Affinity Analysis Models of Transactional and Behavioral Customer Data for Personalized Content Generation in B2B E-commerce Systems
DOI:
https://doi.org/10.15276/opu.2.72.2025.10Keywords:
B2B e-commerce, personalization, affinity analysis, transactional data, behavioral data, sequential patterns, conceptual modeling, Apriori, FP-Growth, EclatAbstract
The growing financial significance and structural complexity of the B2B e-commerce market segment, coupled with the necessity to enhance its efficiency, have necessitated a systematic analysis of existing affinity analysis models for transactional and behavioral data. This study aims to justify the adaptation of these models to improve the personalization of product, information, and recommendation content within B2B e-commerce systems. Based on a comparative analysis of B2B and B2C e-commerce systems across key indicators, the specific characteristics of B2B systems are established to define the “Business-to-Business” transaction concept. The study concludes that product, information, and recommendation content must prioritize rational decision-making, large-scale procurement volumes, and long-term partnerships. The paper analyzes affinity analysis models based on Apriori, FP-Growth, and Eclat algorithms and data structures, identifying their limitations regarding the analysis of B2B customer transactional and behavioral data. Furthermore, it explores pathways for enhancing content personalization by leveraging these models. A key contribution of this research is the development of conceptual model elements for B2B commercial activity. This model for content personalization is built upon affinity analysis concepts while accounting for wholesale purchase characteristics, such as large volumes, individual pricing, and specific behavioral scenarios. The structure of the conceptual model for B2B commercial activity analysis is established. Finally, the study defines a comprehensive set of metrics to evaluate the effectiveness of B2B content personalization, based on the proposed conceptual model.
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References
Shopify. (2025). Global ecommerce statistics: Trends to guide your store in 2025. https://www.shopify.com/enterprise/blog/global-ecommerce-statistics.
Insider Intelligence. (2025). Mobile commerce shopping trends and stats. Emarketer. www.emarketer.com.
Panasiuk, T. S., & Zubenko, I. R. (2024). Prospects for the development of b2c e-commerce in the world. https://doi.org/10.5281/zenodo.14603373.
Arsirii, O. O., Ivanov, D. V., & Babilunha, O. Y. (2025). Analysis of models, methods and technologies for creating personalized content in B2B e-commerce systems. Informatyka. Kultura. Tekhnika, 2, 273–278. https://ict.op.edu.ua/index.php/journal/uk/issue/view/3.
Shveda, N. M., & Krauze, O. I. (2024). E-commerce: current state and development strategies. Internauka. Series: Economic Sciences, 2(82). https://doi.org/10.25313/2520-2294-2024-2-9639.
Hu, Y. H., Chen, Y. L., & Tang, K. (2009). Mining sequential patterns in the B2B environment. Journal of Information Science, 35(6), 677–694. https://doi.org/10.1177/0165551509103600.
Baza Obuvi. (n.d.). B2B e-commerce system "Shoe Base". Retrieved November 2025, Retrieved from https://bazaobuvi.com.ua/ua/.
Cramer-Flood, E. (2022). Global ecommerce forecast & growth projections. Insider Intelligence. Retrieved from https://www.insiderintelligence.com/content/globalecommerce-forecast-2022.
Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–216. https://doi.org/10.1145/170035.170072.
Han, J., Pei, J., & Yin, Y. (2004). Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 8, 53–87. https://doi.org/10.1023/B:DAMI.0000005258.31418.83.
Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390. DOI: 10.1109/69.
Turis. (n.d.). Grow your B2B sales with 6 smart B2B product bundling strategies. Retrieved from https://turis.app/b2b-ecommerce/grow-b2b-sales-6-b2b-product-bundle-strategies/.
Bloomenthal, A. (2025, June 15). SKU: What it is and how it works. Investopedia. https://www.investopedia.com/terms/s/stock-keeping-unit-sku.asp.
Arsirii, O. O., Liubomska, O. M., Rudenko, O. V., & Ivanov, D. V. (2024). Hybrid recommendation system to support UI/UX designers. Odeska Politekhnika, 29–35. https://doi.org/10.15276/ict.01.2024.30.
Koh, Y. S., & Rountree, N. (2009). Rare association rule mining and knowledge discovery: Technologies for infrequent and critical event detection. IGI Global. DOI 10.4018/978-1-60566-754-6.
Larose, D. T., & Larose, C. D. (2014). Discovering knowledge in data: An introduction to data mining. John Wiley & Sons, Inc. DOI 10.4018/978-1-60566-754-6.
Sengupta, D., Sood, M., Vijayvargia, P., Hota, S., & Naik, P. K. (2013). Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor. Bioinformation, 9(11), 555–559. DOI 10.6026/97320630009555.
Lakshmi, K. S., & Vadivu, G. (2017). Extracting association rules from medical health records using multi-criteria decision analysis. Procedia Computer Science, 115, 290–295. https://doi.org/10.1016/j.procs.2017.09.137.
Zimek, A., Assent, I., & Vreeken, J. (2014). Frequent pattern mining algorithms for data clustering. In C. Aggarwal & J. Han (Eds.), Frequent pattern mining (pp. 403–423). Springer, Cham. https://doi.org/10.1007/978-3-319-07821-2_16.
Heaton, J. (2017). Comparing dataset characteristics that favor the Apriori, Eclat or FP-growth frequent itemset mining algorithms. arXiv. https://doi.org/10.48550/arXiv.1701.09042.
