From Qual To Quant: How Coding Can Transform Open-Ended Responses In Quantitative Marketing Research

When it comes to quantitative research in marketing, numbers can often take the centre stage, with percentages, charts, and statistical data models helping to drive decisions. However, while this insight can provide clarity and be an invaluable source of insight, it often takes precedence over things like open- ended responses, which can be just as useful.

These open-ended, qualitative answers are able to provide rich insight that explains why a customer thinks or acts a certain way. While traditionally, qualitative and quantitative marketing research were separate, with the right approach, you can transform this goldmine of qualitative insight into measurable and comparable data.

That method is called coding, and in this latest article, we take a closer look at how it can transform your research projects…

Qualitative-to-quantitative transformation

Qualitative-to-quantitative transformation is the process of converting open- ended, descriptive data into structured numerical information that can be analyzed statistically. In the context of quantitative research in marketing, this means taking customer comments, feedback, or interview transcripts and organizing them into measurable categories.

Why open-ended responses are a gold mine for quantitative market research

Why open-ended responses are a gold mine for quantitative market research The use of open-ended questions in your research projects can be an incredible source of insight. By giving respondents the chance to express themselves in their own words, it allows them to reveal the emotional drivers behind their decisions, any nuanced pain points, and potentially unexpected opportunities.

This style of question can help to:

— Uncover hidden motivations behind answers to your closed-ended answers.

— Highlight the emerging trends before they start to appear in the numbers.

— Provide context for any anomalies in your quantitative market research data.

Yet despite the huge advantages they provide, many marketing teams and researchers skip the detailed analysis of these responses because they are harder to quantify. Without coding, the insights remain anecdotal rather than statistically usable.

What is coding in research?

In quantitative research marketing, coding is the process of categorizing and labeling qualitative data, such as survey comments, into themes that can then be counted and analyzed statistically. For example, if you received the raw response of, “The checkout process was too slow, and I couldn’t find the right product filter,” then the coded themes would be ‘checkout process’ and ‘website navigation’.

Once you have been able to code all of your qualitative responses, you will then be able to quantify them into valuable responses. For example, 28% of respondents reported checkout issues, while 34% reported issues navigating the website.

What is the difference between manual and automated coding?

Manual coding involves human coders having to go through each response and assigning it manually to one or more coding categories. On the other hand, automated coding uses Artificial Intelligence (AI), Natural Language Processing (NLP), and keyword-based algorithms to do the same tasks but in a fraction of the time.

That means rather than having to spend hours going through individual responses, automated coding is able to scan the transcripts, detect sentiment, and apply category tags instantly. That is not to say manual coding is not still worthwhile for ensuring nuanced interpretation, but for large-scale projects automation is helping to speed up the process.

Why transform qualitative into quantitative market research?

Transforming qualitative into quantitative data is especially useful when undertaking large customer satisfaction surveys or when looking for product feedback during beta testing. It is also useful for any post-campaign brand perception studies or for analyzing social media comments.

Transforming this data is able to provide a number of benefits, with the biggest including:

— Scalability

You will be able to quickly scale your approach as needed, processing thousands of open-ended answers in a few minutes compared to the hours it would have taken previously.

— Comparability

Another major benefit is the level of comparability you are able to enjoy. You will be able to directly compare qualitative themes with numerical survey metrics.

— Statistical analysis

Alongside being able to compare, you will also be able to analyze the data more thoroughly and apply standard quantitative market research techniques such as correlation, regression, and segmentation.

A step-by-step guide to coding open-ended responses

If you are wondering how to code open-ended responses, here is our step-by- step guide to help you…

Step 1: Data preparation

Before coding begins, you should first clean the data to remove any irrelevant text such as N/A or duplicates and spam. You will then need to standardize the formatting to ensure consistent casing, spelling, and punctuation.

Step 2: Develop a coding frame

Next, you will need to develop your coding frame. This is your master list of categories and themes, and you should start with a small set of high-level categories before expanding into deeper subcategories as patterns emerge.

Step 3: Apply your codes

Once you have your framing, you can start to apply your codes. This can be done manually, which is great for smaller datasets or highly nuanced topics, or automated via AI, which is able to auto-detect themes.

Step 4: Ensure consistency

To ensure consistency amongst multiple coders, then you should regularly check the inter-coder reliability (the percentage of agreement between coders) to maintain accuracy.

Step 5: Quantify the codes

Finally, you can then turn those codes into numbers that you can analyze. To do this, you should first count the number of responses that mention each theme and then calculate the percentage of the total respondents. You can then represent that data in charts, tables, or various statistical models as part of your wider quantitative market research method.

The pros and cons of manual and automated coding

Both manual and automated coding bring with them a variety of pros and cons. For manual coding, the biggest benefits are the ability to interpret nuanced data, providing a far higher level of accuracy for complex and ambiguous responses. However, it is incredibly time-consuming and requires a lot of manpower, which can become expensive for large datasets.

When looking at automated coding, the biggest benefits it provides is the ability to process thousands of responses in a mere matter of minutes. This significantly reduces the timescale of your research projects, while the data insight is also highly consistent and very scalable. However, AI and NLP can sometimes miss the subtle context within responses and require strong training data and pre-set code frames to accurately label responses.

For the best results, you should combine the two. Use automation for initial categorization and coding, but then have human reviewers check and refine the high-priority datasets.

Challenges and best practices

Naturally, like all aspects of quantitative research in marketing, this approach is not without its own challenges. That is why we have put together some key best practices to help ensure your data is as strong as possible.

1. Avoid bias in code frame creation

our categories should emerge from the data, not just from your assumptions. If you start with preconceived notions, you risk overlooking emerging trends, so ensure that your frames are free from bias.

2. Coding multi-theme responses correctly

Many of the comments you receive will likely contain multiple ideas. Not tagging these correctly, or trying to force them into a single category, can lead to poor datasets that don’t accurately reflect your customers.

3. Maintaining transparency

Ensuring transparency is essential, so keep all documentation relating to your coding frame and rules and be sure to document any changes you made during the process. This will help you maintain reliability throughout the project.

Conclusion

Coding is able to transform your unstructured text responses into comprehensive and analyzable data that bridges the gap between qualitative depth and the precision of quantitative marketing research. It allows you to unlock richer insights, validate patterns, and connect emotional drivers with statistical evidence. By combining human expertise with AI-powered automation, you can analyze more data, faster, without sacrificing quality.

Here at Yasna, we’re proud to offer the easiest way to conduct in-depth interviews and our free tool for coding open-ended survey responses will transform your data. With this tool, you will be able to instantly analyze open- ended survey responses and create valuable insights quickly. Want to find out more? Start y our free trial today!