基于混合神经网络的客户流失预测模型
Xinyu Liu1, Guoen Xia1,2, Xianquan Zhang3
1College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541000, China.
Scientific reports
|December 27, 2024
概括
本研究介绍了CCP-Net,这是一个用于预测客户流失的混合神经网络. 通过有效提取复杂特征,CCP-Net显著提高了预测准确性,在各种数据集中表现优于现有模型.
科学领域:
- 机器学习 机器学习
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 保持客户对于组织的可持续性至关重要.
- 传统模型在难以处理复杂的非线性和时间序列数据来进行流失预测.
- 样本不平衡会对模型性能产生负面影响.
研究的目的:
- 提出一种新的混合神经网络模型,CCP-Net,用于准确的客户流失预测.
- 解决传统机器学习和单一深度学习模型的局限性.
- 通过提高预测准确度来增强客户保留策略.
主要方法:
- 使用ADASYN采样进行数据预处理,以平衡不平衡的数据集.
- 采用混合架构,结合多头自我注意,BiLSTM和CNN进行特征提取.
- 实现了电信,银行,保险和新闻数据集的交叉验证.
主要成果:
- 与现有的算法相比,CCP-Net在所有评估指标中表现出卓越的性能.
- 实现了高精度率:92.19% (电信),91.96% (银行),95.87% (保险) 和95.12% (新闻).
- 与其他混合神经网络模型相比,其性能提高了1-3%.
结论:
- CCP-Net模型有效地提高了客户流失预测的准确性和稳定性.
- 它的设计适用于各种行业的广泛应用,包括金融,电信和媒体.
- 为企业提供更有效的流失管理策略.
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