Prediction of Customer Engagement Response to E-wallet Content Based on Machine Learning Using Combined E-Wallet Dataset and Individual E-Wallet Dataset
DOI:
https://doi.org/10.22441/jurnal_mix.2024.v14i1.012Keywords:
E-Wallet, Machine Learning, Customer Engagement, MarketingAbstract
Objectives: The COVID pandemic proved that humans are not hesitant to adopt technology in the banking industry, especially as a method of payment. In Indonesia, this fact is demonstrated by the value of e-wallet transactions in 2021 which reached 18,5 billion USD. The implementation of QRIS (Quick Response Code Indonesian Standard) in the payment systems of e-wallets compels every e-wallet provider to intensify their marketing activities to overcome competitors. Previous studies on this topic primarily focused on factors that influence customer engagement on social media using traditional approaches, such as interviews and statistical methods to process the data. This research aims to overcome the limitations of previous research by using machine learning methods in predicting the customer engagement type of primary data collected directly from social media in this case Twitter.
Methodology: In this paper, we propose the application of machine learning methods such as XGBoost, Random Forest, Decision Tree, and KNN to predict the most likely engagement type of a tweet related to e-wallet content using 15.756 data which are directly collected from Twitter.
Finding: This research successfully found that XGBoost and KNN are the machine learning algorithms that perform best and the results in prediction from using the combined dataset and the individual e-wallet brands dataset are similar.
Conclusion: Even though the prediction accuracy in this research is good, this research still has many limitations. Thus, future research in the same field would benefit from a larger amount of data to accommodate machine learning algorithms that are more complex like deep learning.
References
Achmad, A., Saputra, A. I., Ardiansyah, A. F., & Hendriana, E. (2022). The Effectiveness of Social Media Advertisement in The Indonesian Sneakers Industry: Application of The Extended Advertising Value Model. MIX: JURNAL ILMIAH MANAJEMEN, 12(1). https://doi.org/10.22441/jurnal_mix.2022.v12i1.001
Ahmad, A. K., Jafar, A., & Aljoumaa, K. (2019). Customer churn prediction in telecom using machine learning in big data platform. Journal of Big Data, 6(1). https://doi.org/10.1186/s40537-019-0191-6
Ali, M. Z., Shabbir, M. N. S. K., Liang, X., Zhang, Y., & Hu, T. (2019). Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. IEEE Transactions on Industry Applications, 55(3), 2378–2391. https://doi.org/10.1109/TIA.2019.2895797
Ali, N., Neagu, D., & Trundle, P. (2019). Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets. SN Applied Sciences, 1(12). https://doi.org/10.1007/s42452-019-1356-9
Astini, Rina, Ishrat, Kehkashan, Ramli, Yanto, Tafiprios, Chong Kwong, Wing, and Ooi Chee, Keong. Nexus among Crypto Trading, Environmental Degradation, Economic Growth and Energy Usage: Analysis of Top 10 Cryptofriendly Asian Economies. International Journal of Energy Economics and Policy. Volume 13, Issue 5. pp. 339-347. DOI: https://doi.org/10.32479/ijeep.14545
Astini, Rina, Salim, Ansa Savad, Deitiana, Tita, and Ramli, Yanto. (2023). Fintech Growth in Asia: A Shift Towards a Net-Zero Carbon Economy. Przestrzeń Spoleczna (Social Space). Volume 23, No. 3. pp.123-148
Bazi, S., Filieri, R., & Gorton, M. (2020). Customers’ motivation to engage with luxury brands on social media. Journal of Business Research, 112(February), 223–235. https://doi.org/10.1016/j.jbusres.2020.02.032
Chi-Hsien, K., & Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics, 6(4), 404–419. https://doi.org/10.1080/23270012.2019.1692254
Chohan, F., Aras, M., Indra, R., Wicaksono, A., & Winardi, F. (2022). Building Customer Loyalty In Digital Transaction Using QR Code: Quick Response Code Indonesian Standard (QRIS). Journal of Distribution Science, 20(1), 1–11. https://doi.org/10.15722/jds.20.01.202201.1
