Application of Natural language processing to enhance qualitative research used for marketing

Understanding consumer behavior is essential for effective marketing strategies. Market research often employs conventional qualitative analysis to uncover the motivations driving consumer purchases. However, a small sample size limits the ability to draw population-level conclusions, while analyzing large-scale data can be time-consuming. Moreover, qualitative research is prone to human error, subjectivity, and bias, leading to potentially misleading results. To address these issues, Natural Language Processing (NLP) is utilized to extract consumer behavior from large-scale product review datasets. The research focuses on product reviews for luxury cars, smart TVs, bread, and soaps to examine complex, dissonance-reducing, habitual, and variety-seeking purchasing behavior, respectively. The results, categorized using the Perceived Value Scale and analyzed based on cluster size, rank, and sentiment, indicate that consumer purchase intentions are influenced by perceived emotional, quality, and social value, respectively in complex purchasing behavior. Dissonance-reducing behavior is primarily driven by perceived quality value, while habitual purchasing behavior is influenced by perceived economic, emotional, and quality value, respectively. Lastly, variety-seeking behavior is affected by perceived emotional and quality value, respectively. Therefore, NLP demonstrates an efficient, cost-effective, and unbiased approach to extracting consumer behaviors from large-scale product reviews.