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Marketing mix modeling for pharmaceutical companies on the basis of data science technologies

Published Online: Aug 26, 2021
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The article contains the results of Data Science technologies application (including machine learning and regression analysis) to modelling the results of marketing activities of key brand of one of the Ukrainian pharmaceutical companies on the basis of historical data for the period from 2015 to 2019 in weekly detail. The main goal of research is to estimate the influence of key elements of the marketing mix (penetration of pharmacy chains, price policy vs main competitors, advertising activity of the brand and its competitors in all communication channels (television, Digital, radio, outdoor advertising, press)) on company’s sales, volume market share and value market share in relevant segment of drugs. Based on the results obtained, the article explains in detail the impact of penetration, price policy and media activity on the competitiveness of the enterprise and its position in the market. The influence of the price policy and penetration directly on sales (market share), as well as on other factors (including the effectiveness of the brand's advertising activity on television) is estimated and taken into account for development the effective marketing strategy. Based on the research, the article contains main recommendations for optimizing the marketing strategy to maximize the company's sales and increasing market share in monetary or physical terms. Data Science technologies become a tool for sales management, because it creates the ability to quantify the impact of each factor on sales, determine their optimal combination for achievement of business goals and strengthening the company's position in the market, effective marketing budgets distribution and scenario forecasting. Continuous model support allows to increase the return on each factor, improve return on investment and ensure the achievement of business goals in the most efficient way. Data Science forms the basis for finding effective marketing solutions and forming an effective business development strategy.
JEL Classification:
C5, M3, O1
How to cite:
Chornous, G., Fareniuk, Y. (2021). Marketing mix modeling for pharmaceutical companies on the basis of data science technologies. Access to science, business, innovation in digital economy, ACCESS Press, 2(3): 274-289.
  • Batra, R., Keller, K. (2016). Integrating marketing communications: New findings, new lessons, and new ideas. Journal of Marketing, 80(6), pp. 122–145.
  • Bowman, D., Gatignon, H. (2010). Market Response and Marketing Mix Models: Trends and Research Opportunities. Foundations and Trends® in Marketing, Vol. 4, No. 3, pp. 129-207. DOI:
  • Brown, M. S. (2015). What IT Needs To Know About The Data Mining Process. Forbes.
  • Büschken, J. (2007). Determinants of Brand Advertising Efficiency: Evidence from the German Car Market. Journal of Advertising, Vol. 36, No. 3, pp. 51-73. DOI:
  • Chan, D., Perry, M. (2017). Challenges and Opportunities in Media Mix Modeling, Technical report, Google Inc, (accessed: August 2021).
  • Chernyak, O., Zaharchenko, P. (2014). Data mining: Textbook, Znannya, Kyiv (UA).
  • Dawes, J., Kennedy, R., Green, K. (2018). Forecasting advertising and media effects on sales: Econometrics and alternatives. International Journal of Market Research, Vol. 60, No. 6, pp. 611-620. DOI:
  • De Toni, D., Milan, G., Saciloto, E., Larentis, F. (2017). Pricing strategies and levels and their impact on corporate profitability. Revista de Administração, Vol. 52 (2), p. 120-133. DOI:
  • Farm, A. (2020). Pricing in practice in consumer markets. Journal of Post Keynesian Economics, Vol. 43:1, pp. 61-75. DOI:
  • Jin, Y., Wang, Y., Sun, Y., Chan, D., Koehler, J. (2017). Bayesian Methods for Media Mix Modeling with Carryover and Shape Effects, Technical report, Google Inc. (accessed: August 2021).
  • Kirsanov, D. (2019). Advertising of pharmaceutical brands in various media based on the results of 9 months of 2019 Helicopter view. Pharmacy Online, No. 44 (1215), (accessed: 01 August 2021).
  • Kirsanov, D. (2019b). Ukrainian pharmacy market for 9 months of 2019: Helicopter View. Pharmacy Online, No. 41 (1212), (accessed: August 2021).
  • Kizim, M., Geiman, O. (2009). Scenario modeling of development of social and economic systems: directions, features and mechanisms. Regional economics, №4, pp. 16-23. (accessed: August 2021).
  • Korzh, M. (2018). Price optimization modeling in international marketing. Foreign trade: economics, finance, law, №5, pp. 87-100.
  • Myshko, O., Kaminska, I. (2021). Formation of price policy of trade enterprises in modern conditions. Economy and society, (23). DOI:
  • Noritsina, N. (2007). Marketing pricing as a factor of profitable activity of the enterprise. Marketing in Ukraine, №5, pp. 41–43.
  • Osypenko, S., Romanchyk, T., Pisarevsky, S. (2020). Substantiating the Prices for Enterprise Products on the Basis of Optimization Models. Business Inform, №6, p. 145-151.
  • Pergelova, A., Prior, D., Rialp, J. (2010). Assessing advertising efficiency. Journal of Advertising, Vol. 39/3. DOI:
  • Ponomarenko, V., Klebanova, T., Guryanova, L. (2020). System analysis and modeling of management. Bratislava-Kharkiv, HSEM - KhNUE im. S. Kuznets, 288 p.
  • Ponomarenko, V., Klebanova, T., Kizim, O. (2013). Models of Assessment and Analysis of Complex Socio-Economic Systems: Monograph. Kherson, Publishing House "Inzhek", 664 p.
  • Ryzhikov, V., Pankov V., Rovenska V., Pidgora Y. (2004). Business Economics: Textbook. Kyiv: Slovo Publishing House, 253 p.
  • Rossiter, J., Percy, L. (2017). Methodological Guidelines for Advertising Research. Journal of Advertising, 46 (1), pp. 71-82.
  • Sandage, C., Fryburger V. (1976). Advertising Theory and Practice. Ninth edition. Homewood, Illinois: Richard D. Irwin, Inc. Journal of Advertising, 5:1, 43, DOI: 10.1080/00913367.1976.10672626.
  • Shearer, C. (2000). The CRISP-DM model: the new blueprint for data mining. J Data Warehousing, 5:13-22.
  • Slushaenko, N., Apenko, O. (2015). Modern methods of pricing in the strategic activities planning of pharmaceutical companies in Ukraine. Bulletin of the Taras Shevchenko National University of Kyiv. Economy, Vol. 4, p. 58-63. DOI:
  • Tarasevich, V. M. (2010). Pricing policy of the enterprise. 3rd ed, Saint-Peterburg, 320 p.
  • Vinkovska, A., Kiv, A., Koycheva, T., Bodnar, L., Donchev, I. (2019). Information model of the economic efficiency of advertising. SHS Web Conf., 65, p. 1-6. DOI:
  • Website of Nielsen Ukraine,
  • Website of Proxima Research,
  • Website of State Statistics Service of Ukraine,
  • Website of Television Industry Committee,
  • Website of VRK,
  • Zhang, S., Vaver, J. (2017). Introduction to the Aggregate Marketing System Simulator, Technical report, Google Inc, (accessed: August 2021).
  • Zhukov, S., Fedurtsa, V., Gromova, Y. (2014). Optimization of marketing price policy of industrial enterprises. Actual problems of economy: Scientific economic journal, №6, pp. 213-219.

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