Analysis of The Most Effective Advertising Platform Selection Decision Support System Using The Analytical Hierarchy Process (AHP) Method
DOI:
https://doi.org/10.64472/jciet.v2i1.20Keywords:
Analytical Hierarchy Process, Advertising Platform, Decision Support System, Meta Ads, Google AdsAbstract
This study aims to determine the most effective digital advertising platform among Google Ads, Meta Ads, and TikTok Ads by applying a Decision Support System (DSS) using the Analytical Hierarchy Process (AHP) method. The research addresses the growing challenge faced by digital marketers in allocating advertising budgets effectively across multiple platforms with different strengths and audience characteristics. Analytical Hierarchy Process (AHP) was selected because of its ability to decompose complex multi-criteria decision-making problems into a structured hierarchical model. The analysis employed three main criteria: Cost, Target Audience, and Conversion. Pairwise comparisons and consistency testing were conducted based on expert judgments to ensure reliable evaluation results. The findings revealed that Conversion was the most influential criterion with the highest Eigen Vector (EV) value of 0.545, followed by Target Audience (0.273) and Cost (0.182). Through Global Synthesis analysis, Meta Ads achieved the highest total EV value of 0.3832, slightly outperforming Google Ads (0.3806), while TikTok Ads ranked third (0.2366). The results indicate that strong audience-targeting capabilities combined with competitive conversion performance make Meta Ads the most effective platform overall. This study provides strategic recommendations for advertisers and digital marketers to optimize advertising budget allocation based on business priorities and campaign objectives based on decision support system.
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[1] I. Ahmadi, J. Habel, M. Jia, and S. Wei, “Consumer stockpiling under the impact of a global disaster: The evolution of affective and cognitive motives,” J. Bus. Res., vol. 142, pp. 56–71, Mar. 2022, doi: https://doi.org/10.1016/j.jbusres.2021.12.042
[2] P. Tarka, M. Kukar-Kinney, and R. J. Harnish, “Consumers’ personality and compulsive buying behavior: The role of hedonistic shopping experiences and gender in mediating-moderating relationships,” Journal of Retailing and Consumer Services, vol. 64, p. 102802, Jan. 2022, doi: https://doi.org/10.1016/j.jretconser.2021.102802.
[3] E. A. Wibowo and S. L. Ratnasari, “OPTIMIZING THE EFFECTIVENESS OF DIGITAL MARKETING CAMPAIGNS WITH THE USE OF E-COMMERCE COMPANY DECISION SUPPORT SYSTEMS,” International Conference of Business and Social Sciences, pp. 284–292, Nov. 2024, doi: https://doi.org/10.24034/icobuss.v4i1.505
[4] M. R. Khan, F. Pervaiz, Ali Raza, A. Latif, and Y. Shang, “A Decision Support System for Assessment of Digital Marketing Platform Selection Using Novel Circular Intuitionistic Fuzzy Dombi Aggregation Operators,” Journal of Innovative Research in Mathematical and Computational Sciences, vol. 4, no. 1, pp. 37–61, Jun. 2025, doi: https://doi.org/10.62270/jirmcs.v4i1.48
[5] R. Hou, X. Ye, H. B. O. Zaki, and N. A. B. Omar, “Marketing Decision Support System Based on Data Mining Technology,” Applied Sciences, vol. 13, no. 7, p. 4315, Mar. 2023, doi: https://doi.org/10.3390/app13074315
[6] A. M. Pereira et al., “Customer models for artificial intelligence-based decision support in fashion online retail supply chains,” Decis. Support Syst., vol. 158, p. 113795, Jul. 2022, doi: https://doi.org/10.1016/j.dss.2022.113795
[7] J. D. Borrero and J. Mariscal, “A Case Study of a Digital Data Platform for the Agricultural Sector: A Valuable Decision Support System for Small Farmers,” Agriculture, vol. 12, no. 6, p. 767, May 2022, doi: https://doi.org/10.3390/agriculture12060767
[8] Z. I. Solodukha and S. V. Leonova, “DIGITAL TOOLS FOR PROMOTING MARKETING AGENCIES IN THE ADVERTISING SERVICES MARKET: A COMPARATIVE ANALYSIS OF GOOGLE ADS AND META APPROACHES,” The actual problems of regional economy development, vol. 1, no. 21, pp. 288–296, Jun. 2025, doi: https://doi.org/10.15330/apred.1.21.288-296
[9] A. Gabay, A. Solomon, I. Guy, and B. Shapira, “Creating an Intelligent Social Media Campaign Decision-Support Method,” in Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization, New York, NY, USA: ACM, Jun. 2024, pp. 149–158. doi: https://doi.org/10.1145/3627043.3659543
[10] A. E. Youssef and K. Saleem, “A Hybrid MCDM Approach for Evaluating Web-Based E-Learning Platforms,” IEEE Access, vol. 11, pp. 72436–72447, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3294798
[11] N. Dadashzadeh, S. Sucu, K. Pangbourne, and D. Ouelhadj, “Socially Sustainable Mobility as a Service (MaaS): A practical MCDM framework to evaluate accessibility and inclusivity with application,” Cities, vol. 154, p. 105360, Nov. 2024, doi: https://doi.org/10.1016/j.cities.2024.105360
[12] I. Mutambik, “Assessing Critical Success Factors for Supply Chain 4.0 Implementation Using a Hybrid MCDM Framework,” Systems, vol. 13, no. 6, p. 489, Jun. 2025, doi: https://doi.org/10.3390/systems13060489
[13] P. Bajdor, “MCDM Approach to Quality Assessment of Functioning of e-Commerce Platforms Operating in Poland,” 2025, pp. 213–223. doi: https://doi.org/10.1007/978-3-031-78465-1_18
[14] Noviyanti, A. Deli, and L. Gloria, “Decision Support System for Village Head Election Using the Weighted Product Method: Case Study in Lumar Village,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 1, pp. 1–6, Jul. 2025, doi: https://doi.org/10.64472/jciet.v1i1.1
[15] L. Pu and Z. Quan, “A CIMS-based decision support system for e-commerce: boosting real-time and effective digital marketing,” J. Supercomput., vol. 81, no. 1, p. 229, Jan. 2025, doi: https://doi.org/10.1007/s11227-024-06730-5
[16] M. H. Shahidzadeh and S. Shokouhyar, “Unveiling just-in-time decision support system using social media analytics: a case study on reverse logistics resource recycling,” Industrial Management & Data Systems, vol. 124, no. 6, pp. 2251–2283, Jun. 2024, doi: https://doi.org/10.1108/IMDS-12-2023-0921
[17] D. A. Putri and W. D. Prastowo, “Analysis of A Decision Support System for the Selection of the Best Coffee Supplier at Arion Coffee Using AHP,” Journal of Computing Innovations and Emerging Technologies, vol. 1, no. 2, pp. 51–58, Dec. 2025, doi: https://doi.org/10.64472/jciet.v1i2.18
[18] M. Mundzir, R. Zulkarnain, and R. Hardi, “Employing Fuzzy AHP in Modeling a Decision Support System for Determining Scholarship Recipients within the University Context,” Jurnal Malikussaleh Mengabdi, vol. 2, no. 2, p. 344, Oct. 2023, doi: https://doi.org/10.29103/jmm.v2i2.13344
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