A Study on the Price Derivation of Creative New Product Based on Big Data and Artificial Intelligence

This study presents a methodology to solve the pricing problems faced by founders developing new products with innovative ideas and technologies. Many founders research and ž innovative products that do not have similar products on the market, but despite the fact that pricing is a key factor in the success of start-ups, it is difficult to systematically set prices, such as relying on fragmentary market surveys in pricing or setting low or high prices through personal intuition because there are no similar products. In this study, the Bank of Korea's industrial statistics, similar product price data on online platforms such as Dana, and financial goals of start-ups were analyzed in an integrated manner. In particular, the author supported a total of 10 years of start-ups and commercialization, including 3.5 years at the Gyeongbuk Creative Economy Innovation Center, Samsung Electronics' C-Lab, and mentoring major university start-up incubation, focusing on real-world examples of innovative product price calculation, including industry average price (IPA), industry average return (IPM), market-like product price (MPP), target return (TPR), and innovation index (II) were derived and multiple regression analysis was conducted on these variables. As a result of the analysis, it was confirmed that these six major variables had a significant influence on pricing. It was found that the average industrial price, average industrial rate of return, market-like product price, target rate of return, and innovation index had a significant positive (+) effect on product prices, and the sales reaching the break-even point had a negative (-) effect. In addition, the derived regression equation P = -13.672 + 0.418(IPA) + 0.714(IPM) + 0.542(MPP) + 0.246(TPR) - 0.183(BEP) + 0.189(II)* showed high explanatory power of R²=0.873, and it was analyzed that this model-based pricing was 17.8% higher in sales and 22.3% higher in average than the subjective judgment of the founder. Therefore, the model is expected to provide founders with objective pricing tools, reduce uncertainty, and support market-appropriate pricing. Limitations of the study include limited samples for domestic start-up companies, the need to strengthen cross-sectional research design, and overcoming the subjectivity of the innovation index, and further studies such as country-specific comparative studies (export price studies), industry-specific models development, deep learning-based prediction models, and practicalization plans are needed as follow-up studies.