• Annual Conference of KIPS
• /
• 2022.05a
• /
• pp.21-24
• /
• 2022

최근 통신업계에서는 축적된 빅데이터를 활용하여 고객의 특성을 이해하고 맞춤형 마케팅에 이용하려는 노력이 지속되어 왔다. 본 연구에서는 CatBoost 모델을 사용하여 이탈 가능성이 높은 고객을 예측하고 XAI(eXplainable Artificial Intelligence) 기법 중 하나인 SHAP을 적용하여 이탈에 영향을 미치는 요인을 설명하고자 하였다. SHAP의 global explanation 기법을 사용하여 특정 고객 segmentation 에 대한 이해력을 높이고, local explanation 기법을 사용하여 개별 고객에 대한 설명과 개인화 마케팅에 적용 가능성을 제시하였다. 본 연구는 기존의 이탈 예측모델인 블랙박스 모델이 갖는 한계점을 극복하고 고객의 특성을 이해하여 실제 비즈니스에 활용 가능성을 높였다는 점에서 의의를 가진다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.3
• /
• pp.1022-1034
• /
• 2023

Various aspects of artificial intelligence (AI) have become of significant interest to academia and industry in recent times. To satisfy these academic and industrial interests, it is necessary to comprehensively investigate trends in AI-related changes of diverse areas. In this study, we identified and predicted emerging convergences with the help of AI-associated research abstracts collected from the SCOPUS database. The bidirectional encoder representations obtained via the transformers-based topic discovery technique were subsequently deployed to identify emerging topics related to AI. The topics discovered concern edge computing, biomedical algorithms, predictive defect maintenance, medical applications, fake news detection with block chain, explainable AI and COVID-19 applications. Their convergences were further analyzed based on the shortest path between topics to predict emerging convergences. Our findings indicated emerging AI convergences towards healthcare, manufacturing, legal applications, and marketing. These findings are expected to have policy implications for facilitating the convergences in diverse industries. Potentially, this study could contribute to the exploitation and adoption of AI-enabled convergences from a practical perspective.

• Journal of Korea Artificial Intelligence Association
• /
• v.2 no.1
• /
• pp.15-24
• /
• 2024

This study is about the effect of chatbot service quality on customer satisfaction and continuous use intention. Data collection was conducted for 13 days from October 23 to November 5, 2023, and a survey was conducted on customers who have used chatbot services. A total of 572 questionnaires were targeted, of which 545 valid data were used for analysis, excluding those that responded insincerely or did not meet the purpose of the study. The analysis results of this study are as follows: First, chatbot service quality partially had a significant effect on satisfaction. Second, customer satisfaction had a significant effect on continuous use intention. Therefore, in order to have a positive impact on continuous use intention, it is necessary to focus on marketing strategies related to chatbot service quality. Also, research focusing on data analysis and performance evaluation is crucial for enhancing chatbot services, necessitating studies that address real-time changes. Through sophisticated data analysis and variable measurement, chatbot services can be effectively improved, leading to enhanced customer satisfaction.

• The Korean Journal of Applied Statistics
• /
• v.32 no.6
• /
• pp.795-835
• /
• 2019

Big data has been generated in various fields. Many companies have now tried to make profits by building a system capable of analyzing big data based on artificial intelligence (AI) techniques. Integrating AI technology has made analyzing and utilizing vast amounts of data increasingly valuable. In particular, demand forecasting with maximum accuracy is critical to government and business management in various fields such as finance, procurement, production and marketing. In this case, it is important to apply an appropriate model that considers the demand pattern for each field. It is possible to analyze complex patterns of real data that can also be enlarged by a traditional time series model or regression model. However, choosing the right model among the various models is difficult without prior knowledge. Many studies based on AI techniques such as machine learning and deep learning have been proven to overcome these problems. In addition, demand forecasting through the analysis of stereotyped data and unstructured data of images or texts has also shown high accuracy. This paper introduces important areas where demand forecasts are relatively active as well as introduces machine learning and deep learning techniques that consider the characteristics of each field.

• Journal of the Korea Convergence Society
• /
• v.12 no.11
• /
• pp.193-206
• /
• 2021

Artificial Intelligence (AI) has been inserted into people's lives through Intelligent Virtual Assistants (IVA), like Alexa. Moreover, intelligent systems have expanded to design studios. This research delves into designers' perspectives on developing AI-based practices and examines the challenges of adopting future intelligent design assistants. We surveyed UX/UI professionals in Brazil to understand how they use IVAs and AI design tools. We also explored a scenario featuring the use of Alexa Sensei, a hypothetical voice-controlled AI-based design assistant mixing Alexa and Adobe Sensei characteristics. The findings indicate respondents have had limited opportunities to work with AI, but they expect intelligent systems to improve the efficiency of the design process. Further, majority of the respondents predicted that they would be able to collaborate creatively with AI design systems. Although designers anticipated challenges in natural language interaction, those who already adopted IVAs were less resistant to the idea of working with Alexa Sensei as an AI design assistant.

