks for surveys, providing a reliable method for handling large datasets while also incorporating both qualitative and quantitative data for comprehensive insights.
Here’s how AI tools can transform your workflow:
Speed: Process thousands of responses in a fraction of the time it takes manually.
Quantity: Handle large volumes of data without becoming overwhelmed.
Objectivity: Reduce the risk of bias by letting an AI tool handle coding and interpretation.
Multiple Languages: If you’re dealing with multilingual surveys, AI tools like Blix can seamlessly manage translation and coding across languages.
By using Blix, you’ll not only save time but also gain deeper, more reliable insights from your open-ended survey responses.