Christodoulou, E., Ma, J., Collins, G. S., Steyerberg, E. W., Verbakel, J. Y., & Van Calster, B. (2019). A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. Journal of Clinical Epidemiology, 110, 12–22. https://doi.org/10.1016/j.jclinepi.2019.02.004
Dai, Y., & Wang, T. (2021). Prediction of customer engagement behaviour response to marketing posts based on machine learning. Connection Science, 33(4), 891–910. https://doi.org/10.1080/09540091.2021.1912710
Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social media engagement behavior: A framework for engaging customers through social media content. European Journal of Marketing, 53(10), 2213–2243. https://doi.org/10.1108/EJM-03-2017-0182
Grover, P., & Kar, A. K. (2020). User engagement for mobile payment service providers – introducing the social media engagement model. Journal of Retailing and Consumer Services, 53(December), 101718. https://doi.org/10.1016/j.jretconser.2018.12.002
Hartono, P. C., Priyana, C. S., Levia, M., & Simanjuntak, E. R. (2023). Increasing Customer Live Streaming Engagement in Online Shopping Platforms. MIX: JURNAL ILMIAH MANAJEMEN, 13(2). https://doi.org/10.22441/jurnal_mix.2023.v13i2.015
Hartono, Sri, Ramli, Yanto, Astini, Rina, Widayati, Catur, and Ali, Anees Janee. (2024). The Clinical Information System That Effects The Patients' Satisfaction Of The Healthcare Services. Jurnal Manajemen. Volume 28, No. 1. pp. 1-22. DOI: https://doi.org/10.24912/jm.v28i1.1463
Hoang, D., Kousi, S., & Martinez, L. F. (2023). Online customer engagement in the post-pandemic scenario: a hybrid thematic analysis of the luxury fashion industry. Electronic Commerce Research, 23(3), 1401–1428. https://doi.org/10.1007/s10660-022-09635-8
Ibrahem Ahmed Osman, A., Najah Ahmed, A., Chow, M. F.,
Feng Huang, Y., & El-Shafie, A. (2021). Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia. Ain Shams Engineering Journal, 12(2), 1545–1556. https://doi.org/10.1016/j.asej.2020.11.011
Imaningsih, Erna Sofriana, Ramli, Yanto, Widayati, Catur, Hamdan, and Yusliza, Mohd Yusoff. (2023). The Influence of Egoistic Values, Biospheric Values, and Altruistic Values on Green Attitudes for Re-intention to Use Eco-Bag: Studies on Millennial Consumers. Przestrzeń Spoleczna (Social Space). Volume 23, No. 3. pp.123-148. pp. 357-376
Indonesia e-wallet transaction to reach $18.5 billion in 2021 amid fierce competition- The Asian Banker. (n.d.). Retrieved January 29, 2023, from https://www.theasianbanker.com/updates-and-articles/big-tech-platforms-heat-up-competition-in-indonesias-digital-payments-landscape
Khanday, A. M. U. D., Rabani, S. T., Khan, Q. R., Rouf, N., & Mohi Ud Din, M. (2020). Machine learning based approaches for detecting COVID-19 using clinical text data. International Journal of Information Technology (Singapore), 12(3), 731–739. https://doi.org/10.1007/s41870-020-00495-9
Koto, F., & Rahmaningtyas, G. Y. (2018). Inset lexicon: Evaluation of a word list for Indonesian sentiment analysis in microblogs. Proceedings of the 2017 International Conference on Asian Language Processing, IALP 2017, 2018-January, 391–394. https://doi.org/10.1109/IALP.2017.8300625
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(1), 51–70. https://doi.org/10.1007/s11747-020-00733-3
Liu, X., Shin, H., & Burns, A. C. (2021). Examining the impact of luxury brand’s social media marketing on customer engagement: Using big data analytics and natural language processing. Journal of Business Research, 125(April), 815–826. https://doi.org/10.1016/j.jbusres.2019.04.042
Ramli, Yanto, Imaningsih, Erna Sofriana, Rajak, Adnan and Ali, Anees Janee. (2022). Environmental Sustainability: To Enhance Organizational Awareness towards Green Environmental Concern. International Journal of Energy Economics and Policy. Volume 12, 4. pp.307-316. DOI: https://doi.org/10.32479/ijeep.13275
