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

TL;DR

This paper introduces a novel deep learning approach combining QRCNN-LSTM and cross-attention mechanisms to improve risk analysis in customer relationship management, enabling more accurate identification of potential risks.

Contribution

The paper presents a new model integrating QRCNN-LSTM with cross-attention for CRM risk analysis, enhancing sequence modeling and feature focus capabilities.

Findings

Effective risk identification in CRM data

Improved model accuracy over traditional methods

Enhanced focus on relevant data features

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.