Peng, C.-C., Wang, Y.-Z., Huang, C.-W.: Artificial-neural-network-based consumer behavior prediction: a survey. In: 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), pp. ĭou, X.: Online purchase behavior prediction and analysis using ensemble learning. Wong, E., Wei, Y.: Customer online shopping experience data analytics: integrated customer segmentation and customised services prediction model. We validate our approach using customer data from an e-commerce company and conduct multi-dimensional comparisons to demonstrate the practicality of our method.Ītta-ur-Rahman, Dash, S., Luhach, A.K., et al.: A Neuro-fuzzy approach for user behaviour classification and prediction. We propose a prediction model based on weighted support vector machines (SVM) that can reduce the churn of customers while maintaining reasonable accuracy. On the other hand, many studies on customer behavior prediction do not leverage unstructured data, such as conversation records, to improve a prediction model’s performance. Many practitioners have noted that in customer behavior prediction, some special requirements, such as minimizing false predictions, need to be implemented to prevent the churn of potential customers. Analyzing customer behavior is an initial step in marketing strategies and revenue generation, and then companies can predict purchasing behavior to enhance efficiency and boost profits. In today’s highly competitive environment, effective customer relationship management (CRM) is critical for every company, especially e-commerce businesses.
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