Cilt 13 Sayı 2 (2025): Business & Management Studies: An International Journal
Makaleler

Dijital pazarlama kampanyalarının yapay zekâ ve makine öğrenmesi ile evrimi: Başarı tahmin yeteneklerinin analizi

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  • Erkan Gülter,
  • Muhammed Fatih Cevher
Erkan Gülter
Dr. Öğretim Üyesi, Fırat Üniversitesi, Elazığ, Türkiye
Biyografi
Muhammed Fatih Cevher
Dr. Öğretim Üyesi, Munzur Üniversitesi, Tunceli, Türkiye
Biyografi

Yayınlanmış 25.06.2025

Anahtar Kelimeler

  • Digital Marketing, Machine Learning, Marketing Research, Campaign Success Prediction, Classification Models
  • Dijital Pazarlama, Makine Öğrenme, Pazarlama Araştırmaları, Kampanya Başarı Tahmini, Sınıflandırma Modelleri

Nasıl Atıf Yapılır

Dijital pazarlama kampanyalarının yapay zekâ ve makine öğrenmesi ile evrimi: Başarı tahmin yeteneklerinin analizi. (2025). Business & Management Studies: An International Journal, 13(2), 478-493. https://doi.org/10.15295/bmij.v13i2.2498

Telif Hakkı (c) 2025 Erkan Gülter- Muhammed Fatih Cevher

Creative Commons License

Bu çalışma Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanslanmıştır.

Nasıl Atıf Yapılır

Dijital pazarlama kampanyalarının yapay zekâ ve makine öğrenmesi ile evrimi: Başarı tahmin yeteneklerinin analizi. (2025). Business & Management Studies: An International Journal, 13(2), 478-493. https://doi.org/10.15295/bmij.v13i2.2498

Öz

Bu çalışmanın amacı, dijital pazarlama kampanyalarında makine öğrenmesi algoritmalarının söz konusu kampanyaların başarısını tahmin etme yeteneğini ortaya çıkarmaktır. Ayrıca çalışma, makine öğrenme algoritması sınıflandırma modellerinin performansını ölçerek hangi sınıflandırma modelinin bu tahmini gerçekleştirmede daha başarılı olduğunu ortaya koymayı amaçlamaktadır. Bu amaç doğrultusunda çalışmada Kaggle platformundan elde edilen 10.001 adet dijital pazarlama kampanyasının yer aldığı bir veri seti kullanılarak makine öğrenmesi algoritmaları ile bir sınıflandırma analizi gerçekleştirilmiştir. Çalışmanın teorik altyapısı Atıf Modellemesi, Teknoloji Kabul Modeli, Artımsal Tepki Modellemesi ve Yeniliklerin Yayılımı Teorisine dayanmaktadır. Analizler sonucunda, makine öğrenme algoritmalarının pazarlama kampanyalarının başarı tahmin yeteneklerinin oldukça yüksek olduğu ortaya çıkmıştır. Makine öğrenme modellerinin başarı tahmin yeteneklerinin karşılaştırıldığı çalışmada en yüksek başarı tahmin yeteneğinin Gradient Boosting modeline ait olduğu (%93,31), en düşük başarı tahmin yeteneğinin ise Logistic Regression modeline (%53,36 doğruluk) ait olduğu ortaya çıkmıştır. Çalışma bulguları, makine öğrenme algoritmalarının pazarlama literatüründe daha fazla yer alması gerekliliğini ortaya koymuştur. Ayrıca işletmelerin de pazarlama kampanyalarında makine öğrenme modellerini kullanarak daha başarılı ve verimli kampanyalar yürütebilecekleri bu çalışma sonuçlarında ortaya çıkmıştır.

