Научный журнал Байкальского государственного университета
Вопросы теории и практики журналистики
ISSN 2308-6203 (Print), 2308-6211 (Online)
Издается с 2012 года
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Название статьи:

Efficient Planning and Selection of Media Advertising Using Linier Programming and Machine Learning Methods

Авторы:
Dagdandorj Amgalanbaatar, PhD Student, Department of Marketing and International Trade, National University of Mongolia, Ulaanbaatar, Mongolia, amgalanbaatar@mmcg.mn,

Namsrai Batnasan, D.Sc. in Economics, Professor, Department of Marketing and International Trade, National University of Mongolia, Ulaanbaatar, Mongolia, batnasan@num.edu.mn
В рубрике:
СОЦИАЛЬНЫЙ МАРКЕТИНГ, PR И РЕКЛАМА
Год: 2022 Том: 11 Номер журнала: 1
Страницы: 144-157
Тип статьи: Научная статья
УДК: 070:659.1(517.3)
DOI: 10.17150/2308-6203.2022.11(1).144-157
Аннотация:
Due to rapid development of information technology and drastic innovations in technology that change customers' media use behavior, it is necessary to analyze the data and make systematic predictions in media planning and selection. Our research used linear programming method to plan and select media and put forward the most effective way to deliver advertising to the most targeted users at the lowest cost. In this study we used data from 2,451 households in 9 districts of Ulaanbaatar, 17 local aimags*, and 26 soums* in the 2020 National Syndicate Survey to determine the lifestyle of Mongolians, as well as data from more than 300 media outlets of 9 types, including such as television, internet, and social media. The study examined the factors that influence amount of time spent on television, website, social media, and FM radio using multinomial logit and multinomial probit models. However, the fact that consumers spend a lot of time on a media outlet does not mean that their purchasing decision will be affected. Therefore, we used the feature selection method based on 16 indicators to determine which media channel ad the customer viewed and made a purchase. We analyzed media users based on 54 data mining and classification indicators, including age, gender, and location. As a result of the study we developed a framework, highlighted the national media use behavior, and proposed a media planning methodology for businesses. The principal author of the article Mr. Amgalanbaatar Dagdandorj is a doctoral student at the Department of Marketing and International Trade of the Business School, National University of Mongolia. He is a CEO of the research and consultation company Mongolian Marketing Consulting group. He developed a research methodology for the National media use syndicate survey and received the copyright certificate. The current research analyzed the survey data implemented during the last eight consecutive years in Mongolia within the dissertation framework of Efficient planning and selection of media advertising using linear programming and machine learning methods as a requirement for a degree of Doctor in Business Administration. As for this work, D. Amgalanbaatar, as the principal author of the article, worked on the research data analysis, developed a methodology and modeling as well as media planning model and recommendations for its application. The last author of this article, Dr. Batnasan Namsrai, works as a Professor at the Marketing and International Trade Department of the Business School, National University of Mongolia. He is the academic advisor of the doctoral student and worked as a consultant in developing the methodology and design of the National media use syndicate survey. As the last author of the current article, he formulated a general methodology and developed the logic and format of a scientific article. The findings and the planning model developed on its basis can be the primary source for effective planning of the country's marketing and advertising activities of Mongolian business, government and media organizations.
Ключевые слова: prospective consumers, consumer segmentation, behavior, media planning, and selection
Список цитируемой литературы:
  • Kumar V. JIT Based Quality Management: Concepts and Implications in Indian Context. International Journal of Engineering Science and Technology, 2010, vol. 2, iss. 1, pp. 40-50.
  • Catherine H. Linear Programming: Theory and Applications. Berlin, Springer, 2008. 66 p.
  • Anderson D.R., Sweeney D.J., Williams A.T. Quantitative Approaches to Decision Making. An Introduction to Management Science. New York, Cengage Learning, 2014. 912 p.
  • Chandon J.L. A Comparative Study of Media Exposure Models. Northwestern University. New York, Garland Publ., 1976. 631 p.
  • Sunoo H., Lin D. A Search for Optimal Advertising Spending Level. Journal of Advertising, 1979, vol. 8, no. 3, pp. 25-28.
  • Lee A.M., Burkart A.J. Some Optimization Problems in Advertising Media Planning. Operational Research Quarterly, 1960, vol. 11, no. 3, pp. 113-122.
  • Mihiotis A., Tsakiris L. Mathematical Programming Study of Advertising Allocation Problem. Applied Mathematics and Computation, 2004, vol. 148, pp. 373-379.
  • Crone S., Lessmann S., Stahlblock R. The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing. European Journal of Operational Research, 2006, vol. 173, no. 3, pp. 781-800.
  • Choudhury A., Nur KA Machine Learning Approach to Identify Potential Customer Based on Purchase Behavior. International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), January 10-12. Dhaka, 2019, pp. 242-247.
  • Lagree P., Cappe O., Cautis B., Maniu S. Algorithms for Online Influencer Marketing. ACM Transactions on Knowledge Discovery from Data (TKDD), 2019, vol. 13, no. 1, pp. 1-30.
  • Syam N., Sharma A. Waiting for a sales renais-sance in the fourth industrial revolution: Machine learning and artifi cial intelligence in sales research and practice. Industrial Marketing Management, 2018, vol. 69, pp. 135-146. DOI:10.1016/j.indmarman.
  • Soureshjani M.H., Kimiagari A.M. Calculating the best cut off point using logistic regression and neural network on credit scoring problem - A case study of a commercial bank. African Journal of Business Management, 2013, vol. 7, no. 16, pp. 1414-1421. DOI: 10.5897/AJBM11.394.