Analysis of E-Commerce Purchase Patterns Using Big Data: An Integrative Approach to Understanding Consumer Behavior
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Dec 10, 2023
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E-Commerce, Consumer Behavior, Big Data Analysis, Digital Transformation, COVID-19 Impact
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Abstract
This research undertakes a meticulous examination of the Indonesian e-commerce industry, aiming to unravel the intricate patterns governing consumer behavior within this rapidly evolving digital landscape. Employing an extensive dataset and cutting-edge data analysis methodologies, this study discerns pivotal trends that have engendered transformative shifts in Indonesia's e-commerce sector. A conspicuous trend uncovered is the escalating reliance on instant messaging platforms and social media conduits for e-commerce transactions. This pronounced transition underscores the remarkable adaptability of businesses to the digital milieu, thereby accentuating the significance of a digitally oriented business paradigm. Furthermore, this research brings to light the prevailing predilection among non-formal e-commerce enterprises, whose revenues predominantly dwell below the IDR 300 million threshold. Notably, the Cash on Delivery (COD) method remains the preeminent payment mechanism. These observations illuminate the structural underpinnings of the market and consumer payment proclivities, thereby exerting a discernible influence on pricing strategies and payment processing mechanisms adopted by enterprises. Moreover, the study delves into the transformative effects of the COVID-19 pandemic, which have expedited the digital metamorphosis of both consumers and e-commerce enterprises. This acceleration has ushered in a new epoch characterized by novel opportunities and concomitant challenges within the e-commerce domain. In summation, this research furnishes a multidimensional and academically rigorous perspective on the Indonesian e-commerce landscape, furnishing actionable insights indispensable for businesses and policymakers alike. The comprehension of these evolving trends is indispensable for strategic formulation and policy calibration, enabling adept navigation of the dynamic e-commerce milieu.
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Caroline, Yuswardi, & Rofi’i, Y. U. (2023). Analysis of E-Commerce Purchase Patterns Using Big Data: An Integrative Approach to Understanding Consumer Behavior. International Journal Software Engineering and Computer Science (IJSECS), 3(3), 352–364. https://doi.org/10.35870/ijsecs.v3i3.1840
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References
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Li, L. and Zhang, J., 2021. Research and analysis of an enterprise E-commerce marketing system under the big data environment. Journal of Organizational and End User Computing (JOEUC), 33(6), pp.1-19.
Chaudhary, H., 2020. Analyzing the paradigm shift of consumer behavior towards E-Commerce during pandemic lockdown. Available at SSRN 3664668. DOI: https://dx.doi.org/10.2139/ssrn.3664668.
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Kamthania, D., Pawa, A. and Madhavan, S.S., 2018. Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business. Journal of computing and information technology, 26(1), pp.57-68. DOI: https://doi.org/10.20532/cit.2018.1003863.
Delvisa, E., 2023. Pengaruh E-Commerce, Pengetahuan Kewirausahaan dan Sistem Informasi Akuntansi pada Minat Berwirausaha. Jurnal EMT KITA, 7(3), pp.630-636. DOI: https://doi.org/10.35870/emt.v7i3.1170.
Wattimena, F.Y. and Rofi'i, Y.U., 2023. E-Commerce Product Recommendation System Using Case-Based Reasoning (CBR) and K-Means Clustering. International Journal Software Engineering and Computer Science (IJSECS), 3(2), pp.162-173. DOI: https://doi.org/10.35870/ijsecs.v3i2.1527.
Favaretto, M., De Clercq, E., Schneble, C.O. and Elger, B.S., 2020. What is your definition of Big Data? Researchers’ understanding of the phenomenon of the decade. PloS one, 15(2), p.e0228987. DOI: https://doi.org/10.1371/journal.pone.0228987.
Sivarajah, U., Kamal, M.M., Irani, Z. and Weerakkody, V., 2017. Critical analysis of Big Data challenges and analytical methods. Journal of business research, 70, pp.263-286. DOI: https://doi.org/10.1016/j.jbusres.2016.08.001.
