• Industry Promotion Research
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• v.9 no.4
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• pp.17-28
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• 2024

Recent advancements in AI have significantly transformed corporate marketing strategies, benefiting SMEs as well. While government support services play a key role in promoting firm & startup growth, research on optimizing their effectiveness remains limited. This study examines the sequential mediating roles of absorptive capacity & government support services in enhancing AI marketing outcomes using Hayes' (2018) Process Macro Model 6. Results show that while AI marketing does not directly impact competitive performance or government support services, it positively influences absorptive capacity, which in turn positively affects government support services & competitive performance. The findings highlight the importance of firms enhancing their absorptive capacity & efficiently using government support, while governments should design more effective support programs & policies for firms to fully leverage these resources.

• Journal of Digital Convergence
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• v.19 no.7
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• pp.95-111
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• 2021

Despite the innovation of AI and Big Data, theoretical research bout the effect of AI and Big Data on market competition is still in early stages; therefore, this paper analyzes the effect of AI, Big Data, and data sharing on an entry firm by using game theory. In detail, the firms' business environments are divided into internal and external ones. Then, AI algorithms are divided into algorithms for (1) customer marketing, (2) cost reduction without automation, and (3) cost reduction with automation. Big Data is also divided into external and internal data. this study shows that the sharing of external data does not affect the incumbent firm's algorithms for consumer marketing while lessening the entry firm's entry barrier. Improving the incumbent firm's algorithms for cost reduction (with and without automation) and external data can be an entry barrier for the entry firm. These findings can be helpful (1) to analyze the effect of AI, Big Data, and data sharing on market structure, market competition, and firm behaviors and (2) to design policy for AI and Big Data.

• Journal of the Korea Society of Computer and Information
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• v.24 no.12
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• pp.183-189
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• 2019

After the fourth industrial revolution, a business that utilizes Artificial Intelligence (AI) is expanding centered around IT industries and it is expected that the quality of AI services will improve. This study aims to examine changes in marketing through the advance and development of AI and to establish and apply marketing strategies to respond to future market changes. Based on existing data, the development of AI technology was examined and looked into changes in marketing and counter strategies through cases overseas and South Korea. Artificial Intelligence technology has a close impact on our lives, changing our lives, and thus changing consumer patterns, perceptions, and consumer culture. In the future, innovative changes in AI technologies will require government policies, the vision of the corporation, and it is necessary to establish longer-term success strategies. Collaboration between companies and industries is also important.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.03a
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• pp.203-214
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• 1999

본 연구는 A카드 회사에서 현재 실시하고 텔레마케팅 시스템에 데이터마이닝 기법 가운데 하나인 CHAID, CART 알고리즘 및 신경망 기법을 적용하여 모델을 개발하고 개발괸 모델들의 성과를 분석한다. 이를 통하여 어떻게 기업이 데이터베이스와 데이터마이닝 기법을 마케팅에 효과적으로 사용할 수 있는가에 대한 방안을 제시하고 여러 모델들의 성과를 비교 분석하는 방안을 함께 제시한다.

• Journal of Digital Convergence
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• v.18 no.10
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• pp.175-180
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• 2020

Through case studies for insurance service marketing using artificial intelligence(AI) in the insurtech industry, it investigated how innovative technologies(artificial intelligence, machine learning etc.) are being used in the insurance ecosystems. In particular, through domestic and international case studies, it was examined by Lemonade's service of insurance contracts and getting the indemnity and AI company's service of calculating the compensation through a medical certificate image based on OCR, which brought disruptive innovations using artificial intelligence. As a result of the case analysis, these services have drastically shortened the lead time of insurance contracts and payment through machine learning using numerous customer data based on artificial intelligence. And accurate and reasonable compensation was calculated in the estimation of indemnity, which has a lot of disputes between customers and insurance companies. It was able to increase customer satisfaction and customer value.

