Modeling optimal price policy of pharmaceutical companies for sales maximization based on Data Science technologies

Authors: O. Chernyak, Dr Hab., Prof., ORCID ID 0000-0002-0453-0063, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine, Dr. J. Sztrik, Dr Hab., Prof., ORCID ID 0000-0002-5303-818X, Faculty of Informatics, University of Debrecen, Hungary, Y. Fareniuk, Economist, ORCID ID 0000-0001-6837-5042, Taras Shevchenko National University of Kyiv, Kyiv, Ukraine

Annotation: Social capital has become an important aspect of most rural communities in developing nations. But, the dimensions of social capital vary across rural regions while little is known about the factors influencing it in rural areas. This study aimed to identify the prevalent social capital dimensions in rural areas and examine the factors determining rural people involved in those dimensions. A field survey which consists of structured and self-administered questionnaire was carried out with rural households. The information of the survey was obtained from 220 rural households in the study area between August and October, 2019. The descriptive analysis identified social networks (3.875), norms (societal values) (3.390), trust and solidarity (4.115), and cooperation and group action (4.139) as the prevailing social capital dimensions in the rural communities. The results further suggest that cooperation, trust and solidarity, and networks are respectively the dominating social capital dimensions in the rural areas. The results from probit model estimates show that the factors that are more likely to be associated with social capital in rural areas include education, access to credit and ownership of farm (cash crop). Since social capital is becoming a prerequisite for rural development, our findings lead to the suggestion that cooperation, build-up of networks should be facilitated for people in the rural areas. Furthermore, policy direction towards access to education, credit provision and development of primary occupation in the rural areas should also be enhanced. Economic policy makers and rural development agencies are invited to continuously work on the identified factors to promote the individual, community and national development on equitable basis.

Keywords: price policy, marketing, Data Science machine learning, regression analysis.

Received: 24/12/2020
1st Revision: 04/01/2021
Accepted: 26/02/2021

DOI: https://doi.org/10.17721/1728-2667.2021/214-1/6

Literature

  1. Zhukov S., Fedurtsa V., Gromova Y., 2014. Optimization of marketing price policy of industrial enterprises. Actual problems of economy: Scientific economic journal, № 6., 213-219 pp.
  2. Kirsanov D., 2019. Ukrainian pharmacy market for 9 months of 2019: Helicopter View. Pharmacy Online, No. 41 (1212). https://www.apteka.ua/ article/519677.
  3. Website of State Statistics Service of Ukraine. http://www.ukrstat.gov.ua/.
  4. Korzh M., 2018. Price optimization modeling in international marketing. Foreign trade: economics, finance, law, №5, 87-100 pp.
  5. Balabanova L., Sardak O., 2003. Price policy of a trading company in terms of marketing orientation: monograph. Donetsk, DonDUET them. M. Tugan-Baranovsky, 149 pp.
  6. Litvinenko Y., 2010. Marketing price policy. Kyiv, Knowledges, 294 pp.
  7. Ivanova R., 2000. A game approach to market pricing. Industrial economics. Ukraine’s Economy on the threshold of the Third Millennium: Collection of Scientific works, Donetsk: NAS of Ukraine. Institute of Economics of Industry. JSC NKMZ., 295–299 pp.
  8. Lipsits I., 1999. Commercial pricing. M.: BEK, 368 pp.
  9. Noritsina N., 2007. Marketing pricing as a factor of profitable activity of the enterprise. Marketing in Ukraine, No 5, 41–43 pp.
  10. Malish O., 2002. An analysis of the optimization of the commodity price solutions of the enterprise. Marketing in Ukraine, No. 5, 43–47 pp.
  11. Büschken J., 2007. Determinants of Brand Advertising Efficiency: Evidence from the German Car Market. Journal of Advertising, Vol. 36, No. 3, pp. 51-73.
  12. Shakhov D.A., Panasenko A.A., 2012. Evaluating Effectiveness of Bank Advertising in the Internet: Theory and Practice, World Applied Sciences Journal 18 (Special Issue of Economics): pp. 83-90.
  13. Pergelova A., Prior D., Rialp J., 2010. Assessing advertising efficiency. Journal of Advertising, v. 39/3.
  14. Chan D., Perry M., 2017. Challenges and Opportunities in Media Mix Modeling. Technical report, Google Inc, 2017. https://ai.google/ research/pubs/pub45998.
  15. 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: https://doi.org/10.1177/1470785318782871.
  16. 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. https://static.googleusercontent.com/media/ research.google.com/ru//pubs/archive/46001.pdf.
  17. Zhang S., Vaver J., 2017. Introduction to the Aggregate Marketing System Simulator. Technical report, Google Inc. https://research.google/ pubs/pub45996/.
  18. Website of Nielsen Ukraine. https://www.nielsen.com/ua/uk/.
  19. Website of Proxima Research. https://proximaresearch.ua/en/.
  20. Website of VRK. https://vrk.org.ua/.
  21. Brown M.S., 2015. What IT Needs To Know About The Data Mining Process. Forbes.
  22. Shearer C., 2000. The CRISP-DM model: the new blueprint for data mining. J Data Warehousing, 5:13-22.
  23. Chernyak O., Zaharchenko P., 2014. Data mining: Textbook. Znannya, Kyiv.
  24. Website of Television Industry Committee. http://www.itk.ua/en/ root/index/
  25. 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). https://www.apteka.ua/article/521815.

Download