Akter, S. and Wamba, S.F., 2016. Big data analytics in E-commerce: a systematic review and agenda for future research. Electronic Markets, 26, pp.173-194. DOI: https://doi.org/10.1007/s12525-016-0219-0.
Le, T.M. and Liaw, S.Y., 2017. Effects of pros and cons of applying big data analytics to consumers’ responses in an e-commerce context. Sustainability, 9(5), p.798. DOI: https://doi.org/10.3390/su9050798.
Zineb, E.F., Najat, R.A.F.A.L.I.A. and Jaafar, A.B.O.U.C.H.A.B.A.K.A., 2021. An intelligent approach for data analysis and decision making in big data: a case study on e-commerce industry. International Journal of Advanced Computer Science and Applications, 12(7).
Song, Z., Sun, Y., Wan, J., Huang, L. and Zhu, J., 2019. Smart e-commerce systems: current status and research challenges. Electronic Markets, 29, pp.221-238. DOI: https://doi.org/10.1007/s12525-017-0272-.
Alrumiah, S.S. and Hadwan, M., 2021. Implementing big data analytics in e-commerce: Vendor and customer view. Ieee Access, 9, pp.37281-37286.DOI: https://doi.org/10.1109/ACCESS.2021.3063615.
Anshari, M., Almunawar, M.N., Lim, S.A. and Al-Mudimigh, A., 2019. Customer relationship management and big data enabled: Personalization & customization of services. Applied Computing and Informatics, 15(2), pp.94-101.DOI: https://doi.org/10.1016/j.aci.2018.05.004.
Rane, N., 2023. Enhancing Customer Loyalty through Artificial Intelligence (AI), Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience. Internet of Things (IoT), and Big Data Technologies: Improving Customer Satisfaction, Engagement, Relationship, and Experience (October 13, 2023). DOI: http://dx.doi.org/10.2139/ssrn.4616051.
Praveen, K.B., Kumar, P., Prateek, J., Pragathi, G. and Madhuri, J., 2020. Inventory management using machine learning. International Journal of Engineering Research & Technology (IJERT), 9(06), pp.866-869.
Kietzmann, J., Paschen, J. and Treen, E., 2018. Artificial intelligence in advertising: How marketers can leverage artificial intelligence along the consumer journey. Journal of Advertising Research, 58(3), pp.263-267. DOI: https://doi.org/10.2501/JAR-2018-035
Nica, E., Sabie, O.M., Mascu, S. and Luţan, A.G., 2022. Artificial intelligence decision-making in shopping patterns: Consumer values, cognition, and attitudes. Economics, Management and Financial Markets, 17(1), pp.31-43.
Nazari, E., Shahriari, M.H. and Tabesh, H., 2019. BigData analysis in healthcare: apache hadoop, apache spark and apache flink. Frontiers in Health Informatics, 8(1), p.14. DOI: https://doi.org/10.30699/fhi.v8i1.180.
Borthakur, D., Gray, J., Sarma, J.S., Muthukkaruppan, K., Spiegelberg, N., Kuang, H., Ranganathan, K., Molkov, D., Menon, A., Rash, S. and Schmidt, R., 2011, June. Apache hadoop goes realtime at facebook. In Proceedings of the 2011 ACM SIGMOD International Conference on Management of data (pp. 1071-1080). DOI: https://doi.org/10.1145/1989323.1989438.
Ahmed, N., Barczak, A.L., Susnjak, T. and Rashid, M.A., 2020. A comprehensive performance analysis of Apache Hadoop and Apache Spark for large scale data sets using HiBench. Journal of Big Data, 7(1), pp.1-18. DOI: https://doi.org/10.1186/s40537-020-00388-5.
Moniruzzaman, A.B.M. and Hossain, S.A., 2013. Nosql database: New era of databases for big data analytics-classification, characteristics and comparison. arXiv preprint arXiv:1307.0191. DOI: https://doi.org/10.48550/arXiv.1307.0191.
Oussous, A., Benjelloun, F.Z., Lahcen, A.A. and Belfkih, S., 2017. NoSQL databases for big data. International Journal of Big Data Intelligence, 4(3), pp.171-185. DOI: https://doi.org/10.1504/IJBDI.2017.085537.
