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Artificial intelligence techniques for outcome prediction in marketing strategies and big data analytics for businesses

첫 페이지 보기
  • 발행기관
    한국경영정보학회 바로가기
  • 간행물
    한국경영정보학회 정기 학술대회 바로가기
  • 통권
    2023년도 한국경영정보학회 추계 학술대회 (2023.11)바로가기
  • 페이지
    pp.164-164
  • 저자
    Donggeun Kim, Juyong Ko, Minho Sun, Jai Woo Lee
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A444619

원문정보

초록

영어
These days, business intelligence has witnessed the various challenges of big data analytics due to exponentially growing information with uncertainty existing in the market. To effectively analyze the large amount of data from various sources in business, algorithms of artificial intelligence techniques should be efficiently improved. We present a comprehensive and novel approach for the evaluation of outcome by using the interactions of features and then applying this approach to estimate the trend by jointly modelling features in management data. In terms of predictive accuracy, the proposed method outperformed machine learning methods such as regression, penalized regression, decision tree, random forest and k-nearest neighbors in the high-dimensional business data analysis. Data-preprocessing was used to curate the data for better prediction and network analysis was conducted to appropriately visualize and analyze the data analysis results. The business literature represents that investigating artificial intelligence techniques with theoretical ideas for big data analytics can have an impact on reducing costs and risks in management. Future directions have been devised to elucidate the gap between actual values in real-world data of business intelligence and predicted values by the proposed approach. Machine learning methods including features of demographic and strategic data can estimate the effect of marketing characteristics. Using the proposed method, businesses may better assess strongly correlated features with the target output in the similarly structured business data.

키워드

Artificial Intelligence Big Data Analytics Statistics Business Intelligence Marketing

저자

  • Donggeun Kim [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
  • Juyong Ko [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
  • Minho Sun [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]
  • Jai Woo Lee [ Department of Big Data Science, College of Public Policy, Korea University, Sejong ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국경영정보학회 [The Korea Society of Management information Systems]
  • 설립연도
    1989
  • 분야
    사회과학>경영학
  • 소개
    이 학회는 경영정보학의 연구 및 교류를 촉진하고 학문의 발전과 응용에 공헌함을 목적으로 합니다.

간행물

  • 간행물명
    한국경영정보학회 정기 학술대회 [KMIS Conference]
  • 간기
    반년간
  • 수록기간
    1990~2025
  • 십진분류
    KDC 325 DDC 658

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