Supporting Product Sales Strategy Through LLM-Based Consumer Review Analysis 


Vol. 14,  No. 3, pp. 194-202, Mar.  2025
https://doi.org/10.3745/TKIPS.2025.14.3.194


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  Abstract

The proliferation of online shopping has made product reviews a crucial factor in consumer purchasing decisions. Reviews provide essential information to consumers, such as product quality, price, and delivery, while also offering companies opportunities to refine their sales strategies and improve their products. However, the sheer volume of reviews poses challenges in efficiently analyzing and utilizing this data. To address this issue, this study proposes a model that automatically collects and systematically analyzes review data to enhance sales strategies. In this study, review data was effectively collected by combining web crawling with OCR technology. Particularly in web environments where crawling is restricted, OCR was used to convert review images into text, thereby expanding the scope of data collection. The collected review data was then analyzed using Large Language Model (LLM) like Chat GPT-4o, allowing for detailed classification of consumer opinions into various aspects such as quality, price, and delivery. The analysis results were utilized to identify key feedback elements and to design a user-friendly app UI that facilitates comparisons between a company's products and those of competitors. This UI includes features for data visualization, real-time search, and personalized analytical insights, enabling both consumers and companies to easily understand and utilize review data. This study introduces a novel approach to understanding consumer behavior by integrating IT technology with psychological analysis. It empirically demonstrates not only the tendency to justify purchasing decisions for high-priced products but also the possibility that a similar pattern can be observed in low-priced products, highlighting the importance of marketing strategies that reflect consumer psychological characteristics. The findings highlight the importance of developing marketing strategies that account for consumers’ psychological traits. Ultimately, this research showcases the potential for more effective, data-driven marketing strategies.

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[IEEE Style]

H. Park, D. Lee, Y. Seo, "Supporting Product Sales Strategy Through LLM-Based Consumer Review Analysis," The Transactions of the Korea Information Processing Society, vol. 14, no. 3, pp. 194-202, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.194.

[ACM Style]

He-sse Park, Dong-Gun Lee, and Yeong-Seok Seo. 2025. Supporting Product Sales Strategy Through LLM-Based Consumer Review Analysis. The Transactions of the Korea Information Processing Society, 14, 3, (2025), 194-202. DOI: https://doi.org/10.3745/TKIPS.2025.14.3.194.

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