Heuristic Simulated Annealing Modeling to Optimum Target Audience Identification in Digital Marketing: A Case Study of a Mining Industry Training Service Company

Document Type : Research Paper

Authors

1 School of Mining, College of Engineering, University of Tehran, Tehran, Iran

2 Payame Noor University

Abstract

Digital marketing has become vital to businesses' marketing strategies in today's technology and social media era. However, the effectiveness of digital marketing campaigns largely depends on accurately identifying the target audience. This study aims to implement the simulated annealing initiative algorithm for digital marketing, as well as audience classification and optimum target audience selection. Traditional methods of target audience identification, such as demographic, geographic, and psychographic segmentation, are only sometimes effective in identifying the most responsive audience. Therefore, advanced techniques such as clustering, genetic, and simulated annealing algorithms have been proposed to identify the optimum target audience. The heuristic simulated annealing algorithm is one of the most promising techniques for optimum target audience identification. It is widely used in combinatorial optimization problems and applied in various fields such as engineering, economics, management, and computer science. In this research, a digital marketing campaign is implemented for a new line to sell training courses in empowerment and competency in human resource management within the mining industry. After conducting market research, we have identified five critical segments: age, gender, income group, place of residence, and level of university education. The number of customers at each customer journey stage was 740 people in brand development, email, and advertising campaigns, of which 620 people are in the "Awareness" stage, 431 people in the "Interest" stage, 261 people in the "Consideration" stage, 203 people in the "Intend" stage, 179 people in the "Purchase" and finally, 179 People were evaluated in the "loyalty" stage for the case of educational service company. The results show we should target 20% of our marketing efforts towards the 18-24 age group, 30% towards females, 20% towards high-income individuals, 10% towards rural areas, and 20% towards University education level in BSc. The best cost per conversion we obtain is 78.105×106 Rials. The results show that the simulated annealing algorithm can be valuable for identifying the optimum target audience in digital marketing campaigns. By considering the entire customer journey and allowing for more complex audience targeting, the algorithm can help companies optimize their marketing strategies and maximize their profits.

Keywords

References

Ali, M. M. and Gabere, M. (2010). A simulated annealing-driven multi-start algorithm for bound-constrained global optimization. Journal of Computational and Applied Mathematics, 233, 2661–2674.
Alnowibet, K. A., Alshamrani, A. M., Alrasheedi, A. F., Mahdi, S., El-Alem, M., Aboutahoun, A. and Mohamed, A.W.  (2022). Efficient Modified Meta-Heuristic Technique for Unconstrained Optimization Problems. Axioms, 11, 483. https://doi.org/10.3390/axioms 11090483
Anderson, E. R. (2009). Next-generation campaign management: How campaign management will evolve to enable interactive marketing. Journal of Direct, Data, and Digital Marketing Practice, 10, 272-282.
Bonfrer, A. and Dreze, X. (2009). Real-Time Evaluation of Email Campaign Performance. Marketing Science, 28, 251-263.
Brandal, H. and Kent, R. (2003). Improving email response in a permission marketing context. International Journal of Market Research, 45(4), 1-13.
Chakraborti, S. and Sanyal, S. (2015). An Elitist Simulated Annealing Algorithm for Solving Multi-Objective Optimization Problems in the Internet of Things Design. International Journal Advanced Networking application, 7, 2784.
Chittenden, L. and Rettie, R. (2003). An evaluation of email marketing and factors affecting response. Journal of Targeting, Measurement, and Analysis for Marketing, 11(3), 203-217.
Dufrene, D. D., Englland, B. T., Lehman, C. M. and Pearson, R. A. (2005). Changes in Consumer Attitudes Resulting from Participation in a Permission E-mail Campaign. Journal of Current Issues & Research in Advertising, 27, 65-77.
Fariborzi, E. and Zahedifard, M. (2012). Email Marketing: Advantages, Disadvantages and Improving Techniques. International Journal of e-Education, e-Business, e-Management and e-Learning, 2(3), 232-236.
Hartemo, M. (2016). Email marketing in the era of the empowered consumer, Journal of Research in Interactive Marketing, 10(3). http://dx.doi.org/10.1108/JRIM-06-2015-0040
Henderson, D., Jacobson, S.H. and Johnson, A.W. (2003). The Theory and Practice of Simulated Annealing. In: Glover, F., Kochenberger, G.A. (eds) Handbook of Metaheuristics. International Series in Operations Research & Management Science, 57. Springer, Boston, MA. https://doi.org/10.1007/0-306-48056-5_10
Ištvanić, M., Milić, D. C. and Krpić, Z. (2017). Digital Marketing in the Business Environment. International Journal of Electrical and Computer Engineering Systems, 8(2), https://doi.org/10.32985/ijeces.8.2.4
Johnson, D.S., Aragon, C.R., McGeoch, L.A. and Schevon, C. (1991). Optimization by simulated annealing—an experimental evaluation; part 2, graph-coloring and number partitioning. Operations Research, 39, 378–406.
Meiri, R. and Zahavi, J. (2004). Using simulated annealing to optimize the feature selection problem in marketing applications, European Journal of Operational Research, 171(3), 842-858, https://doi.org/10.1016/j.ejor.2004.09.010
Nikandish, A., Kheleva, I. B., Yin-Fah, C. and Chuen, P. W. (2020). A Proposed Model of Electronic Mail Communication: Content Marketing Channel among Generation Y, Test; Engineering and Management, 637 – 652.
Pavlov, O. V., Melville, N. and Plice R. K. (2008). Toward a sustainable email marketing infrastructure. Journal of Business Research, 61(11), 1191-1199.
Sabbagh, F. (2021). Email Marketing: The most Important Advantages and Disadvantages, Journal of Economics and Technology Research, 2 (3), www.scholink.org/ojs/index.php/jetr
Sarkheil, H., Habibi Rad, M. and Khorasani, N. A. (2015). Measuring Water Use Efficiency and Productivity of Tehran using Data Envelopment Analysis. Journal of Natural Environment, 68(4), 595-608 (in Persian). DOI:10.22059/jne.2015.56932
Sarkheil, H., Rahbari, Sh. and Rayegani, B. (2020). Conversion based fuzzy fractal dimension integrating self-similarity and porosity, via DFS and FIS (Mamdani and Sugeno systems). Chaos Soliton Frac 140:110183. https://doi.org/10.1016/j.chaos.2020.110183
Smaili, M. Y. and Hachimi, H. (2020). Metaheuristic for optimizing targeted offers in Direct Marketing, IEEE 6th International Conference on Optimization and Applications (ICOA), DOI: 10.1109/ICOA49421.2020.9094454
Smaili, M. Y. and Hachimi, H. (2020). Population-based Method for Optimizing Targeted Offers Problem in Direct Marketing Campaigns, WSEAS TRANSACTIONS on COMPUTERS, 19, 156-164. DOI: 10.37394/23205.2020.19.21
Tang J., Zhang, R., Wanga, P., Zhao, Z., Fan, L. and Xin, L. (2020). A discrete shuffled frog-leaping algorithm to identify influential nodes for influence maximization in social networks, Knowledge-Based Systems, 187, 104833. https://doi.org/10.1016/j.knosys.2019.07.004
Tovey, C.A. (1988) Simulated annealing. American Journal of Mathematical and Management Sciences, 8, 389–407.
Venugopal, K., Das S. and Murthy D. V. (2012). Email Marketing: A Paradigm shift to Marketing, International Journal of Advanced Research in Management and Social Sciences, 1(1).
International Journal of Supply and Operations Management
Volume 11, Issue 2
May 2024
Pages 188-202

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