Ramli, Yanto, Kurniawan, Deden, Imaningsih, Erna Sofriana, Yuliantini, Tine, and Anah, Sri. (2022). Imposing Green Management to Enhance the Organizational Awareness against the Environmental Sustainability. International Journal of Energy Economics and Policy. Volume 13, Issue 1. pp. 518-528. DOI: https://doi.org/10.32479/ijeep.14001
Ramli, Yanto and Kartini, Dwi. (2022). Manajemen Strategik dan Bisnis. Bumi Aksara. Jakarta. Indonesia
Rustam, F., Mehmood, A., Ahmad, M., Ullah, S., Khan, D. M., & Choi, G. S. (2020). Classification of Shopify App User Reviews Using Novel Multi Text Features. IEEE Access, 8, 30234–30244. https://doi.org/10.1109/ACCESS.2020.2972632
Ryu, S. E., Shin, D. H., & Chung, K. (2020). Prediction model of dementia risk based on XGBoost using derived variable extraction and hyper parameter optimization. IEEE Access, 8, 177708–177719. https://doi.org/10.1109/ACCESS.2020.3025553
Sashi, C. M. (2012). Customer engagement, buyer-seller relationships, and social media. Management Decision, 50(2), 253–272. https://doi.org/10.1108/00251741211203551
Shiratina, Aldina, Ramli, Yanto and Hanifah, Haniruzila. (2022). SME Innovation and Social-Media on Intention to Visit Ternate City with Destination Image as the Moderating Variable. Jurnal Bisnis dan Manajemen. Volume 23, No. 1. pp. 66-78. DOI:https://doi.org/10.24198/jbm.v23i1.733
Shiratina, Aldina, Ramli, Yanto, Imaningsih, Erna Sofriana, Rajak, Adnan and Ali, Anees Janee. (2023). The Role of Entrepreneurial Marketing and Relationship Marketing that Strengthen the Women Entrepreneurs' Business Performance. Indonesian Journal of Business and Entrepreneurship. Volume 9 No. 2. pp. 177-185. DOI: 10.17358/IJBE.9.2.177
Soelton, M., Ramli, Y., Wahyono, T., Saratian, E. T. P., Oktaviar, C., & Mujadid, M. (2021). The impact of impulse buying on retail markets in Indonesia. The Journal of Asian Finance, Economics and Business, 8(3), 575-584.
Song, Y. Y., & Lu, Y. (2015). Decision tree methods: applications for classification and prediction. Shanghai Archives of Psychiatry, 27(2), 130–135. https://doi.org/10.11919/j.issn.1002-0829.215044
Wardhana, A., Pradana, M., Kartawinata, B. R., Mas-Machuca, M., Pratomo, T. P., & Wasono Mihardjo, L. W. (2022). A Twitter Social Media Analytics Approach on Indonesian Digital Wallet Service. Proceedings - International Conference Advancement in Data Science, E-Learning and Information Systems, ICADEIS 2022, April 2023. https://doi.org/10.1109/ICADEIS56544.2022.10037442
Wardhani, A. K., & Chen, D. K. J. (2021). the Effect of Youtube Media of Online Review, Visualization and Trust on Intention To Buy Smartphone. Mix: Jurnal Ilmiah Manajemen, 11(1), 36. https://doi.org/10.22441/mix.2021.v11i1.003
Widokarti, J. R., Patiro, S. P. S., Tantri, S. N., & Budiyanti, H. (2022). MSMEs and Fintech: A Comparison of Theory of Trying and Theory of Planned Behavior. MIX: JURNAL ILMIAH MANAJEMEN, 12(3). https://doi.org/10.22441/jurnal_mix.2022.v12i3.008
Yang, M., Ren, Y., & Adomavicius, G. (2019). Understanding user-generated content and customer engagement on Facebook business pages. Information Systems Research, 30(3), 839–855. https://doi.org/10.1287/isre.2019.0834
Yuliastuti, I. A. N., Kepramareni, P., Bhegawati, D. A. S., & Purnawati, N. L. G. P. (2022). The Use of E-Money During the Covid-19 Pandemic: Attitudes and Interests of Balinese People. MIX: JURNAL ILMIAH MANAJEMEN, 12(1), 117. https://doi.org/10.22441/jurnal_mix.2022.v12i1.009
Zhang, W., Wu, C., Zhong, H., Li, Y., & Wang, L. (2021). Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization. Geoscience Frontiers, 12(1), 469–477. https://doi.org/10.1016/j.gsf.2020.03.007
Zhu, R., Hu, X., Hou, J., & Li, X. (2021). Application of machine learning techniques for predicting the consequences of construction accidents in China. Process Safety and Environmental Protection, 145, 293–302. https://doi.org/10.1016/j.psep.2020.08.006
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