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Referanslar

  1. Abd, N. S., Atiyah, O. S., Ahmed, M. T., & Bakhit, A. (2024). Digital Marketing Data Classification by Using Machine Learning Algorithms. Iraqi Journal for Electrical and Electronic Engineering, 20(1). doi: 10.37917/ijeee.20.1.23
  2. Achyutha, P. N., Chaudhury, S., Bose, S. C., Kler, R., Surve, J., & Kaliyaperumal, K. (2022). User Classification and Stock Market‐Based Recommendation Engine Based on Machine Learning and Twitter Analysis. Mathematical Problems in Engineering, 2022(1), 4644855. https://doi.org/10.1155/2022/4644855
  3. Afolabi, I., Ifunaya, T. C., Ojo, F. G., & Moses, C. (2019, August). A model for business success prediction using machine learning algorithms. In Journal of Physics: Conference Series 1299 (1) p. 012050). IOP Publishing. doi: 10.1088/1742-6596/1299/1/012050
  4. Ansari, A., Li, Y., & Zhang, J. Z. (2018). Probabilistic topic model for hybrid recommender systems: A stochastic variational Bayesian approach. Marketing Science, 37(6), 987-1008. https://doi.org/10.1287/mksc.2018.1113
  5. Artrith, N., Butler, K. T., Coudert, F. X., Han, S., Isayev, O., Jain, A., & Walsh, A. (2021). Best practices in machine learning for chemistry. Nature chemistry, 13(6), 505-508. Access address: https://www.nature.com/articles/s41557-021-00716-z
  6. Bayoude, K., Ouassit, Y., Ardchir, S., & Azouazi, M. (2018). How machine learning potentials are transforming the practice of digital marketing: State of the art. Periodicals of Engineering and Natural Sciences, 6(2), 373-379. Access address: http://pen.ius.edu.ba
  7. Blomster, M., Koivumäki, T. (2022). Exploring the resources, competencies, and capabilities needed for successful machine learning projects in digital marketing. Inf Syst E-Bus Manage 20, 123–169 (2022). https://doi.org/10.1007/s10257-021-00547-y
  8. Boddu, R. S. K., Santoki, A. A., Khurana, S., Koli, P. V., Rai, R., & Agrawal, A. (2022). An analysis to understand the role of machine learning, robotics and artificial intelligence in digital marketing. Materials Today: Proceedings, 56, 2288-2292. https://doi.org/10.1016/j.matpr.2021.11.637
  9. Cambria, E., Grassi, M., Hussain, A., & Havasi, C. (2012). Sentic computing for social media marketing. Multimedia tools and applications, 59, 557-577. https://doi.org/10.1007/s11042-011-0815-0
  10. Chakraborty, I., Kim, M., & Sudhir, K. (2019). Attribute sentiment scoring with online text reviews: Accounting for language structure and attribute self-selection. Cowles Foundation Discussion. Access address: https://ssrn.com/abstract=3395012
  11. Chen, Q., Cai, S., & Gu, X. (2021). Construction of the Luxury Marketing Model Based on Machine Learning Classification Algorithm. Scientific Programming, 2021(1), 6511552. https://doi.org/10.1155/2021/6511552
  12. Chiong, K. X., & Shum, M. (2019). Random projection estimation of discrete-choice models with large choice sets. Management Science, 65(1), 256-271. https://doi.org/10.1287/mnsc.2017.2928
  13. Cui, D., & Curry, D. (2005). Prediction in marketing using the support vector machine. Marketing Science, 24(4), 595-615. https://doi.org/10.1287/mksc.1050.0123
  14. Cui, G., Wong, M. L., & Lui, H. K. (2006). Machine learning for direct marketing response models: Bayesian networks with evolutionary programming. Management Science, 52(4), 597-612. https://doi.org/10.1287/mnsc.1060.0514
  15. Das, T. K. (2015, October). A customer classification prediction model based on machine learning techniques. In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT) (pp. 321-326). IEEE. doi: 10.1109/ICATCCT.2015.7456903
  16. Davis, F. D. (1989). Technology acceptance model: TAM. Al-Suqri, MN, Al-Aufi, AS: Information Seeking Behaviour and Technology Adoption, 205(219), 5. Access address: efaidnbmnnnibpcajpcglclefindmkaj/https://quod.lib.umich.edu/b/busadwp/images/b/1/4/b1409190.0001.001.pdf
  17. Dhir, R., & Raj, A. (2018, December). Movie success prediction using machine learning algorithms and their comparison. In 2018 first international conference on secure cyber computing and communication (ICSCCC) (pp. 385-390). IEEE. doi: 10.1109/ICSCCC.2018.8703320
  18. Duarte, V., Zuniga-Jara, S., & Contreras, S. (2022). Machine learning and marketing: A systematic literature review. IEEE Access, 10, 93273-93288. doi: 10.1109/ACCESS.2022.3202896
