DOI QR코드

DOI QR Code

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

구글 트렌드 빅데이터를 통한 바이오의약품의 시장 점유율 분석과 추정

  • Bong, Ki Tae (Department of Management of Technology, Sungkyunkwan University) ;
  • Lee, Heesang (Department of Management of Technology, Sungkyunkwan University)
  • 봉기태 (성균관대학교 기술경영전문대학원) ;
  • 이희상 (성균관대학교 기술경영전문대학원)
  • Received : 2020.02.24
  • Accepted : 2020.04.02
  • Published : 2020.06.30

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.

Keywords

References

  1. Bae, E.Y., Determinants of drug market share, The Korean Journal of Health Economics and Policy, 2000, Vol. 6, No. 2, pp. 1-30.
  2. BIOSPECTATOR, http://www.biospectator.com/view/news_view.php.
  3. BIOSPECTATOR, http://www.biospectator.com/view/news_view.php?varAtcId=7055.
  4. Biotech Policy Research Center, Strategic insights into biopharmaceuticals industry, Frost & Sullivan Analysis, 2019. 3.
  5. BP Technology Trade, Industrial analysis report of biologics, 2018, pp. 2-29.
  6. Chamberlin, G., Googling the present, The Labour gazette, 2010, Vol. 4, No. 12, pp. 59-95.
  7. Choi, H. and Varian, H., Predicting the present with Google Trends, Economic Record, 2012, Vol. 88, No. 1, pp. 2-9. https://doi.org/10.1111/j.1475-4932.2012.00809.x
  8. Dugas, A.F., Hsieh, Y.H., Levin, S.R., Pines, J.M., Mareiniss, D.P., Mohareb, A., and Rothman, R.E., Google flu trends : Correlation with emergency department influenza rates and crowding metrics, Clinical Infectious Diseases, 2012, Vol. 54, No. 4, pp. 464-469. https://doi.org/10.1093/cid/cis050
  9. FDA, https://www.fda.gov/drugs/therapeutic-biologicsapplications-bla/purple-book-lists-licensed-biological-products-reference-product-exclusivity-and-biosimilarity-or.
  10. Fortune Korea, http://www.fortunekorea.co.kr/news/articleView.html?idxno=11536.
  11. Korea biologics and biohealth information assistance system, http://www.kobics.or.kr/UBFCtr.do.
  12. Lee, B. and Park, W.Y., Company update : Samsung biologics, Samsung Securities, 2017, p. 6.
  13. Lee, J.B. and Paek, D.H., The effect of smartphone purchasing determinants on repurchase intention, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 2, pp. 1-12.
  14. Magazzini, L., Pammolli, F., and Riccaboni, M., Dynamic competition in pharmaceuticals Patent expiry, generic penetration, and industry structure, European Journal of Health Economics, 2004, Vol. 5, pp. 175-182.
  15. Naver, https://blog.naver.com/sigmagil/221502514892.
  16. Oh, K.Y., Kim, B.H., and Kim, T.G., Estimating market share function of generic drugs after patent expiration in Korea, The Korean Journal of Health Economics and Policy, 2007, Vol. 13, No. 1, pp. 117-138.
  17. Qin, S. J., Process data analytics in the era of big data, AIChE Journal, 2014, Vol. 60, No. 9, pp. 3092-3100. https://doi.org/10.1002/aic.14523
  18. Shin, M.S., Park, M.G., and Bae, S.H., Nano technology trend analysis using google trend and data mining method for nano-informatics, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 40, No. 4, pp. 237-245. https://doi.org/10.11627/jkise.2017.40.4.237
  19. Sohn, J.W. and Kim, J.K., Attributes of social networking services : A classification and comparison, Journal of Society of Korea Industrial and Systems Engineering, 2017, Vol. 41, No. 1, pp. 24-38. https://doi.org/10.11627/jkise.2018.41.1.024
  20. Stress and Coping Strategies among Commuter and Noncommuter Students, [dissertation], [Kedah, Malaysia] : Universiti Utara Malaysia, 2013.
  21. Youn, S. and Cho, H.C., Nowcast of TV market using google trend data, Journal of Electrical Engineering and Technology, 2016, Vol. 11, No. 1, pp. 227-233. https://doi.org/10.5370/JEET.2016.11.1.227
  22. Yu, J.P. and Lee, B.U., 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, 2018, Vol. 8, No. 10, pp. 491-501.

Cited by

  1. Leucine zipper도메인의 융합에 의한 바이오시밀러 레미케이드 Single-chain Fv 항체의 항원 결합력 개선 vol.30, pp.11, 2020, https://doi.org/10.5352/jls.2020.30.11.1012
  2. 인공지능의 사회적 수용도에 따른 키워드 검색량 기반 주가예측모형 비교연구 vol.27, pp.1, 2020, https://doi.org/10.13088/jiis.2021.27.1.103