Personalization Algorithms Impact on Customer Loyalty and Purchase Diversity in Retail Businesses (C-Stores)
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V6I3P102Keywords:
Personalization Algorithms, Customer Loyalty, Purchase Diversity, C-Stores, Retail Analytics, Collaborative Filtering, Content-Based Filtering, Customer Segmentation, Machine LearningAbstract
Personalization algorithms have become an integral part of modern retail strategies, particularly in the convenience store (C-store) segment. These algorithms analyse consumer behavior, preferences, and transactional data to tailor product recommendations and marketing communications, thereby fostering customer loyalty and encouraging diverse purchasing behavior. This paper explores the impact of personalization algorithms on customer loyalty and purchase diversity in the retail sector, emphasizing convenience stores. Using real-world data, the study implements collaborative filtering, content-based filtering, and hybrid algorithms to assess their effectiveness. The findings reveal that personalized recommendations significantly enhance customer retention and broaden the variety of purchased items. Furthermore, customer segmentation based on purchasing behavior, frequency, and recency is found to contribute to improved algorithm performance. Through detailed analysis, simulation, and case studies, the research provides actionable insights into how retailers can leverage AI-driven personalization to optimize customer experiences and revenue streams. Additionally, ethical considerations, algorithmic fairness, and the risks associated with over-personalisation are discussed. The study concludes with a roadmap for future research directions in the domain of personalized retail
References
1. Riegger, A. S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140-155.
2. Obiegbu, C. J., & Larsen, G. (2024). Algorithmic personalization and brand loyalty: An experiential perspective. Marketing Theory, 14705931241230041.
3. Anderson, E. T., Hansen, K., & Simester, D. (2009). The option value of returns: Theory and empirical evidence. Marketing Science, 28(3), 405-423.
4. Fader, P. S., Hardie, B. G., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, 42(4), 415-430.
5. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., & Riedl, J. (1994, October). Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer-supported cooperative work (pp. 175-186).
6. Sarwar, B., Karypis, G., Konstan, J., & Riedl, J. (2001, April). Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web (pp. 285-295).
7. Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web: methods and strategies of web personalization (pp. 325-341). Berlin, Heidelberg: Springer Berlin Heidelberg.
8. Burke, R. (2002). Hybrid recommender systems: Survey and experiments. User modelling and user-adapted interaction, 12(4), 331-370.
9. Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749.
10. Gupta, S., Lehmann, D. R., & Stuart, J. A. (2004). Valuing customers. Journal of Marketing Research, 41(1), 7-18.
11. Hu, R., & Pu, P. (2011, October). Enhancing Collaborative Filtering Systems with Personality Information. In Proceedings of the fifth ACM conference on Recommender systems (pp. 197-204).
12. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30-37.
13. Reinartz, W. J., & Kumar, V. (2003). The Impact of Customer Relationship Characteristics on Profitable Lifetime Duration. Journal of Marketing, 67(1), 77-99.
14. Lassk, F. G. (2000). Improving the satisfaction of C-store customers: preferences for potential product offerings. Journal of Professional Services Marketing, 20(2), 121-131.
15. Atak, A. (2021). Design Diversity and Brand Loyalty. Saudi Journal of Business and Management Studies, 6(3).
16. Park, S. H., & Han, S. P. (2013). From accuracy to diversity in product recommendations: Relationship between diversity and customer retention. International Journal of Electronic Commerce, 18(2), 51-72.
17. Deka, P. K. (2016). Understanding Customer Loyalty for Retail Stores and Influencing Factors. Journal of Management in Practice (Online Only), 1(1).
18. Widayanti, R., Chakim, M. H. R., Lukita, C., Rahardja, U., & Lutfiani, N. (2023). Improving recommender systems using hybrid techniques of collaborative filtering and content-based filtering. Journal of Applied Data Sciences, 4(3), 289-302.
19. Parthasarathy, G., & Sathiya Devi, S. (2023). A hybrid recommendation system based on collaborative and content-based filtering. Cybernetics and Systems, 54(4), 432-453.
20. Geetha, G., Safa, M., Fancy, C., & Saranya, D. (2018, April). A hybrid approach combining collaborative filtering and content-based filtering for a recommender system. In Journal of Physics: conference series (Vol. 1000, p. 012101). IOP Publishing.
21. Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.
22. Pereira, M. D. S., de Castro, B. S., Cordeiro, B. A., de Castro, B. S., Peixoto, M. G. M., da Silva, E. C. M., & Gonçalves, M. C. (2025). Factors of Customer Loyalty and Retention in the Digital Environment. Journal of Theoretical and Applied Electronic Commerce Research, 20(2), 71.
