New Feature: AI Coding Boosts Efficiency in Open-Ended Question Analysis

Open-ended questions, as unstructured text, allow respondents to freely express any ideas, making them widely used in surveys. However, for decades, the bulk coding and analysis of open-ended answers into structured data have been a headache for market researchers.

InsightWorks introduces AI Coding, liberating researchers from hundreds or thousands of diverse open-ended responses, significantly saving researchers' time and effort, and enhancing the efficiency of open-ended question analysis.




What is AI Coding?

Traditional analysis of open-ended questions typically involves simple keyword analysis such as tokenization and word frequency counting, without truly understanding the semantic meaning of the data. Thanks to the support of large-scale language models, AI Coding possesses intelligent semantic understanding capabilities, enabling deeper exploration of the meaning behind the data. It can intelligently identify key ideas, themes, and patterns in the dataset of open-ended questions. This solution automates the coding process while ensuring that the coding is context-specific, comprehensive, mutually exclusive, and aligned with research objectives.


Before AI coding, researchers can use AI-generated codebooks or reference/import existing codebooks. After AI coding, researchers can also manually check and adjust the coding results.


What are the advantages?

Enhanced analysis efficiency: AI Coding significantly reduces the processing time of open-ended data, allowing researchers to focus more on data insights.

Reduced human error: Manual labeling of open-ended data is prone to subjectivity and errors, which may affect the final analysis results. AI Coding, based on advanced natural language processing technology, can understand the semantic meaning of the data, thereby reducing the likelihood of labeling errors and ensuring more accurate and reliable analysis results.

Customized models: After fine-tuning training on InsightWorks' own market research industry dataset, our AI Coding feature performs more accurately. Compared to directly using a generic LLM model, this customized training enables AI Coding to more precisely recognize industry-specific terms, contexts, and underlying meanings, even understand industry backgrounds, thereby more accurately interpreting and coding open-ended responses, avoiding the generalization issues that general AI models may have, and possessing higher accuracy and reliability in market research data processing.


In addition to open-ended questions in surveys, there are many other scenarios in market research that require semantic understanding and coding of open-text data. Therefore, in the future, we will apply AI Coding to other existing products or research scenarios, such as real-time interviews, social media listening, customer feedback, etc., providing you with more intelligent and efficient data support for your decisions.

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