  19. Guo, T., Sriram, S., & Manchanda, P. (2021). The effect of information disclosure on industry payments to physicians. Journal of Marketing Research, 58(1), 115-140. https://doi.org/10.1177/0022243720972106
  20. Hagen, L., Uetake, K., Yang, N., Bollinger, B., Chaney, A. J., Dzyabura, D., ... & Zhu, Y. (2020). How can machine learning aid behavioural marketing research?. Marketing Letters, 31, 361-370. https://doi.org/10.1007/s11002-020-09535-7
  21. Hair Jr, J. F., & Sarstedt, M. (2021). Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. Journal of Marketing Theory and Practice, 29(1), 65-77. https://doi.org/10.1080/10696679.2020.1860683
  22. Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M., & Levy, D. J. (2021). Machines learn neuromarketing: Improving preference prediction from self-reports using multiple EEG measures and machine learning. International Journal of Research in Marketing, 38(3), 770-791. https://doi.org/10.1016/j.ijresmar.2020.10.005
  23. Hartmann, J., Heitmann, M., Schamp, C., & Netzer, O. (2019). The power of brand selfies in consumer-generated brand images. Columbia Business School Research Paper, 1-57. Access address: https://ssrn.com/abstract=3354415
  24. Herhausen, D., Bernritter, S. F., Ngai, E. W., Kumar, A., & Delen, D. (2024). Machine learning in marketing: Recent progress and future research directions. Journal of Business Research, 170, 114254. https://doi.org/10.1016/j.jbusres.2023.114254
  25. Huang, D., & Luo, L. (2016). Consumer preference elicitation of complex products using fuzzy support vector machine active learning. Marketing Science, 35(3), 445-464. https://doi.org/10.1287/mksc.2015.0946
  26. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the academy of marketing science, 49, 30-50. https://doi.org/10.1007/s11747-020-00749-9
  27. Kakatkar, C., & Spann, M. (2019). Marketing analytics using anonymised and fragmented tracking data. International Journal of Research in Marketing, 36(1), 117-136. https://doi.org/10.1016/j.ijresmar.2018.10.001
  28. Kaponis, A., & Maragoudakis, M. (2022). Data Analysis in Digital Marketing using Machine learning and Artificial Intelligence Techniques, Ethical and Legal Dimensions, State of the Art. In Proceedings of the 12th Hellenic Conference on Artificial Intelligence (pp. 1-9). https://doi.org/10.1145/3549737.3549756
  29. Kawaf, F. (2019). Capturing digital experience: The method of screencast videography. International Journal of Research in Marketing, 36(2), 169-184. https://doi.org/10.1016/j.ijresmar.2018.11.002
  30. Kim, J., Kim, H., & Geum, Y. (2023). How to succeed in the market? Predicting startup success using a machine learning approach. Technological Forecasting and Social Change, 193, 122614. https://doi.org/10.1016/j.techfore.2023.122614
  31. Kumar, N. S. T., & Reddy, D. S. (2021). Bank marketing data classification using machine learning algorithms. Central Asian Journal of Mathematical Theory And Computer Sciences, 2(9), 31-36. Access address: https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/101
  32. Kumar, T. S. (2020). Data mining-based marketing decision support system using a hybrid machine learning algorithm. Journal of Artificial Intelligence, 2(03), 185-193. https://doi.org/10.36548/jaicn.2020.3.006
  33. Lahbabi, Y., Raki, S., Chakir Lamrani, H., & Dehbi, S. (2021). Machine learning in digital marketing. MENACIS2021. 28. Access address: https://aisel.aisnet.org/menacis2021/28
  34. Lee, K., Park, J., Kim, I., & Choi, Y. (2018). Predicting movie success with machine learning techniques: ways to improve accuracy. Information Systems Frontiers, 20, 577-588. https://doi.org/10.1007/s10796-016-9689-z
  35. Li, X., Shi, M., & Wang, X. S. (2019). Video mining: Measuring visual information using automatic methods. International Journal of Research in Marketing, 36(2), 216-231. https://doi.org/10.1016/j.ijresmar.2019.02.004
  36. Liakos, K. G., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674. https://doi.org/10.3390/s18082674
  37. Lin, R. (2024, May). Brand Digital Marketing Based on Machine Learning Classification Algorithm. In 2024 5th International Conference for Emerging Technology (INCET) (pp. 1-5). IEEE. doi: 10.1109/INCET61516.2024.10593648
