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In today’s era, many companies are actively building their own digital platforms and mining valuable insights from big data. They also expect that market research can also use data to obtain more representative results. Therefore, with the rapid development of AI, many market researchers have begun to use this technology to effectively conduct data cleaning, in-depth predictive analysis, and data mining (Quantilope, 2023). Although from the above application scope, some people may mistakenly think that AI is limited to the field of quantitative research, in fact, AI can also exert excellent performance for qualitative research.

Qualitative research collects and analyzes non-numeric data to understand people’s experiences, attitudes, behaviors, and interactions (Pathak, Jena, Kalra, 2013). Therefore, researchers can use facial coding technology to deeply analyze the micro-expressions and emotional responses of respondents, providing deeper insights into their emotional states and consumer behavior (Srinivasan, 2023). This is an advanced AI algorithm that can carefully capture the subtle changes in the face and the intonation differences in the voice (Srinivasan, 2023).

In addition, qualitative research is often criticized for being too subjective, and sentiment analysis technology may be able to alleviate this concern. This technology can systematically help researchers delve into the hidden emotional aspects of written or oral feedback, allowing them to more thoroughly understand the emotional response that activities, products, or services bring to consumers (Srinivasan, 2023). Sentiment analysis tools based on AI can provide objective and in-depth insights and reduce personal bias that may be caused by manual review (Amazon Web Services, 2023). It can not only handle large amounts of data analysis but also provide results in real-time, allowing us to grasp the context of information instantly (Amazon Web Services, 2023).

In addition to the previously mentioned technologies, the applications of AI in qualitative market research are quite diverse. It can assist and enhance our ability to analyze subjective data. Therefore, I believe that AI will become an important tool in qualitative market research.

Reference

Quantilope. (2023, December 9). How AI Continues To Disrupt Market Research, for the Better. https://www.quantilope.com/resources/how-ai-is-disrupting-market-research.

Pathak, V., Jena, B., Kalra, S. (2013). Qualitative research. Perspectives in Clinical Research, 4(3), 192-194. https://doi.org/10.4103/2229-3485.115389.

Srinivasan, S. (2023, May 22). AI Has Taken Over Qualitative Market Research. Here’s What That Means for Your Business. https://www.entrepreneur.com/science-technology/how-ai-is-transforming-market-research/450593.

Amazon Web Services. (2023). What is Sentiment Analysis? https://aws.amazon.com/what-is/sentiment-analysis/?nc1=h_ls.

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When ChatGPT was launched at the end of 2022, I was working as a market researcher, and the AI chatbox instantly caused a sensation. As more and more applications began to be developed, my colleagues and I started thinking about the impact of this innovation on the market research industry. Indeed, it posed many challenges, which we will not discuss in this article. From my experience, ChatGPT actually presented a lot of opportunities for market research. It has multiple potential applications and advantages that could revolutionize traditional methods.

• Improve the efficiency of Secondary Research

The advantage of ChatGPT lies in its powerful information processing capabilities. Although we must pay attention to the authenticity of the data, it can collect a large amount of data and quickly organize and summarize the key points, thereby providing researchers with the most important and valuable information (Shah, 2023). Now that ChatGPT has such powerful sorting and summarizing capabilities, if it can be combined with other tools for collecting primary information, such as website cookie tracking or some AI customer service, I believe this will change the way consumer research is done.

• Process Automation

Although each project has different requirements and research objectives, the overall process is fixed, so the research process can be split into SOPs and then completed with ChatGPT. Taking the qualitative research of focus group discussion as an example, it will need a screener to filter the interviewees and a discussion guide for the interview. Compared with designing them all by myself, when I inform ChatGPT of the entire research design with appropriate prompts, it will design a nice document for me. Using it as a draft will save a lot of time. Moreover, sometimes the provided answers will contain some points that I did not think of before, which really inspires me.

The above two applications are only described based on my own experience. I believe that AI has broader potential in market research, and I look forward to its application in more places in the future to optimize the industry.

Reference

Sagar Shah. (2023, January 29). Using ChatGPT for Secondary Research: Pros and Cons. https://www.linkedin.com/pulse/using-chatgpt-secondary-research-pros-cons-sagar-shah