디지털융복합연구 (Journal of Digital Convergence)

  • 제17권7호
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  • Pages.35-41
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  • 2019
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  • 2713-6434(pISSN)
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  • 2713-6442(eISSN)
한국디지털정책학회 (The Society of Digital Policy and Management)
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기능성 화장품 마케팅의 소셜 빅데이터 분석 활용 : H사 사례를 중심으로

Application of Social Big Data Analysis for CosMedical Cosmetics Marketing : H Company Case Study

  • 황신해 (영남대학교 일반대학원 경영학과) ;
  • 구동영 (영남대학교 일반대학원 경영학과) ;
  • 김정군 (영남대학교 경영학과)
  • 투고 : 2019.04.17
  • 심사 : 2019.07.20
  • 발행 : 2019.07.28
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초록

본 연구는 소셜 빅데이터 분석을 통해 튼살 기능성 화장품 시장과 고객 분석을 수행하고 중소화장품제조 기업의 마케팅 활용 후 시사점을 도출하기 위해 수행되었다. 20만개 이상의 네이버 블로그, 네이버 까페, 인스타그램, 네이버스토어 게시글을 대상으로 R을 활용한 빅데이터 분석을 수행하였다. 키워드 빈도분석, 연관관계 분석을 통해 고객 니즈와 경쟁사 포지셔닝을 이해하고 마케팅 전략 수립을 위한 시사점을 도출하였다. 분석 결과 튼살 완화와 함께 예방이 핵심 소구점으로 파악되었고 선물용 시장을 위한 제품 라인의 확장이 주요 시사점으로 나타났고 제품에 대해 상호 보완할 수 있는 제품과의 연관성이 높은 것으로 나타났다. 전통적인 마케팅 기법과 함께 사용 시 소셜 빅데이터 분석은 증거기반의 의사 결정과 기존에 파악하지 못했던 고객과 시장의 특성 도출에 유용함을 확인하였다. 향후 연구에서는 word2vec과 같은 자동화된 문장 분류를 통해 추가적인 마케팅 인사이트를 얻을 수 있을 것으로 판단된다.

This study aims to analyze the cosmedical cosmetics market and the nature of customer through the social big data analysis. More than 80,000 posts were analyzed using R program. After data cleansing, keyword frequency analysis and association analysis were performed to understand customer needs and competitor positioning, formulated several implications for marketing strategy sophistication and implementation. Analysis results show that "prevention" is a new and essential attribute for appealing target customers. The expansion of the product line for the gift market is also suggested. It has been shown that there is a high correlation with products that can be complementary to each other. In addition to the traditional marketing technique, the social big data analysis based on evidence was useful in deriving the characteristics of the customers and the market that had not been identified before. Word2vec algorithm will be beneficial to find additional.

키워드

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Fig. 1. Frequency analysis by counseling field through R program

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Fig. 2. Frequency analysis by counseling field through R program

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Fig. 3. Frequency analysis by counseling field through R program

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Fig. 4. Frequency analysis by counseling field through R program

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Fig. 5. Linkage Analysis of Consultation Needs for 2017-2018 by R program

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Fig. 6. Linkage Analysis of Consultation Needs for 2017-2018 by R program

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Fig. 7. Frequency analysis by counseling field through R program

DJTJBT_2019_v17n7_35_f0008.png 이미지

Fig. 8. Linkage Analysis of Consultation Needs for2017-2018 by R program

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Fig. 9. Frequency analysis by counseling field through R program

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Fig. 10. Linkage Analysis of Consultation Needs for 2017-2018 by R program

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Fig. 11. Frequency analysis by counseling field through R program

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Fig. 12. Linkage Analysis of Consultation Needs for 2017-2018 by R program

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Fig. 13. Frequency analysis by counseling field through R program

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