Risk Analysis in Customer Relationship Management via Quantile Region Convolutional Neural Network-Long Short-Term Memory and Cross-Attention Mechanism

Huang, Yaowen, Der Leu, Jun, Lu, Baoli, Zhou, Yan

arXiv.org Artificial Intelligence 

Northeastern University, San Jose, California, 95131, United States Abstract Risk analysis is an important business decision support task in customer relationship management (CRM), involving the identification of potential risks or challenges that may affect customer satisfaction, retention rates, and overall business performance. To enhance risk analysis in CRM, this paper combines the advantages of quantile region convolutional neural network-long short-term memory (QRCNN-LSTM) and cross-attention mechanisms for modeling. The QRCNN-LSTM model combines sequence modeling with deep learning architectures commonly used in natural language processing tasks, enabling the capture of both local and global dependencies in sequence data. The cross-attention mechanism enhances interactions between different input data parts, allowing the model to focus on specific areas or features relevant to CRM risk analysis. By applying QRCNN-LSTM and cross-attention mechanisms to CRM risk analysis, empirical evidence demonstrates that this approach can effectively identify potential risks and provide data-driven support for business decisions. Keywords: CRM, deep learning, QRCNN-LSTM, cross-attention mechanism, business decision Introduction In today's competitive business environment, customer relationship management (CRM) is one of the key factors for success (Haiyun et al., 2021). To provide personalized services, increase customer satisfaction, and improve sales performance, businesses need to deeply understand and analyze customer behavior and timely identify potential risks (Li et al., 2020b). Traditional risk analysis methods often rely on experience and intuition, but the development of deep learning and machine learning technologies offers new opportunities and challenges for risk analysis in CRM (Libai et al., 2020). Some commonly used deep learning or machine learning models are: Logistic regression model (Guerola-Navarro et al., 2021): Logistic regression is a widely used classification algorithm that can predict different risk categories. However, logistic regression models often fail to capture complex nonlinear relationships. Decision tree model (Chen et al., 2021): Decision tree models can generate easy-tounderstand rules and have good interpretability. But they tend to overfit when dealing with data that has complex structures and high-dimensional features. Random forest model (Rao et al., 2020): Random forests are an ensemble learning method that improves prediction performance by combining multiple decision tree models.