  38. Liu, J., & Toubia, O. (2018). A semantic approach for estimating consumer content preferences from online search queries. Marketing Science, 37(6), 930-952. https://doi.org/10.1287/mksc.2018.1112
  39. Liu, X., Lee, D., & Srinivasan, K. (2019). Large-scale cross-category analysis of consumer review content on sales conversion leveraging deep learning. Journal of Marketing Research, 56(6), 918-943. https://doi.org/10.1177/0022243719866690
  40. Liu, Y., & Yang, S. (2022). Application of Decision Tree‐Based Classification Algorithm on Content Marketing. Journal of Mathematics, 2022(1), 6469054. https://doi.org/10.1155/2022/6469054
  41. Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005
  42. Ma, L., Sun, B., & Zhang, K. (2019). Image network and interest group– A heterogeneous network embedding approach to analyse social curation on Pinterest. working paper. Access address: https://www-2.rotman.utoronto.ca/userfiles/seminars/marketing/files/PinterestNetworkEmbedding_MaSunZhang_2019.pdf
  43. Malik, N., Singh, P. V., & Srinivasan, K. (2019). A dynamic analysis of beauty premium. Available at SSRN 3208162. Access address: https://ssrn.com/abstract=3208162
  44. Miklosik, A., & Evans, N. (2020). Impact of big data and machine learning on digital transformation in marketing: A literature review. Ieee. Access, 8, 101284-101292. doi: 10.1109/ACCESS.2020.2998754
  45. Miklosik, A., Kuchta, M., Evans, N., & Zak, S. (2019). Towards the adoption of machine learning-based analytical tools in digital marketing. Ieee Access, 7, 85705-85718. Access address: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8746184
  46. Min, S., So, K. K. F., & Jeong, M. (2021). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. In Future of tourism marketing (pp. 2-15). Routledge. Access address: https://www.cabidigitallibrary.org/doi/full/10.5555/20193387047
  47. Misra, K., Schwartz, E. M., & Abernethy, J. (2019). Dynamic online pricing with incomplete information using multiarmed bandit experiments. Marketing Science, 38(2), 226-252. https://doi.org/10.1287/mksc.2018.1129
  48. Misra, M., Yadav, A. P., & Kaur, H. (2018). Stock market prediction using machine learning algorithms: a classification study. In 2018 International Conference on Recent Innovations in Electrical, Electronics & Communication Engineering (ICRIEECE) (pp. 2475-2478). IEEE. doi: 10.1109/ICRIEECE44171.2018.9009178
  49. Modak, C., Ghosh, S. K., Sarkar, M. A. I., Sharif, M. K., Arif, M., Bhuiyan, M., ... & Devi, S. (2024). Machine Learning Model in Digital Marketing Strategies for Customer Behaviour: Harnessing CNNs for Enhanced Customer Satisfaction and Strategic Decision-Making. Journal of Economics, Finance and Accounting Studies, 6(3), 178-186. https://doi.org/10.32996/jefas.2024.6.3.14
  50. Nagaraj, P., Nani, K., Krishna, E. T., Reddy, K. A. K., Sekar, R. R., & Rajkumar, T. D. (2023, December). Customer Sale Analysis and Classification Using Machine Learning Algorithm. In 2023 International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) (pp. 1-5). IEEE. doi: 10.1109/ICDSAAI59313.2023.10452665
  51. Ngai, E. W., & Wu, Y. (2022). Machine learning in marketing: A literature review, conceptual framework, and research agenda. Journal of Business Research, 145, 35-48. https://doi.org/10.1016/j.jbusres.2022.02.049
  52. Nikolajeva, A., & Teilans, A. (2021). Machine Learning Technology Overview In Terms Of Digital Marketing And Personalization. ECMS, 125-130. Access address: https://www.researchgate.net/profile/Artis-Teilans/publication/352032253_Machine_Learning_Technology_Overview_In_Terms_Of_Digital_Marketing_And_Personalization/links/65963c5b2468df72d3f96319/Machine-Learning-Technology-Overview-In-Terms-Of-Digital-Marketing-And-Personalization.pdf
  53. Nisar, T. M., & Yeung, M. (2018). Attribution modeling in digital advertising: An empirical investigation of the impact of digital sales channels. Journal of Advertising Research, 58(4), 399-413. https://doi.org/10.2501/JAR-2017-055
  54. Pan, C., Gao, Y., & Luo, Y. (2018). Machine learning prediction of companies' business success. CS229: Machine Learning, Fall, 35. Access address: https://cs229.stanford.edu/proj2018/report/88.pdf