Kausar, M.A. and Nasar, M., 2021. SQL versus NoSQL databases to assess their appropriateness for big data application. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 14(4), pp.1098-1108. DOI: https://doi.org/10.2174/2213275912666191028111632.
Embarak, D.O. and Embarak, O., 2018. The importance of data visualization in business intelligence. Data analysis and visualization using python: analyze data to create visualizations for BI systems, pp.85-124.
Embarak, D.O., Embarak and Karkal, 2018. Data analysis and visualization using python. Berkeley, CA, USA: Apress.
Grant, R., 2018. Data visualization: Charts, maps, and interactive graphics. Crc Press.
Yim, A., Chung, C. and Yu, A., 2018. Matplotlib for Python Developers: Effective techniques for data visualization with Python. Packt Publishing Ltd.
Behl, A., Dutta, P., Lessmann, S., Dwivedi, Y.K. and Kar, S., 2019. A conceptual framework for the adoption of big data analytics by e-commerce startups: a case-based approach. Information systems and e-business management, 17, pp.285-318. DOI: https://doi.org/10.1007/s10257-019-00452-5.
Li, L. and Zhang, J., 2021. Research and analysis of an enterprise E-commerce marketing system under the big data environment. Journal of Organizational and End User Computing (JOEUC), 33(6), pp.1-19.
Chaudhary, H., 2020. Analyzing the paradigm shift of consumer behavior towards E-Commerce during pandemic lockdown. Available at SSRN 3664668. DOI: https://dx.doi.org/10.2139/ssrn.3664668.
Rita, P. and Ramos, R.F., 2022. Global research trends in consumer behavior and sustainability in E-Commerce: A bibliometric analysis of the knowledge structure. Sustainability, 14(15), p.9455. DOI: https://doi.org/10.3390/su14159455.
Policarpo, L.M., da Silveira, D.E., da Rosa Righi, R., Stoffel, R.A., da Costa, C.A., Barbosa, J.L.V., Scorsatto, R. and Arcot, T., 2021. Machine learning through the lens of e-commerce initiatives: An up-to-date systematic literature review. Computer Science Review, 41, p.100414. DOI: https://doi.org/10.1016/j.cosrev.2021.100414.
Shen, Y., 2020, November. Application of Big Data technology in e-commerce. In Journal of Physics: Conference Series (Vol. 1682, No. 1, p. 012075). IOP Publishing.
Wu, C., Li, H., Ren, J., Marimuthu, K. and Kumar, P.M., 2021. Artificial neural network based high dimensional data visualization technique for interactive data exploration in E-commerce. Annals of Operations Research, pp.1-19. DOI: https://doi.org/10.1007/s10479-021-04436-y.
Abbas, W.F., Ahmad, N.D. and Zaini, N.B., 2013, December. Discovering purchasing pattern of sport items using market basket analysis. In 2013 International Conference on Advanced Computer Science Applications and Technologies (pp. 120-125). IEEE. DOI: https://doi.org/10.1109/ACSAT.2013.31.
Abdelrhim, M. and Elsayed, A., 2020. The Effect of COVID-19 Spread on the e-commerce market: The case of the 5 largest e-commerce companies in the world. Available at SSRN 3621166. DOI: https://dx.doi.org/10.2139/ssrn.3621166.
Galhotra, B. and Dewan, A., 2020, October. Impact of COVID-19 on digital platforms and change in E-commerce shopping trends. In 2020 fourth international conference on I-SMAC (IoT in social, mobile, analytics and cloud)(I-SMAC) (pp. 861-866). IEEE. DOI: https://doi.org/10.1109/I-SMAC49090.2020.9243379.
Kamthania, D., Pawa, A. and Madhavan, S.S., 2018. Market segmentation analysis and visualization using K-mode clustering algorithm for E-commerce business. Journal of computing and information technology, 26(1), pp.57-68. DOI: https://doi.org/10.20532/cit.2018.1003863.