The open-ended question data is often difficult to handle, as it requires a large amount of time and human resources for coding and analysis. To address this challenge, PowerCX  has developed a series of features using AI technology, aiming to improve analysis efficiency, reduce subjective bias, and deepen understanding of consumer opinions and behaviors, thereby providing faster, more accurate, and deeper insights for market research and decision-making.

AI Probing

AI Probing conducts targeted follow-up questions based on respondents' previous open-ended responses, aiming to uncover deeper insights into their thoughts.

 
When AI Probing is activated, there will be no apparent changes to the respondents' answering interface. There's no need to navigate to another page; instead, a tailored open-ended question will be added within the current survey.

What are the advantages of AI Probing?
Deeper Insights: AI Probing can analyze respondents' open-ended answers and propose more insightful, deeper-level questions. Through this approach, researchers can obtain more valuable information about respondents' viewpoints and opinions, aiding in a more comprehensive understanding of their thoughts and experiences.

Personalized Responses and Care: Through AI Probing, respondents can feel that their thoughts are genuinely being acknowledged and valued. This personalized response can enhance respondents' sense of involvement and willingness to share genuine, in-depth viewpoints, thereby improving the effectiveness and credibility of the survey.

Quality Control: AI Probing not only intelligently comprehends and analyzes answers to open-ended questions but also evaluates their quality. It can flag inadequate answers or respondents, identify potential issues promptly, adjust the direction of questioning, and thereby enhance the quality and accuracy of survey data.

How effective is AI Probing?
To validate the effectiveness of AI Probing, we recruited 300 respondents from our own panel for a product concept test. AI Probing was activated when asking about reasons for liking and disliking the concept.

Research results indicate that when asked about reasons for liking the concept, 69% of respondents provided additional answers after AI Probing, with an average increase of 1.2 answers per respondent. When asked about reasons for disliking the concept, the proportions of respondents providing additional answers after probing and the average increase in answers were 21% and 0.3, respectively.

AI QC

Assessing the quality of responses and flagging those low quality answers 

AI Coding

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 PowerCX's 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.

AI Summary

Using AI technology to analyze the results of open-ended and closed-ended questions, summarizing the results of the entire research project to enhance the efficiency of report writing. The AI summary is based on precise data such as word frequency obtained from coding to summarize and generate word cloud images. Compared to directly using AI language model output for summarization, our AI summary can quickly obtain viewpoint summaries supported by data, making the results more persuasive and avoiding empty generalizations. At the same time, the word cloud format is more intuitive than textual summaries.