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Big Data: A Panacea for Optimizing Digital Marketing Strategies

Ezugwu Lilian Martina Ozioko Frank Ekene MBA Chioma Juliet

Subject area: Science,Engineering and Technology  ·  Area of research: Computer Science

Abstract

This paper is aimed at investigating the role of big data in digital marketing. The methodology used in carrying out the research is literature review. Several recent papers on the application of big data for digital marketing was presented, considering their techniques, work done, contribution to knowledge, results and research gap was identified. To fill the knowledge gap, a proposed system was presented using customer segmentation techniques and leveraging data from customers to present a conceptual model capable of improving reliability of digital marketing in real world scenario.

Keywords

Big Data, Customer Segmentation, Challenges, Review, Digital Marketing

References

[1] Alsolami, J. F., Saleem, F., and Al-Ghamdi, AM. A. (2020). "Predicting the Accuracy for Telemarketing Process in Banks using Data Mining". JKAU: Comp. IT. Sci., Vol. 9 No. 2, pp: 69 – 83 (1441 A.H. / 2020 A.D.). Doi: 10.4197/Comp. 9-2.4

[2] Anas Nabeel Falih AL-Shawi. Hybrid Datamining Approaches to Predict Success of Bank Telemarketing. International Journal of Computer Science and Mobile Computing, Vol.8 Issue.3, March- 2019, pg. 49-60. ISSN 2320–088X www.ijcsmc.com.

[3] Borugadda, P., Nandru, P., Madhavaiah, P. (2021). "Predicting the Success of Bank Telemarketing for Selling Long-term Deposits: An Application of Machine Learning Algorithms". St. Theresa Journal of Humanities and Social Sciences. Researchgate. https://www.researchgate.net/publication/352755139. Pp. 91-108.

[4] Cedric stephane. K. T., Stefan C. G, Hamza T., Pedro N. M. (2022)." A machine learning framework towards bank telemarketing prediction". journal of risk and financial management 15(6):269. DOI:10.3390/jrfm15060269. https//www.researchgate.net.

[5] ChiX.,GangW.(2022) “Howtoimprovethesuccessofbanktelemarketing? Predictionand interpretability analysis based on machine learning” Computers and Insudtrial Engineering; 1088874

[6] Fakhri Azhar, (2022). Predicting Call Success of Bank Telemarketing Campaign with Machine Learning—a Pythonista with a Passion for Data Analytics, Data Science, QA Automation, and Network DevOps https://www.linkedin.com/in/fakhri-azhar.

[7] Farooqi, R. M., and Iqbal, N. (2019). "Performance Evaluation for Competency of Bank Telemarketing Prediction using Data Mining Techniques". International Journal of Recent Technology and Engineering (IJRTE). ISSN: 2277-3878, Volume-8 Issue-2, July 2019.

[8] Fawaz J. Alsolami, Farrukh Saleemand Abdullah AL-Malaise AL-Ghamdi. Predicting the Accuracy for Telemarketing Process in Banks Using Data Mining. JKAU: Comp. IT. Sci., Vol. 9 No. 2, pp: 69 – 83 (1441 A.H. / 2020 A.D.) Doi: 10.4197/Comp. 9-2.4

[9] Feng, Y., Yin, Y., Wang, D., & Dhamotharan, L. (2022). A dynamic ensemble selection method for bank telemarketing sales prediction. Journal of Business Research, 139(4), 368–382.

[10] Gbenrooluwatobiloba C. (2023)” Modeling of smartdata-Driven-Based decision support system For Enhanced Telemarketing Success In Commercial Banking Using Machine Learning” SOLENTUNIVERSITY; pp.1-68

[11] Gu, J., Na, J., Park, J., & Kim, H. (2020). Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network. Applied Sciences, 11(15), 7147. https://doi.org/10.3390/app11157147.

[12] Gu, J., Na, J., Park, J., and Kim, H. (2021). "Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network". Appl. Sci. 2021, 11, 7147. https://doi.org/10.3390/app11157147

[13] Gu, J.; Na, J.; Park, J.; Kim, H. Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network. Appl. Sci. 2021, 11, 7147. https://doi.org/10.3390/app11157147

[14] HassanSilken, Cedric S.K.T, Walid Cherif. (2018). "Optimizing the prediction of telemarketing by classification technique". Conference Paper. DOI: 10.1109/WINCOM.2018.8629675. https//www.researchgate.net.

