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

Author

Listed:
  • Galyna CHORNOUS

    (Taras Shevchenko National University of Kyiv, Kyiv, Ukraine)

  • Yana FARENIUK

    (Taras Shevchenko National University of Kyiv, Kyiv, Ukraine)

Abstract

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.

Suggested Citation

  • Galyna CHORNOUS & Yana FARENIUK, 2021. "Marketing mix modeling for pharmaceutical companies on the basis of data science technologies," Access Journal, Access Press Publishing House, vol. 2(3), pages 274-289, September.
    • Handle: RePEc:aip:access:v:2:y:2021:i:3:p:274-289
    DOI: 10.46656/access.2021.2.3(6)
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    References listed on IDEAS

    as
    1. Bowman, Douglas & Gatignon, Hubert, 2010. "Market Response and Marketing Mix Models: Trends and Research Opportunities," Foundations and Trends(R) in Marketing, now publishers, vol. 4(3), pages 129-207, May.
    2. Ante Farm, 2020. "Pricing in practice in consumer markets," Journal of Post Keynesian Economics, Taylor & Francis Journals, vol. 43(1), pages 61-75, January.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Strategy; marketing mix modelling; data science; machine learning; regression analysis; pharmaceutical company; Return on investment (ROI);
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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