通过集体学习增强银行营销策略:实证分析
1Institute of Traffic Engineering, Nanjing Vocational University of Industry Technology, Nanjing, Jiangsu, China.
PloS one
|January 11, 2024
概括
本研究引入了一种集体学习模型,通过准确预测客户需求和优化数据道来改善银行营销. 该模型增强了市场细分,从而提高了电子商务银行业务的销售额和客户满意度.
科学领域:
- * 金融服务营销 金融服务营销
- * 银行业中的数据科学.
- *电子商务战略 *电子商务战略
背景情况:
- * 传统的银行营销策略往往导致客户需求不分化和产品均化.
- *大型银行面临市场份额和竞争力方面的挑战,原因是营销方法效率低下.
- *银行客户交易数据的不平衡阻碍了有效的营销努力.
研究的目的:
- *使用金融数据集开发客户需求学习模型.
- * 优化银行大数据道分发模式,以改善营销.
- *通过先进的预测模型,加强电子商务银行的市场细分.
主要方法:
- * 开发一个客户需求学习模型.
- *利用诱导优化银行大数据道的分布.
- *对随机森林和支持矢量机 (SVM) 预测模型进行比较分析.
- * 应用集体学习来增强市场细分.
主要成果:
- *随机森林模型达到92%的准确性,超过了SVM模型的87%的准确性.
- * 与单个模型相比,组合学习模型显示出更高的准确性和预测能力.
- * 整体学习模式的实施导致销售额增长率达到20%,客户满意度增加了30%.
结论:
- * 集合学习模型通过实现有针对性的策略和改进客户关系管理,显著增强了银行营销.
- * 拟议的模型有效地解决了客户需求预测,并优化了银行电子商务服务的产品营销策略.
- * 该研究为银行营销决策和改善整体营销绩效提供了有价值的学术和实际见解.
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