2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 1 , Issue 2 , PP: 70-76, 2020 | Cite this article as | XML | Html | PDF | Full Length Article
Salah-ddine Krit 1 *
Doi: https://doi.org/10.54216/AJBOR.010202
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.
E-Commerce , Business Intelligence , Recommendation System , Customer Engagement
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