Novosibirsk, Novosibirsk, Russian Federation
Institute of Economics and Industrial Engineering
Novosibirsk, Novosibirsk, Russian Federation
Institute of Economics and Industrial Engineering
Novosibirsk State Technical University
Novosibirsk, Novosibirsk, Russian Federation
VAK Russia 5.2.3
In the modern business environment, companies make significant investments across diverse marketing channels, ranging from traditional media (television advertising, outdoor advertising, print media, etc.) to contemporary digital formats (contextual advertising, influencer marketing, social networks, email campaigns, etc.). This wide array of channels creates a complex system of marketing communications, which in turn necessitates precise and systematic assessment of each individual channel’s contribution, as well as their interactions, in achieving business objectives and maximizing marketing investment efficiency. This paper presents the theoretical foundations of the Marketing Mix Modeling (MMM) concept, aimed at modeling and analyzing the influence of individual components of marketing strategies on key business indicators such as sales, profit, or market share. The research pays special attention to studying the effects of interactions between marketing channels, as well as their dynamic aspects: synergy effects, saturation, and Adstock. These considerations facilitate a more accurate evaluation of temporal sequences and relationships between investments and outcomes. Furthermore, the work provides a detailed examination of Bayesian methods. These techniques allow for effective integration of prior knowledge and expert assessments into the models, which is especially important in conditions of limited data, high multicollinearity between channels, or uncertainties. Bayesian approaches not only enhance the accuracy of estimates but also ensure reliable interval estimations, and they can easily model complex, hierarchical, and nonlinear interactions between components of the marketing mix, making the analysis more flexible and adaptable to various conditions.
marketing mix modelling, Bayesian approach, synergy effect, funnel effect, saturation effect, Adstock effect, advertising channels, causal inference
1. Birim S., Kazancoglu I., Mangla S. K., Kahraman A., Kazancoglu Y. The derived demand for advertising expenses and implications on sustainability: a comparative study using deep learning and traditional machine learning methods // Annals of Operations Research. 2024. Vol. 339. P. 131-161. DOI: https://doi.org/10.1007/s10479-021-04429-x
2. Bishop C. Pattern Recognition and Machine Learning // Journal of Electronic Imaging. 2006. Vol. 16(4). P. 140-155. DOI: https://doi.org/10.1117/1.2819119
3. Borden N. H. The concept of the marketing mix // Journal of Advertising Research. 1964. Vol. 4(2). P. 7-12. DOI: https://doi.org/10.1080/00218499.1964.12519724
4. Briggs R., Krishnan R., Borin N. Integrated Multichannel Communication Strategies: Evaluating the Impact on Consumer Purchase Decisions // Journal of Advertising Research. 2005. Vol. 45(4). P. 375-387.
5. BNg & Wang, 2024 S. Modelling with Adstock // Journal of the Market Research Society. 1984. Vol. 26(4). P. 295-312.
6. Chan D., Perry M. Challenges and opportunities in media mix modeling // Google Inc. 2017, April 14.
7. Chornous G., Fareniuk Y. Marketing mix modeling for pharmaceutical companies on the basis of data science technologies // Access Journal. 2021. Vol. 2(3). P. 274-289. DOI: https://doi.org/10.46656/access.2021.2.3(6)
8. Funk M. J. Doubly Robust Estimation of Causal Effects // American Journal of Epidemiology. 2011. Vol. 173(7). P. 761-767. DOI: https://doi.org/10.1093/aje/kwq439
9. Jin Y., Wang Y., Sun Y., Chan D., Koehler J. Bayesian methods for media mix modeling with carryover and shape effects // Google Inc. 2017, April 14.
10. Kireyev P., Pauwels K., Gupta S. Do Display Ads Influence Search? Attribution and Dynamics in Online Advertising // International Journal of Research in Marketing. 2016. Vol. 33(3). P. 475-490. DOI: https://doi.org/10.1016/j.ijresmar.2015.09.007
11. Koyck L. M. Distributed Lags and Investment Analysis. Amsterdam: North-Holland, 1954. 111 p.
12. Kruschke J. K. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. Academic Press, 2015. 749 p. DOI: https://doi.org/10.1016/B978-0-12-405888-0.00008-8
13. Lambrecht A., Tucker C. When Does Retargeting Work? // Journal of Marketing Research. 2013. Vol. 50(5). P. 561-576. DOI: https://doi.org/10.1177/002224371305000508
14. Lewis R. A., Rao J. M. The unfavorable economics of measuring the returns to advertising // Quarterly Journal of Economics. 2015. Vol. 130(4). P. 1941-1973. DOI: https://doi.org/10.1093/qje/qjv023
15. Li H., Kannan P. K. Attributing Conversions in a Multichannel Online Marketing Environment // Marketing Science. 2014. Vol. 33(1). P. 40-56. DOI: https://doi.org/10.1509/jmr.13.0050
16. McCarthy J. E. Basic Marketing: A Managerial Approach. Homewood, IL: Richard D. Irwin, 1960. 770 p.
17. McElreath R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC Press, 2020. 483 p. DOI: https://doi.org/10.1201/9780429029608
18. Morais J., Thomas-Agnan C., Simioni M. Impact of advertising on brand’s market shares in the automobile market: a multi-channel attraction model with competition and carryover effect // TSE Working Paper. 2018. No. 18(878).
19. Naik P. A., Raman K. Understanding the Impact of Synergy in Multimedia Communications // Journal of Marketing Research. 2003. Vol. 40(4). P. 375-388. DOI: https://doi.org/10.1509/jmkr.40.4.375.19385; EDN: https://elibrary.ru/FHWBND
20. Ng E., Wang Z., Dai A. Bayesian Time Varying Coefficient Model with Applications to Marketing Mix Modeling // arXiv preprint arXiv:2106.03322v4. 2024.
21. Pandey S., Gupta S., Chhajed S. Marketing Mix Modeling (MMM) – Concepts and Model Interpretation // International Journal of Engineering Research & Technology (IJERT). 2021. Vol. 10(6). P. 784-793.
22. Pearl J. An Introduction to Causal Inference // The International Journal of Biostatistics. 2010. Vol. 6(2). P. 1-62. DOI: https://doi.org/10.2202/1557-4679.1203
23. Rimša R. Marketing Mix Modelling using Bayesian statistics (Master’s thesis). Vilnius University, Vilnius. 2024, 38 p.
24. Rossi P. E. Bayesian Statistics and Marketing. Wiley, 2014. 384 p.
25. Sun Y., Wang Y., Jin Y., Chan D., Koehler J. Geo-level Bayesian hierarchical media mix modeling // Google Inc. 2017, April 14.
26. Tellis J. Modeling marketing mix // The handbook of marketing research: uses, misuses, and future advances. 2006. P. 506-522. DOI: https://doi.org/10.4135/9781412973380.n24
27. Wang Y., Jin Y., Sun Y., Chan D., Koehler J. A hierarchical Bayesian approach to improve media mix models using category data // Google Inc. 2017, April 14.
28. Weibull W. A Statistical Distribution Function of Wide Applicability // Journal of Applied Mechanics. 1951. Vol. 18(3). P. 293-297. DOI: https://doi.org/10.1115/1.4010337
29. Wigren R., Cornell F. Marketing Mix Modelling: A comparative study of statistical models (Master’s thesis). Linköping University, Department of Computer and Information Science, 2019. 122 p.
30. Zhang S. S., Vaver J. Introduction to the Aggregate Marketing System Simulator // Google Inc. 2017, April 14.
