Practical Applications of Artificial Intelligence in Marketing Research
DOI:
https://doi.org/10.15170/MM.2024.58.KSZ.01.03Keywords:
artificial intelligence, market research, response willingnessAbstract
THE AIMS OF THE PAPER
The rapid advancement and broad industrial application of artificial intelligence (AI) is creating new opportunities in the field of market research. This study aims to explore the practical applications of AI in market research, highlighting its potential advantages and disadvantages. We specifically examine the attitudes of the Hungarian population toward AI, focusing on their perceptions of AI-based virtual interviewers.
METHODOLOGY
The study begins with a review of the related literature, followed by an examination of the current state of AI applications in market research. In our primary research, we surveyed the attitudes of 1,000 Hungarian adults towards artificial intelligence and AI-based virtual interviewers through computer-assisted telephone interviews (CATI), using randomly generated mobile numbers. The sample is representative by gender, age, educational level, type of settlement, and region. The data was analyzed across different demographic groups to identify differences in attitudes and potential barriers to technology acceptance.
MOST IMPORTANT RESULTS
Our findings indicate that a significant portion of respondents are resistant to AI-based virtual interviewers, particularly among older age groups and individuals with lower educational attainment. Respondents can be classified into three groups: rejecters, neutrals, and willing respondents. The study revealed that the use of a familiar voice actor’s voice enhances response willingness among neutrals, though it has little effect on the rejecters. Results suggest that personalized features that align with individual preferences may be critical for the successful adoption of these technological advancements.
RECOMMENDATIONS
The use of AI-based virtual interviewers offers several advantages in market research, such as cost-efficiency and standardized processing of large data sets. However, successful adoption requires emphasizing respondent experience and personalization. We recommend that developers consider respondent preferences during development, including interviewer gender, voice style, and a human-like approach to interaction. Additionally, it is essential to address data privacy and ethical issues, particularly in light of AI’s increasing role, to maintain respondent trust.
Acknowledgments: Data collection was conducted by KÓD Market-Opinion and Media Research Institute.
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