[15] Jin, W., and He, Y. (2019). "Three data mining models to predict bank telemarketing". IOP Conf. Series: Materials Science and Engineering 490 (2019) 062075 IOP Publishing. doi:10.1088/1757-899X/490/6/062075

[16] Muneeb A. (2020) “Predicting the success of bank telemarketing using various classification algorithms” Inbook: Degree project, Orebro University.

[17] Muneeb Asif, (2018). Predicting the Success of Bank Telemarketing Using Various Classification Algorithms. Orebro university school of business. https://www.diva-portal.org.

[18] Namulia, M., (2011). “Effect of Selected Marketing Communication Tools on Student Enrolment in Private Universities in Kenya”. European Jornal of Business and Management, Vol 3, No 3 pp. 172-205

[19] Selma, M. 2020. Predicting the Success of Bank Telemarketing Using Artificial Neural Network. International Journal of Economics and Management Engineering 14: 1–4.

[20] Turkmen E. 2021. Deep Learning Based Methods for Processing Data in Telemarketing-Success Prediction. Paper presented at 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, Frbruary 4–6; pp. 1161–66.

[21] Vafeiadis, Thanasis, Konstantinos I. Diamantaras, George Sarigiannidis, and Konstantinos C. Chatzisavvas. 2015. A comparison of machine learning techniques for customer churn prediction. Simulation Modelling Practice and Theory 55: 1–9.

[22] Vongchalerm, L. (2022). "Analysis of predicting the success of the banking telemarketing campaigns by using machine learning techniques". National College of Ireland

[23] Yan, Chun, Meixuan Li, and Liu Wei. 2020. Prediction of bank telephone marketing results based on improved whale algorithms optimizing S_Kohonen network. Applied Soft Computing 92: 106259.

[24] Yiyan Jiang.Using Logistic Regression Model to Predict the Success of Bank Telemarketing. International Journal on Data Science and Technology. Vol. 4, No. 1, 2018, pp. 35-41. doi: 10.11648/j.ijdst.20180401.15

[25] Youngkeun Choi, Jae Choi, (2022). " How Does Machine Learning Predict the Success of Bank Telemarketing?". DOI: https://doi.org/10.21203/rs.3.rs-1695659/v1.

[26] YoungkeunChoi andJae Cho, 2022. How does Machine Learning Predict the Success of Bank Telemarketing? DOI:10.21203/rs.3.rs-1695659/v1.

[27] Yufeng S. (2022)” Bank telemarketing analysis; prediction customers response to future marketing campaigns” Article; Github; available at http:// www.yfsui.github.io.

How to cite this paper

Ezugwu Lilian Martina, Ozioko Frank Ekene, MBA Chioma Juliet "Big Data: A Panacea for Optimizing Digital Marketing Strategies" Iconic Research And Engineering Journals Volume 9 Issue 1 2025 Page 151-158
Ezugwu Lilian Martina, Ozioko Frank Ekene, MBA Chioma Juliet "Big Data: A Panacea for Optimizing Digital Marketing Strategies" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025
Ezugwu Lilian Martina, Ozioko Frank Ekene, MBA Chioma Juliet (2025). Big Data: A Panacea for Optimizing Digital Marketing Strategies. Iconic Research And Engineering Journals, 9(1).
Ezugwu Lilian Martina, Ozioko Frank Ekene, MBA Chioma Juliet "Big Data: A Panacea for Optimizing Digital Marketing Strategies" Iconic Research And Engineering Journals, vol. 9, no. 1, Jul. 2025.
@article{1709531,
      author = {Ezugwu Lilian Martina, Ozioko Frank Ekene, MBA Chioma Juliet},
      title = {Big Data: A Panacea for Optimizing Digital Marketing Strategies},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {1},
      pages = {151-158},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1709531.pdf},
      abstract = {This paper is aimed at investigating the role of big data in digital marketing. The methodology used in carrying out the research is literature review. Several recent papers on the application of big data for digital marketing was presented, considering their techniques, work done, contribution to knowledge, results and research gap was identified. To fill the knowledge gap, a proposed system was presented using customer segmentation techniques and leveraging data from customers to present a conceptual model capable of improving reliability of digital marketing in real world scenario.},
      keywords = {Big Data, Customer Segmentation, Challenges, Review, Digital Marketing},
      month = {July},
  }