  55. Panarese, A., Settanni, G., Vitti, V., & Galiano, A. (2022). Developing and preliminary testing of a machine learning-based platform for sales forecasting using a gradient boosting approach. Applied Sciences, 12(21), 11054. https://doi.org/10.3390/app122111054
  56. Pawłowski, M. (2022). Machine learning based product classification for ecommerce. Journal of Computer Information Systems, 62(4), 730-739. https://doi.org/10.1080/08874417.2021.1910880
  57. Rafieian, O., & Yoganarasimhan, H. (2021). Targeting and privacy in mobile advertising. Marketing Science, 40(2), 193-218. https://doi.org/10.1287/mksc.2020.1235
  58. Saba, N. S., Gandhi, R., Rajendran, S. R., & Abraham, N. D. (2023). Revolutionising digital marketing using machine learning. In Contemporary Approaches of Digital Marketing and the Role of Machine Intelligence (pp. 1-22). IGI Global. doi: 10.4018/978-1-6684-7735-9.ch001
  59. Salminen, J., V. Yoganathan, J. Corporan, B.J. Jansen, and S.G. Jung. (2019). Machine learning approach to auto-tagging online content for content marketing efficiency: A comparative analysis between methods and content type. Journal of Business Research 101: 203–217. https://doi.org/10.1016/j.jbusres.2019.04.018
  60. Sanisoglu, M., Burnaz, S., & Kaya, T. (2024). A gateway toward truly responsive customers: using the uplift modeling to increase the performance of a B2B marketing campaign. Journal of Marketing Analytics, 12(4), 909-924. https://doi.org/10.1057/s41270-023-00254-2
  61. Sharma, A., Poojitha, S., Saxena, A., Bhanushali, M. M., & Rawal, P. (2022). A conceptual analysis of machine learning towards digital marketing transformation. In 2022 5th International Conference on Contemporary Computing and Informatics (IC3I) (pp. 313-316). IEEE. doi: 10.1109/IC3I56241.2022.10073416.
  62. Sharma, A., Srinivasulu, A., Barua, T., & Tiwari, A. (2021). Classification of Digital Marketing Targeted Data Using Machine Learning Techniques. In 2021 IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) (pp. 1-6). IEEE. doi: 10.1109/TRIBES52498.2021.9751646
  63. Türk, A. (2023). Digital leadership role in developing business strategy suitable for digital transformation. Frontiers in psychology, 13, 1066180. https://doi.org/10.3389/fpsyg.2022.1066180
  64. Ullal, M. S., Hawaldar, I. T., Soni, R., & Nadeem, M. (2021). The Role of Machine Learning in Digital Marketing. SAGE Open, 11(4). https://doi.org/10.1177/21582440211050394
  65. Ullal, M. S., Hawaldar, I. T., Soni, R., & Nadeem, M. (2021). The role of machine learning in digital marketing. Sage Open, 11(4), 21582440211050394. https://doi.org/10.1177/215824402110503
  66. Van Giffen, B., Herhausen, D., & Fahse, T. (2022). Overcoming the pitfalls and perils of algorithms: A classification of machine learning biases and mitigation methods. Journal of Business Research, 144, 93-106. https://doi.org/10.1016/j.jbusres.2022.01.076
  67. Yang, Y., Zhang, K., & Kannan, P. K. (2022). Identifying market structure: A deep network representation learning of social engagement. Journal of Marketing, 86(4), 37-56. https://doi.org/10.1177/00222429211033585
  68. Zaki, A. M., Khodadadi, N., Hong Lim, W., & Towfek, S. K. (2024). Predictive Analytics and Machine Learning in Direct Marketing for Anticipating Bank Term Deposit Subscriptions. American Journal of Business & Operations Research, 11(1). doi: https://doi.org/10.54216/AJBOR.110110
  69. Zhang, K., & Luo, X. (2019). Leveraging Deep-learning and Field Experiment Response Heterogeneity to Enhance Customer Targeting Effectiveness. ICIS 2019 Proceedings. 28. Access adress: https://aisel.aisnet.org/icis2019/data_science/data_science/28
  70. Zhang, M. (2022). Research on precision marketing based on consumer portrait from the perspective of machine learning. Wireless Communications and Mobile Computing, 2022(1), 9408690. https://doi.org/10.1155/2022/9408690
  71. Zhang, M., & Luo, L. (2023). Can consumer-posted photos serve as a leading indicator of restaurant survival? Evidence from Yelp. Management Science, 69(1), 25-50. Access address: https://ssrn.com/abstract=3108288
  72. Zhao, Y., Yu, Y., Li, Y., Han, G., & Du, X. (2019). Machine learning based privacy-preserving fair data trading in big data market. Information Sciences, 478, 449-460. https://doi.org/10.1016/j.ins.2018.11.028