• Journal of Platform Technology
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• v.10 no.1
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• pp.20-28
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• 2022

Artificial intelligence-based health data verification has become an essential element not only to help clinical research, but also to develop new treatments. Since the US Food and Drug Administration (FDA) approved the marketing of medical devices that detect mild abnormal diabetic retinopathy in adult diabetic patients using artificial intelligence in the field of medical diagnosis, tests using artificial intelligence have been increasing. In this study, an artificial intelligence model based on image classification was created using a Teachable Machine supported by Google, and a predictive model was completed through learning. This not only facilitates the early detection of cataracts among eye diseases occurring among patients with chronic diseases, but also serves as basic research for developing a digital personal health healthcare app for eye disease prevention as a healthcare program for eye health.

• Annual Conference of KIPS
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• 2023.11a
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• pp.949-950
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• 2023

본 연구는 AI 기술을 활용하여 CCTV(Closed-Circuit Television)영상 데이터를 분석하고, 실시간으로 고객을 측정하고 분석하는 방법에 대한 연구이다. 이러한 AI-Dirven Audience Measurement는 마케팅, 이벤트 기획 등에서 응용 가능성을 지니고 있다. 매장에 설치된 CCTV를 통해 데이터를 수집하고 수집된 데이터를 통해 입장한 고객의 성별과 나이를 예측한다. 이에 본 연구를 통해 기업의 마케팅 전략의 최적화 및 이벤트 기획 등 활용할 수 있고 고객의 행동 및 성향 분석을 통해 시설의 구조 및 레이아웃 개선 등을 위한 설계 개선에도 기여할 것으로 기대된다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.232-234
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• 2022

Big data is widely used in customer relationship management, relationship marketing, financial business improvement, credit information and risk management. Moreover, as non-face-to-face financial transactions have become more active recently due to the COVID-19 virus, the use of financial big data is more demanded in terms of relationships with customers. In terms of customer relationship, financial big data has arrived at a time that requires an emotional rather than a technical approach. In relational marketing, it was necessary to emphasize the emotional aspect rather than the cognitive, rational, and rational aspects. Existing traditional financial data was collected and utilized through text-type customer transaction data, corporate financial information, and questionnaires. In this study, the customer's emotional image data, that is, atypical data based on the customer's cultural and leisure activities, is acquired through SNS and the customer's activity image is analyzed with an artificial intelligence CNN algorithm. Activity analysis is again applied to the annotated AI, and the AI big data model is designed to analyze the behavior model shown in the annotation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.340-348
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• 2004

온라인상점의 상품추천시스템은 일대일마케팅의 대표적 실현수단으로써의 가치를 인정받고 있다. 대부분의 상품추천시스템은 시시각각 변화하는 소비자의 기호에 따라 상품을 어떻게 추천할 것인가에 대한 문제에 직면해 있다. 본 연구에서는 급변하는 온라인상점 환경에 탄력적으로 대응하기 위하여 데이터마이닝과 다중모형조합기법을 이용한 상품추천시스템 모형을 제안하고자 한다. 제안하는 상품추천시스템은 현재 운영중인 온라인상점 데이터로 프로토타입을 구축하고 실제 소비자에 대한 적용가능성을 검증하였으며, 그 결과 실제 유용할 것으로 확인되었다.

• Journal of the Korea Computer Industry Society
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• v.2 no.9
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• pp.1175-1182
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• 2001

One to one Marketing (a.k.a. database marketing or relationship marketing) is one of the many fields that will benefit from the electronic revolution and shifts in consumer sales and advertising. As a component of intelligent customer services on Internet storefront, this paper describes technology of providing personalized advertisement using the market basket analysis, a well-Known data mining technique. The underlining theories of recommendation techniques are statistics, data mining, artificial intelligence, and/or rule-based matching. In the rule-based approach for personalized recommendation, marketing rules for personalization are usually collected from marketing experts and are used to inference with customer's data. However, it is difficult to extract marketing rules from marketing experts, and also difficult to validate and to maintain the constructed Knowledge base. In this paper, using marketing basket analysis technique, marketing rules for cross sales are extracted, and are used to provide personalized advertisement selection when a customer visits in an Internet store.