• Convergence Security Journal
• /
• v.22 no.2
• /
• pp.115-120
• /
• 2022

This study analyzed the recent trends in the sports environment to which big data and AI technologies, which are representative technologies of the 4th Industrial Revolution, and approached them from the perspective of convergence of big data and AI technologies in the sports field. And the results are as follows. First, it is being used for player and game data analysis and team strategy establishment and operation. Second, by combining big data collected using GPS, wearable equipment, and IoT with artificial intelligence technology, scientific physical training for each player is possible through user individual motion analysis, which helps to improve performance and efficiently manage injuries. Third, with the introduction of an AI-based judgment system, it is being used for judge judgment. Fourth, it is leading the change in marketing and game broadcasting services. The technology of the 4th Industrial Revolution is bringing innovative changes to all industries, and the sports field is also in the process. The combination of big data and AI is expected to play an important role as a key technology in the rapidly changing future in a sports environment where scientific analysis and training determine victory or defeat.

• Journal of Digital Contents Society
• /
• v.19 no.1
• /
• pp.1-10
• /
• 2018

This study is to seek to find the importance of next digital marketing factors by using AHP method and analyze comparison between an advertising expert and a non-advertising expert. In results, the relative importance ranking is as follows; combination (0.26), transformation (0.259), optimization (0.243), and technology (0.238). The relative importance ranking of sub-factors is as follows: artificial intelligence and maching learning (0.086), big data (0.085), and contents curation (0.060). While the relative importance of combination and optimization for an advertising expert is higher than for non-advertising expert, the relative importance of transformation and technology for non-advertising is higher than for an advertising expert. This study provides managerial implication to build digital strategy based on these result.

• Information Systems Review
• /
• v.25 no.2
• /
• pp.145-162
• /
• 2023

Asset management is a complex and difficult field that requires insight into numerous variables and even human psychology. Thus, it has traditionally been the domain of professionals, and these services have been expensive to obtain. Changes are taking place in these markets, and the driving force is the digital revolution, so-called the fourth industrial revolution. Among them, the Robo-Advisor service using artificial intelligence technology is the highlight. The reason is that it is possible to popularize investment advisory services with convenient accessibility and low cost. This study aims to clarify what factors are critically important when selecting robo-advisors for service users and providers in Korea, and what perception differences exist in the selection factors between user and provider groups. The framework of the study was based on the marketing mix 4C model, and the design and analysis of the model used Delphi survey and AHP. Through the study design, 4 main criteria and 15 sub-criteria were derived, and the findings of the study are as follows. First, the importance of the four main criteria was in the order of customer needs > customer convenience > customer cost > customer communication for both groups. Second, looking at the 15 sub-criteria, it was found that investment purpose coverage, investment propensity coverage, fee level and accessibility factors were the most important. Third, when comparing between groups, the user group found that the fee level and accessibility factors were the most important, and the provider group recognized the investment purpose coverage and investment propensity coverage factors as important. This study derived useful implications in practice. First, when designing for the spread of the robo-advisor service, the basis for constructing a user-oriented system was prepared by considering the priority of importance according to the weight difference between the four main criteria and the 15 sub-criteria. In addition, the difference in priority of each sub-criteria shown in the group comparison and the cause of the sub-criteria with large weight differences were identified. In addition, it was suggested that it is very important to form a consensus to resolve the difference in perception of factors between those in charge of strategy and marketing and system development within the provider group. Academically, it is meaningful in that it is an early study that presented various perspectives and perspectives by deriving a number of robo-advisor selection factors. Through the findings of this study, it is expected that a successful user-oriented robo-advisor system can be built and spread in Korea to help users.

• Asia pacific journal of information systems
• /
• v.16 no.1
• /
• pp.71-84
• /
• 2006

Case-based reasoning (CBR) is a reasoning technique that reuses past cases to find a solution to the new problem. It often shows significant promise for improving effectiveness of complex and unstructured decision making. It has been applied to various problem-solving areas including manufacturing, finance and marketing for the reason. However, the design of appropriate case indexing and retrieval mechanisms to improve the performance of CBR is still a challenging issue. Most of the previous studies on CBR have focused on the similarity function or optimization of case features and their weights. According to some of the prior research, however, finding the optimal k parameter for the k-nearest neighbor (k-NN) is also crucial for improving the performance of the CBR system. In spite of the fact, there have been few attempts to optimize the number of neighbors, especially using artificial intelligence (AI) techniques. In this study, we introduce a genetic algorithm (GA) to optimize the number of neighbors to combine. This study applies the novel approach to Korean stock market. Experimental results show that the GA-optimized k-NN approach outperforms other AI techniques for stock market prediction.

• Journal of Venture Innovation
• /
• v.2 no.1
• /
• pp.1-11
• /
• 2019

Many research or consulting institute refered to Artificial Intelligence, Internet of Things, Blockchain technology and 3D Printing as key driving forces and technologies of 4th industrial revolution. Compared with traditional manufacturing as a subtractive manufacturing(SM), 3D printing technology as an additive manufacturing(AM) will revolutionary impacts on many industries. This study compared 3D printing with traditional manufacturing in the economic, manufacturing, and marketing perspectives. This study also analyzed issues of 3D printing for the purpose of building business ecosystem. Finally agenda for the further research were suggested.