Evolving Practices in Marketing Attribution and Media Mix Modeling for Data-Driven Decision-Making
DOI:
https://doi.org/10.32628/IJSRST251313Keywords:
Evolving practices, Marketing attribution, Media mix modeling, Data-driven, Decision-makingAbstract
As businesses now face a range of challenges, technology integration is a dire necessity. The integration of machine learning, real-time analytics, and granular data ingestion into media mix modeling (MMM) frameworks offers valuable online and offline insights. Also, first-touch, last-touch and other traditional models of attribution are getting replaced with probabilistic and algorithmic ones that are more sophisticated. The purpose of this study is to examine the critical role of evolving practices in marketing attribution and media mix modeling (MMM). It dwells on innovative practices that integrate attribution and MMM. It demonstrates that Shapely value-based attribution, Bayesian hierarchical modeling, and synthetic methods allow for effective evaluation of the trade-offs among model scalability, accuracy, and transparency. The study also contributes to solving the problems of channel saturation, loss of signals, and data privacy regulations. The findings suggest the importance of technology integration, data-driven decision-making and integrative practices that suit marketing analytics and dynamics. Thus, the efficiency of marketing can be achieved at a significant extent through strategic models, data-driven decision-making and other related practices.
📊 Article Downloads
References
Akinloye, A., & Shawana, T. A. (2023). Gas caps detection, hydrocarbon recovery and environmental sustainability. Journal of Applied Ecology and Environmental Design (JAEED), 2(2), 118-130.
Arora, N., Berman, R., Feit, E., Hanssens, D., Li, A., Lovett, M., Mela, C., Wilbur, K., & Lynch, J. (2023). Charting the future of marketing mix modeling best practices. Marketing Science Institute Blue Ribbon Panel Report. https://www.msi.org/presentation/msi-blue- ribbon-panel-report-charting-the-future-of-marketing-mix-modeling-best-practices
Baig, F., & Shahzad, S. U. (2022). Impact of social media influencer’s credibility dimensions on consumer behavior: An empirical study related to influencer marketing on Pakistan’s fashion industry. Retrieved from https://www.divaportal.org/smash/record.jsf?pid=diva2%3A1687888&dswid=588
Batat, W. (2024). Why is the traditional marketing mix dead? Towards the “experiential marketing mix”(7E): A strategic framework for business experience design in the phygital age. Journal of Strategic Marketing, 32(2), 101–113. https://doi.org/10.1080/0965254x.2022.2129745 DOI: https://doi.org/10.1080/0965254X.2022.2129745
Bhargava, N., Galvin, R., & Moakler, R. (2021). How focusing on true value empowers modern marketing measurement. Facebook IQ. https://en- gb.facebook.com/business/news/insights/how-focusing-on-true-value-empowers-modern- marketing-measurement]
Binet, L., Kite, G., Cook, L., Cross, M., Deykin, A., Andrew, M., Charles, N., Stallwood, A., Jones, S., Gascoigne, J., Talbot, J., Duckworth, S., Dickerson, S., Heppenstall, S., Charles, D., Hilton, S., & Fisher, T. (2023). An open letter to marketing departments from experts on evaluating advertising. WARC guest post. https://www.warc.com/newsandopinion/opinion/an-open-letter-to-marketing- departments-from-experts-on-evaluating-advertising/en-gb/6261
Cain, P. M. (2022). Modelling short-and long-term marketing effects in the consumer purchase journey. International Journal of Research in Marketing, 39(1), 96-116. DOI: https://doi.org/10.1016/j.ijresmar.2021.06.006
Chaffey, D., & Ellis-Chadwick, F. (2019). Digital marketing. Pearson uk.
Chan, D., & Perry, M. (2017). Challenges and opportunities in media mix modeling. Google Whitepaper.
