机器学习在预测消费者行为和精确营销方面的应用
1College of New Media, Yango University, Fuzhou City, China.
PloS one
|May 6, 2025
概括
像CatBoost和XGBoost这样的机器学习模型擅长预测消费者购买意图,提高精确营销. 这些模型有效处理复杂的数据,在竞争性市场上提高转换率.
科学领域:
- 机器学习 机器学习
- 消费者行为分析 消费者行为分析
- 精准营销是指精准的营销.
背景情况:
- 增加的市场竞争和复杂的消费者行为需要先进的客户识别和转化率优化策略.
- 准确预测消费者购买意图对于有效的营销活动至关重要.
研究的目的:
- 研究机器学习模型用于预测消费者购买意向的应用.
- 为了比较支持向量机 (SVM),极端梯度提升 (XGBoost),分类提升 (CatBoost) 和反向传播人工神经网络 (BPANN) 在消费者行为预测中的性能.
主要方法:
- 使用了四种机器学习模型:SVM,XGBoost,CatBoost和BPANN.
- 通过专注于预测准确性,F1分数和ROC AUC的实验来评估模型性能.
- 进行特征重要性分析,以确定购买行为的关键驱动因素.
主要成果:
- CatBoost和XGBoost在复杂的特征和大型数据集上表现出卓越的预测性能,分别获得了0.93和0.92的F1分数.
- CatBoost获得了最高的ROC AUC,为0.985.
- SVM显示了高准确性,但在大规模数据中表现不佳.
结论:
- 机器学习模型,特别是CatBoost和XGBoost,是预测消费者购买意图并实现精确营销的有效工具.
- 诸如页面浏览和停留时间等关键功能显著影响购买行为.
- 模型预测可以为优化策略提供信息,包括推系统,动态定价和个性化广告.
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