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구글 트렌드 빅데이터를 통한 바이오의약품의 시장 점유율 분석과 추정

Analysis and Estimation for Market Share of Biologicsbased on Google Trends Big Data

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초록

Google Trends is a useful tool not only for setting search periods, but also for providing search volume to specific countries, regions, and cities. Extant research showed that the big data from Google Trends could be used for an on-line market analysis of opinion sensitive products instead of an on-site survey. This study investigated the market share of tumor necrosis factor-alpha (TNF-α) inhibitor, which is in a great demand pharmaceutical product, based on big data analysis provided by Google Trends. In this case study, the consumer interest data from Google Trends were compared to the actual product sales of Top 3 TNF-α inhibitors (Enbrel, Remicade, and Humira). A correlation analysis and relative gap were analyzed by statistical analysis between sales-based market share and interest-based market share. Besides, in the country-specific analysis, three major countries (USA, Germany, and France) were selected for market share analysis for Top 3 TNF-α inhibitors. As a result, significant correlation and similarity were identified by data analysis. In the case of Remicade’s biosimilars, the consumer interest in two biosimilar products (Inflectra and Renflexis) increased after the FDA approval. The analytical data showed that Google Trends is a powerful tool for market share estimation for biosimilars. This study is the first investigation in market share analysis for pharmaceutical products using Google Trends big data, and it shows that global and regional market share analysis and estimation are applicable for the interest-sensitive products.

Abstract

Google Trends is a useful tool not only for setting search periods, but also for providing search volume to specific countries, regions, and cities. Extant research showed that the big data from Google Trends could be used for an on-line market analysis of opinion sensitive products instead of an on-site survey. This study investigated the market share of tumor necrosis factor-alpha (TNF-α) inhibitor, which is in a great demand pharmaceutical product, based on big data analysis provided by Google Trends. In this case study, the consumer interest data from Google Trends were compared to the actual product sales of Top 3 TNF-α inhibitors (Enbrel, Remicade, and Humira). A correlation analysis and relative gap were analyzed by statistical analysis between sales-based market share and interest-based market share. Besides, in the country-specific analysis, three major countries (USA, Germany, and France) were selected for market share analysis for Top 3 TNF-α inhibitors. As a result, significant correlation and similarity were identified by data analysis. In the case of Remicade’s biosimilars, the consumer interest in two biosimilar products (Inflectra and Renflexis) increased after the FDA approval. The analytical data showed that Google Trends is a powerful tool for market share estimation for biosimilars. This study is the first investigation in market share analysis for pharmaceutical products using Google Trends big data, and it shows that global and regional market share analysis and estimation are applicable for the interest-sensitive products.

참고문헌 (22)

  1. Bae, E. Y., "Determinants of drug market share", The Korean Journal of Health Economics and Policy, 6(2), pp.1~30 (2000)
  2. "BIOSPECTATOR"[URL]
  3. "BIOSPECTATOR"[URL]
  4. Strategic insights into biopharmaceuticals industry, "Biotech Policy Research Center", Frost & Sullivan Analysis, ~ (2019)
  5. Industrial analysis report of biologics, "BP Technology Trade", 2~29 (2018)
  6. Chamberlin, G., "Googling the present", The Labour gazette, 4(12), pp.59~95 (2010)
  7. Choi, H., "Predicting the present with Google Trends", Economic Record, 88(1), pp.2~9 (2012)
  8. Dugas, A. F., "Google flu trends : Correlation with emergency department influenza rates and crowding metrics", Clinical Infectious Diseases, 54(4), pp.464~469 (2012)
  9. "FDA"[URL]
  10. "Fortune Korea"[URL]
  11. "Korea biologics and biohealth information assistance system"[URL]
  12. Lee, B., "Company update : Samsung biologics", Samsung Securities, 6~ (2017)
  13. 이중배, "스마트폰 구매결정 요인이 재구매 의도에 미치는 영향", 한국산업경영시스템학회지, 40(2), pp.1~12 (2017), 한국산업경영시스템학회
  14. Magazzini, L., "Dynamic competition in pharmaceuticals Patent expiry, generic penetration, and industry structure", European Journal of Health Economics, 5, pp.175~182 (2004)
  15. "Naver"[URL]
  16. 오근엽, "제약산업에서 신약의 특허기간 만료 후 복제약의 시장 점유율 변화 추정", 보건경제와 정책연구, 13(1), pp.117~138 (2007), 한국보건경제정책학회
  17. Qin, S. J., "Process data analytics in the era of big data", AIChE Journal, 60(9), pp.3092~3100 (2014)
  18. 신민수, "나노 인포매틱스 기반 구축을 위한 구글 트렌드와 데이터 마이닝 기법을 활용한 나노 기술 트렌드 분석", 한국산업경영시스템학회지, 40(4), pp.237~245 (2017), 한국산업경영시스템학회
  19. 손정웅, "Attributes of Social Networking Services : A Classification and Comparison", 한국산업경영시스템학회지, 41(1), pp.24~38 (2018), 한국산업경영시스템학회[DOI]
  20. "Stress and Coping Strategies among Commuter and Noncommuter Students", pp.2013
  21. 윤성욱, "Nowcast of TV Market using Google Trend Data", Journal of Electrical Engineering & Technology, 11(1), pp.227~233 (2016), 대한전기학회
  22. Yu, J. P., "Forecasting company sales and stock price using google trend : Focusing on the keywords of BMW and Mercedes-Benz, the convergent research society among humanities", Sociology, Science, and Technology, 8(10), pp.491~501 (2018)

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