Application of Association Rules in Analyzing Online Shopping Behavior Using Data Mining Techniques
DOI:
https://doi.org/10.55164/ecbajournal.v18i3.278139Keywords:
Association Rules, Behavior Analysis, Data MinningAbstract
The application of data mining techniques in online markets has gained significant popularity due to the rapid growth of online businesses and growing customer demand, resulting in the accumulation of transaction data on online platforms. While online businesses face intense competition in reaching target customers, customer purchase data can be analyzed to develop targeted promotional strategies that align with customer needs. The generation of association rules from purchase data enables businesses to effectively reach target customers and gain a competitive advantage. This research focuses on analyzing the relationship between purchasing behavior and the products ordered by customers on online platforms, using data mining techniques to identify association rules using the Apriori algorithm under the SEMMA framework. The researcher utilized a dataset comprising 97,780 rows and 51 columns collected during 2022–2023 from an online platform to analyze the relationship between 10 product items from 10 rules. The results revealed that the strongest association rule was that customers purchasing perfume on TikTok were also likely to purchase lipstick on TikTok, with a confidence value of 0.80, indicating that 80% of customers who purchased perfume also purchased lipstick. The rule had a support value of 0.12, indicating that 12% of all transactions included both products, and a confidence value of 0.80. This study contributes to the development of knowledge in the application of data mining techniques in the context of online marketplaces, particularly by employing the Apriori algorithm to analyze associations among products that are frequently purchased together by customers. The objective is to generate association rules that can be applied to proactive marketing strategies. The findings reveal notable association patterns between certain product categories, which can be utilized to develop promotional campaigns that align with consumer behavior and enhance the competitive advantage of online businesses in the digital era.
References
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of The 1993 ACM SIGMOD International Conference on Management of Data. 207-216. DOI: https://doi.org/10.1145/170035.170072
Arunkijcharoen, N., Phongtheeratham, P., & Lake, P. (2023). Using association rules in data mining techniques to enhance retail sales in community stores: A case study of Suan-Luang community. The 9th Thai-Nichi Institute of Technology Academic Conference (TNIAC). 129 – 133. IT-business-computer-2023-proceeding-Using-Association-Rules-in-Data-Mining-Techniques-of-Suan-Luang-Community-1.pdf (E-Research.Siam.edu)
Belani, S., & Hadiyati, R. (2024). Consumer behavior in the e-commerce era. Management Studies and Business Journal (Productivity), 1(2), 171-179. DOI: https://doi.org/10.62207/4KVWFG64
Ganesh, J., Reynolds, K. E., Luckett, M., & Pomirleanu, N. (2010). Online shopper motivations, and e-store attributes: An examination of online patronage behavior and shopper typologies. Journal of Retailing, 86(1), 106-115. DOI: https://doi.org/10.1016/j.jretai.2010.01.003
Huang, Z., & Benyoucef, M. (2013). From e-commerce to social commerce: A close look at design features. Electronic Commerce Research and Applications, 12(4), 246-259. DOI: https://doi.org/10.1016/j.elerap.2012.12.003
Kongsawee, S., & Wiwatwattana, N. (2023). Association rules analysis of fresh fruits : A case study of instacart company. Proceeding of the Data Science Conference Master of Science in Data Science. 132 – 148. retrieved from http://ir-ithesis.swu.ac.th/dspace/handle/123456789/2235
Kuppuswamy, P., Ansari, M. D., Mohan, M., & Al Khalidi, S. Q. (2024). Data mining for predictive analytics. Intelligent Techniques for Predictive Data Analytics, 1-24. DOI: https://doi.org/10.1002/9781394227990.ch1
Li, L. (2023). Analysis of e-commerce customers' shopping behavior based on data mining and machine learning. Soft Computing, 1-10. DOI: https://doi.org/10.1007/s00500-023-08903-5
Punhani, R., Arora, V. P. S., Sabitha, A. S., & Shukla, V. K. (2021). Segmenting e-commerce customer through data mining techniques. In Journal of Physics: Conference Series, 1714(1), 12 - 26. DOI: https://doi.org/10.1088/1742-6596/1714/1/012026
Santoso, M. H. (2021). Application of association rule method using apriori algorithm to find sales patterns case study of Indomaret Tanjung Anom. Brilliance: Research of Artificial Intelligence, 1(2), 54-66. DOI: https://doi.org/10.47709/brilliance.v1i2.1228
Sinha, A. (2021). Implying association rule mining and market basket analysis for knowing consumer behavior and buying pattern in lockdown-a data mining approach. Prerpints.org Logo. 125 – 140. DOI: https://doi.org/10.20944/preprints202105.0102.v1
Tsiu, S. V., Ngobeni, M., Mathabela, L., & Thango, B. (2025). Applications and competitive advantages of data mining and business intelligence in smes performance: A systematic review. Businesses, 5(2), 22. DOI: https://doi.org/10.3390/businesses5020022
Zhou, S. (2024). Research on online shopping recommendation algorithm based on apriori algorithm with association rules. In Proceedings of the 2024 International Conference on Intelligent Education and Computer Technology. 322-328. DOI: https://doi.org/10.1145/3687311.3687370
Zuniarti, I., Yuniasih, I., Martana, I., Setyaningsih, E., Susilowati, I., Pramularso, E., & Astuti, D. (2021). The effect of the presence of e-commerce on consumer purchasing decisions. International Journal of Data and Network Science, 5(3), 479-484. DOI: https://doi.org/10.5267/j.ijdns.2021.3.005
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