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使用复合深度学习技术预测客户流失率
Asad Khattak1, Zartashia Mehak2, Hussain Ahmad2
1College of Technological Innovation, Zayed University, Abu Dhabi Campus, 144534, Abu Dhabi, UAE.
Scientific reports
|October 12, 2023
概括
这项研究引入了一种混合深度学习模型BiLSTM-CNN,以提高客户流失预测的准确性. 这种新的方法有效地识别了可能会离开的客户,减少了企业的财务损失.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 业务分析 业务分析
背景情况:
- 客户流失给企业带来了重大的财务挑战,影响了客户保留工作.
- 现有的机器学习和深度学习模型往往难以准确地预测客户流失率.
- 以前的方法忽略了深度神经网络特征提取中的序列信息.
研究的目的:
- 开发一种有效的混合深度学习模型,用于准确的客户流失预测.
- 为了提高客户流失估计流程的准确性和可靠性.
- 为了解决当前ML/DL方法的限制,以检测流失.
主要方法:
- 开发了一个新的混合深度学习模型BiLSTM-CNN.
- 该模型整合了双向长短期记忆 (BiLSTM) 和卷积神经网络 (CNN) 组件.
- 拟议的模型在基准数据集上进行了培训,测试和验证.
主要成果:
- 在基准数据集上,BiLSTM-CNN模型实现了81%的惊人的预测准确度.
- 实验结果证明了该模型在估计客户流失率方面的有效性.
- 混合方法显示了比传统方法更好的性能.
结论:
- BiLSTM-CNN模型为预测客户流失提供了一个有希望和有效的解决方案.
- 这种先进的深度学习技术可以显著改善客户保留策略.
- 这项研究强调了序列信息在流失预测模型中的重要性.
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