Chen, H., Zhang, M., Han, L., & Lim, A. (2021). Hierarchical marketing mix models with sign constraints. Journal of Applied Statistics, 48(13-15), 2944–2960. DOI: https://doi.org/10.1080/02664763.2021.1946020
De Haan, E., Wiesel, T., & Pauwels, K. (2016). The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework. International Journal of Research in Marketing, 33(3), 491-507. DOI: https://doi.org/10.1016/j.ijresmar.2015.12.001
Ding, Y., DeSarbo, W. S., Hanssens, D. M., Jedidi, K., Lynch, J. G., & Lehmann, D. R. (2020). The past, present, and future of measurement and methods in marketing analysis. Marketing Letters, 31, 175-186. DOI: https://doi.org/10.1007/s11002-020-09527-7
Firmansyah, D., & Wahdiniwaty, R. (2023). Prediction of innovation capability: The role of mediation in the relationship between digital transformation and competitiveness with the PLS-SEM model. International Journal of Management and Business Intelligence, 1(2), 125–142. https://doi.org/10.59890/ijmbi.v1i2.238 DOI: https://doi.org/10.59890/ijmbi.v1i2.238
Firmansyah, D., Wahdiniwaty, R., & Budiarti, I. (2023). Entrepreneurial performance model: a business perspective in the digital economy era. Jurnal Bisnis, Manajemen, Dan Ekonomi, 4(2), 125–150. https://doi.org/10.47747/jbme.v4i2.110 DOI: https://doi.org/10.47747/jbme.v4i2.1106
Francisco, J., & Rocha, S. M. (2019). Media mix modeling. MGI. https://run.unl.pt/handle/10362/94986
Ghandour, A. (2018). Strategic planning in Jordanian private universities: An empirical investigation. Studies in Higher Education, 43(8), 1348–1365.
Gong, C. et al. (2024). Our: Learning causal structure for marketing mix modeling. arXiv preprint. DOI: https://doi.org/10.1145/3616855.3635766
Hartman, K. (2019). How to bring your marketing mix modeling into the 21st century. Think with Google. https://www.thinkwithgoogle.com/marketing-strategies/data-and- measurement/marketing-mix-modeling-tutorial/.
Jackson, G., & Ahuja, V. (2016). Dawn of the digital age and the evolution of the marketing mix. Journal of Direct, Data and Digital Marketing Practice, 17(3), 170–186. https://doi.org/10.1057/dddmp.2016.3 DOI: https://doi.org/10.1057/dddmp.2016.3
Jha, A., & Jha, R. (2022). The art and science of marketing: building the ideal media mix model. Journal of Marketing & Supply Chain Management, 1(1), 1-2. DOI: https://doi.org/10.47363/JMSCM/2022(1)127
Jin, y., Wang, Y., Sun, Y., Chan, D., & Koehler, J. (2017). Bayesian methods for media mix modeling. Google Whitepaper.
Johnson, G. A. (2023). Inferno: A guide to field experiments in online display advertising. Journal of Economics & Management Strategy, 32(3), 469-490. DOI: https://doi.org/10.1111/jems.12513
Johnson, G., Runge, J., & Seufert, E. (2022). Privacy-centric digital advertising: implications for research. Customer Needs and Solutions, 9(1-2), 49-54. DOI: https://doi.org/10.1007/s40547-022-00125-4
Kireyev, P., Pauwels, K., & Gupta, S. (2016). Do display ads influence search?: Attribution and dynamics in online advertising. International Journal of Research in Marketing, 33(3), 475-490. DOI: https://doi.org/10.1016/j.ijresmar.2015.09.007
Li, F., Larimo, J., & Leonidou, L. C. (2021). Social media marketing strategy: Definition, conceptualization, taxonomy, validation, and future agenda. Journal of the Academy of Marketing Science, 49, 51–70. https://doi.org/10.1007/s11747-020-00733-3 DOI: https://doi.org/10.1007/s11747-020-00733-3
Mela, C., & Cooper, B. (2021). Don’t buy the wrong marketing tech. Harvard Business Review, Spotlight Series.
Mir, P., & Sadaba, T. (2022). The ultimate theory of the marketing mix: A proposal for marketers and managers. International Journal of Entrepreneurship, 26, 1-22.
Ng, E., Wang, Z., & Dai, A. (2021). Bayesian time varying coefficient model with applications to marketing mix modeling. arXiv preprint, arXiv:2106.05539.
