1
Ibn Zohr University, Agadir, Morocco
(salahddine.krit@gmail.com)
Abstract :
The e-commerce industry is continuously growing, and personalized customer engagement has become a crucial aspect of business success. In this paper, we propose a smart recommendation system using a business intelligence approach to enhance customer engagement and increase sales. We explore the use of machine learning algorithms to generate personalized product recommendations, incorporating customer behavior analysis and historical data. Our proposed approach considers various factors such as purchase history, browsing history, demographics, and social media activities to generate personalized recommendations. The system's effectiveness is evaluated using metrics such as click-through rate, conversion rate, and revenue generated. We believe that our proposed approach can provide e-commerce businesses with an effective way to increase customer engagement and sales while improving the overall customer experience.
Keywords :
E-Commerce; Business Intelligence; Recommendation System; Customer Engagement
References :
[1] Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.
[2] Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29-44.
[3] Itani, O. S., Agnihotri, R., & Dingus, R. (2017). Social media use in B2b sales and its impact on competitive intelligence collection and adaptive selling: Examining the role of learning orientation as an enabler. Industrial Marketing Management, 66, 64-79.
[4] Griva, A., Bardaki, C., Pramatari, K., & Papakiriakopoulos, D. (2018). Retail business analytics: Customer visit segmentation using market basket data. Expert Systems with Applications, 100, 1-16.
[5] Gunasekaran, A., Yusuf, Y. Y., Adeleye, E. O., & Papadopoulos, T. (2018). Agile manufacturing practices: the role of big data and business analytics with multiple case studies. International Journal of Production Research, 56(1-2), 385-397.
[6] Kubina, M., Koman, G., & Kubinova, I. (2015). Possibility of improving efficiency within business intelligence systems in companies. Procedia Economics and Finance, 26, 300-305.
[7] Laursen, G. H., & Thorlund, J. (2016). Business analytics for managers: Taking business intelligence beyond reporting. John Wiley & Sons.
[8] Conboy, K., Mikalef, P., Dennehy, D., & Krogstie, J. (2020). Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), 656-672.
[9] Phillips-Wren, G., Iyer, L. S., Kulkarni, U., & Ariyachandra, T. (2015). Business analytics in the context of big data: A roadmap for research. Communications of the Association for Information Systems, 37(1), 23.
[10] Ram, J., Zhang, C., & Koronios, A. (2016). The implications of big data analytics on business intelligence: A qualitative study in China. Procedia Computer Science, 87, 221-226.
[11] Duan, L., & Xiong, Y. (2015). Big data analytics and business analytics. Journal of Management Analytics, 2(1), 1-21.
[12] Peters, M. D., Wieder, B., Sutton, S. G., & Wakefield, J. (2016). Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage. International Journal of Accounting Information Systems, 21, 1-17.
[13] Krishnamoorthi, S., & Mathew, S. K. (2018). Business analytics and business value: A comparative case study. Information & Management, 55(5), 643-666.
[14] Aydiner, A. S., Tatoglu, E., Bayraktar, E., Zaim, S., & Delen, D. (2019). Business analytics and firm performance: The mediating role of business process performance. Journal of business research, 96, 228-237.
[15] Olszak, C. M. (2016). Toward better understanding and use of business intelligence in organizations. Information systems management, 33(2), 105-123.
[16] Rapp, A., Agnihotri, R., Baker, T. L., & Andzulis, J. M. (2015). Competitive intelligence collection and use by sales and service representatives: how managers’ recognition and autonomy moderate individual performance. Journal of the Academy of Marketing Science, 43, 357-374.
[17] Duan, Y., Cao, G., & Edwards, J. S. (2020). Understanding the impact of business analytics on innovation. European Journal of Operational Research, 281(3), 673-686.
[18] Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence & analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37-58.
[19] Banerjee, M., & Mishra, M. (2017). Retail supply chain management practices in India: A business intelligence perspective. Journal of Retailing and Consumer Services, 34, 248-259.
[20] He, W., Wu, H., Yan, G., Akula, V., & Shen, J. (2015). A novel social media competitive analytics framework with sentiment benchmarks. Information & Management, 52(7), 801-812.
[21] Bolton, R. N., McColl-Kennedy, J. R., Cheung, L., Gallan, A., Orsingher, C., Witell, L., & Zaki, M. (2018). Customer experience challenges: bringing together digital, physical and social realms. Journal of service management, 29(5), 776-808.
[22] Parise, S., Guinan, P. J., & Kafka, R. (2016). Solving the crisis of immediacy: How digital technology can transform the customer experience. Business Horizons, 59(4), 411-420.
[23] Delen, D., & Ram, S. (2018). Research challenges and opportunities in business analytics. Journal of Business Analytics, 1(1), 2-12.
[24] Davenport, T. H. (2018). From analytics to artificial intelligence. Journal of Business Analytics, 1(2), 73-80.
[25] Kunz, W., Aksoy, L., Bart, Y., Heinonen, K., Kabadayi, S., Ordenes, F. V., ... & Theodoulidis, B. (2017). Customer engagement in a big data world. Journal of Services Marketing.
| Style | # |
|---|---|
| MLA | Salah-ddine Krit. "Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales." American Journal of Business and Operations Research, Vol. 1, No. 2, 2020 ,PP. 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |
| APA | Salah-ddine Krit. (2020). Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales. Journal of American Journal of Business and Operations Research, 1 ( 2 ), 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |
| Chicago | Salah-ddine Krit. "Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales." Journal of American Journal of Business and Operations Research, 1 no. 2 (2020): 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |
| Harvard | Salah-ddine Krit. (2020). Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales. Journal of American Journal of Business and Operations Research, 1 ( 2 ), 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |
| Vancouver | Salah-ddine Krit. Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales. Journal of American Journal of Business and Operations Research, (2020); 1 ( 2 ): 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |
| IEEE | Salah-ddine Krit, Smart Recommendations in E-commerce: A Business Intelligence Approach for Personalized Customer Engagement and Increased Sales, Journal of American Journal of Business and Operations Research, Vol. 1 , No. 2 , (2020) : 70-76 (Doi : https://doi.org/10.54216/AJBOR.010202) |