In surveys, alongside closed-ended response options, there is often the “Other, please specify” field, designed to give participants the opportunity to add a personal choice. These open-ended responses offer valuable insights but can quickly become a messy collection of heterogeneous data that’s difficult to manage.
The critical issue is that, without proper coding, these contributions cannot be included in quantitative analysis: they remain isolated notes with no statistical weight.
Ignoring them means assigning partial meaning to the closed responses alone. The result is an incomplete analysis that considers only part of the data and risks producing a picture that does not truly represent the entire sample.
In this article, we’ll explore how IdSurvey’s AI, integrated with ChatGPT, offers a solution that brings order to the chaos. We’ll see how AI automates the categorization of “Other, please specify” responses, detects useful patterns, and transforms even marginal contributions into structured quantitative data—delivering practical benefits in speed, accuracy, and insight for researchers.
The challenges of “Other, please specify”: variety, time, and inconsistency
Open-ended responses from the “Other, please specify” field are, by nature, free-form: each participant can write whatever they want, resulting in a wide variety of answers. Let’s take a simple example.
In a survey question with these response options:
– Size/fit
– Defective product
– Does not meet expectations
– Other, please specify
Some respondents might type “color different from photo” or “fabric too thin,” which can be categorized under “Does not meet expectations.” Others might mention unexpected issues like “arrived late, don’t need it anymore” or “damaged box, unusable product.”
In these cases, AI can automatically assign the first set of answers to an existing category and propose new categories—like “Delivery issues” or “Damaged packaging”—for the uncategorized ones.
This diversity makes coding complex: it’s necessary to decide whether and how each response should fit into an existing category or if new ones need to be created.
Manually coding hundreds of these responses is a notoriously time-consuming and labor-intensive process. As a result, many researchers limit the use of open-ended questions to avoid the analytical burden.
It involves carefully reading each response, interpreting it, and assigning an appropriate code or category. This requires time and attention. With large data volumes, the risk of inconsistency increases: different analysts may apply slightly different criteria, or even the same person may not maintain consistent interpretation throughout. Manual coding is subjective and may introduce bias or inconsistency into the data.
IdSurvey’s AI: from the chaos of open-ended answers to consistent categories
Faced with these challenges, the AI integrated into IdSurvey marks a real step forward. Thanks to the integration of OpenAI’s language model, IdSurvey can automatically analyze and categorize open-ended responses, bringing structure to the disorder.
In practice, what once took hours of manual work can now be done by AI in just moments—with a level of consistency that’s hard to achieve manually.
The AI reads each “Other, please specify” response and understands its meaning through natural language processing. As a result, it systematically assigns each response to the appropriate category.
If the answer matches an existing option, AI will map it accordingly. If new topics or issues emerge that the original options didn’t cover, AI detects recurring patterns and suggests creating new categories.
This smart approach enables accurate and consistent coding at scale. Every open-ended response is treated with the same criteria, eliminating discrepancies caused by human interpretation.
The result is a solid categorization where similar information is grouped uniformly. What’s more, IdSurvey’s AI operates at unmatched speed: a traditionally long and complex process becomes fast and efficient.
This not only drastically reduces the time and resources needed for coding—it also frees research teams from repetitive tasks, allowing them to focus on high-level analysis.
Importantly, AI does not replace the researcher—it supports them. It generates coding suggestions and new categories, giving the researcher full control to accept or refine them based on their expertise and analytical goals.
This ensures the speed and consistency of automation is combined with the researcher’s critical judgment and oversight.
Practical benefits: speed, accuracy, and richer insights
Automating the coding of “Other, please specify” responses with AI brings several concrete benefits for researchers:
- Turning qualitative into quantitative
AI allows you to transform open-text answers into structured data ready for quantitative analysis. Once “Other, please specify” responses are coded, even those that were unique or marginal can be integrated into the dataset as part of analyzable categories.
This means participants’ verbatim comments become numbers, percentages, and charts.
The researcher can see how many people mentioned a certain theme in open-ended feedback—just like with closed options.
In short, AI creates an automatic coding scheme and returns the open-ended responses already categorized “as if they were quantitative data,” ready to be used in reports and statistical summaries.
This process breaks down the barrier between qualitative and quantitative data, making it easy to include the richness of open text in structured analysis. - Speed and efficiency
The time savings are remarkable. AI can process thousands of responses in a fraction of the time required by a human team.
This means that survey results can be delivered much faster, allowing teams to respond and make decisions quickly.
Tasks that used to take days of manual coding can now be completed in minutes, with significant savings in staff time, outsourcing costs, and resources. - Accuracy and consistency
AI ensures consistent and accurate coding across the entire dataset.
Unlike manual work—which can suffer from errors or inconsistent interpretations—AI applies the same logic to every response.
This approach eliminates most human errors and subjective variations: for example, all versions of the same idea will be identified and coded the same way, ensuring uniformity.
The result is a cleaner dataset where categories are applied consistently to all relevant responses. - Pattern detection and new categories
A key advantage of AI is its ability to detect meaningful patterns in open-ended text.
If many respondents mention a recurring theme in the “Other” field that wasn’t originally listed, the AI will flag it.
This enables researchers to create new categories where needed, avoiding the risk of missing emerging trends.
For example, if multiple people write “price too high” in a product satisfaction survey, AI may group them together and suggest “Price” as a new feedback category—even if it wasn’t an option before.
This way, hidden insights become visible, enriching the overall analysis.
Conclusion: greater efficiency, better data, deeper insights
Adopting AI to code “Other, please specify” responses in surveys represents a strategic leap in research quality.
In the past, managing open-ended answers meant a major investment of time and effort—with the risk of incomplete or inconsistent data.
Today, thanks to IdSurvey’s AI, that burden is lifted.
Automatic coding transforms a complex and time-consuming task into a fast, reliable process.
Research teams can now focus on interpreting data and making decisions, rather than spending time on repetitive classification work.
In a world where the best decisions are based on complete and reliable data, AI-powered coding of open responses becomes an essential ally.
It means having more time for strategic thinking, more consistent data, and the confidence of not missing any key insights hidden between the lines.
From “Other, please specify” to actionable insight: AI truly turns chaos into categories, helping companies and researchers extract maximum value from every survey.