Nwode, G. C. (2022). Impact of new media on students’ performance in written composition in selected tertiary institutions in Ebonyi State, Nigeria. PhD thesis, Department of Languages and Linguistics, Post-Graduate School, Ebonyi State University, Abakaliki
Ogirri, K. O., Akinloye, A., & Oguntona, T. I. (2024). Machine learning techniques, gas emission mitigation, and environmental safety. Journal of Science Innovation & Technology Research (JSITR), 5(9), 255-267.
Ogunsakin, O. L. (2024). Artificial Intelligence in healthcare, revamping the artificial intelligence in medical sector. Iconic Research and Engineering Journals, 7(10), 245- 258.
Ogunsakin, O. L., & Anwansedo, S. (2024). Leveraging AI for healthcare administration: Streamlining operations and reducing costs. Iconic Research and Engineering Journals, 7(10), 235-244.
Oguntona, T. I. (2023, March). Mitigating the impact of high energy prices on UK households using machine learning techniques. International Journal of Modelling & Applied Science Research, 27(9), 215-230.
Okpeseyi, S. B. (2022, November). Rating the negative effects of social media on tertiary students’ academic performance: a survey of public perceptions. International Journal of Research and Technology, 11(9), 255-269.
Oluwamayomikun, O. A., & Oparaku, D. D. (2022). Analyzing the dual impact of emerging technologies on children of 8-12 years. African Scholars Journal of Education Research and Library Practice (JERLP 8), 26(8), 211-225.
Oluwamayomikun, O. A., & Oparaku, D. D. (2023, Dec.). Creating an effective curriculum to develop cognitive and social skills in primary school children. International Journal of Education Effectiveness Research, 2(2), 88-95.
Rasi, J. (2023). Towards data-driven marketing organization. Master’s thesis submitted to the School of Business and Economics, Jyväskylä University.
Ros´ario, A. T., & Dias, J. C., (2023). How has data-driven marketing evolved: Challenges and opportunities with emerging technologies. International Journal of Information Management Data Insights, 3, 100203. https://doi.org/10.1016/j.jjimei.2023.100203 DOI: https://doi.org/10.1016/j.jjimei.2023.100203
Runge, J., & Seufert, E. (2021). Apple is changing how digital ads work. Are advertisers prepared? Harvard Business Review, digital article. https://hbr.org/2021/04/apple-is- changing-how-digital-ads-work-are-advertisers-prepared]
Sethi, S. (2024). Strategic implementation of marketing mix model analytics for data-driven decision making: a holistic view. International Journal of Leading Research Publication (IJLRP), 5(10), 1-9.
Shawana, T. A. (2022). Carbon emissions as threats to environmental sustainability: Exploring conventional and technology-based remedies. African Journal of Environmental Sciences and Renewable Energy, 8 (1). https://publications.afropolitanjournals.com/index.php/ajesre/article/view/736
Sulaj, K., & Pfoertsch, W. (2024). Introducing 5es marketing-mix: A new framework for effective marketing in the digital age. Academy of Strategic Management Journal, 23(2), 1–15.
Sun, Y., Wang, Y., Jin, Y., Chan, D., & Koehler, J. (2017). Geo-level Bayesian hierarchical media mix modeling.” Google Whitepaper.
Thabit, T., & Manaf, R. (2018). The evaluation of marketing mix elements: A case study. International Journal of Social Sciences & Educational Studies, 4, 100-109. DOI: https://doi.org/10.23918/ijsses.v4i4p100
Venkatesan, R., Farris, P. W., & Wilcox, R. T. (2021). Marketing analytics: Essential tools for data-driven decisions. University of Virginia Press. DOI: https://doi.org/10.2307/j.ctv1bd4mz9
Wichmann, J. R. K., Uppal, A., Sharma, A., & Dekimpe, M. G. (2022). A global perspective on the marketing mix across time and space. International Journal of Research in Marketing, 39(2), 502–521. https://doi.org/https://doi.org/10.1016/j.ijresmar.2021.09.001 DOI: https://doi.org/10.1016/j.ijresmar.2021.09.001
Zhang, Y. et al., (2024). Media mix model calibration with Bayesian priors. Google Research Whitepaper.
Zhang, Y., Wurm, M., Wakim, A., Li, E., & Liu, Y. (2023). Bayesian hierarchical media mix model incorporating reach and frequency data. Google Research Whitepaper.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Science and Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
https://creativecommons.org/licenses/by/4.0
