基于价值分级策略的商业银行潜在客户挖掘

Potential Customers Mining of Commercial Banks Based on Value Stratification Strategy

摘要: 潜在客户挖掘一直以来都是商业银行重点关注的领域之一，为解决单一模型在进行潜在客户挖掘可能存在的如精确度不足等问题，首先利用熵权法对客户信息进行赋权生成新的一级指标；其次，通过K-means，对客户进行价值分级，最后使用经过超参数调优的随机森林、KNN、BP-神经网络的混合模型对商业银行的潜在客户进行挖掘。最终的数据分析结果显示，客户挖掘的精确度高达99%，充分证明了本文建立的混合模型在异构数据与动态场景适应性的优势。该模型可以为商业银行提供端到端的客户分级解决方案，助力其合理分配资源，实现精准营销。局限性在于依赖仿真数据验证，未来需要引入真实业务数据进一步检验模型的鲁棒性。

Abstract: Potential customer mining has always been one of the key areas that commercial banks focus on. In order to solve the problems that may exist in the potential customer mining of a single model, such as insufficient accuracy, the entropy weight method is first used to empower customer information to generate new first-level indicators. Secondly, the value of customers is graded through K-means, and finally the potential customers of commercial banks are mined using a hybrid model of random forest, KNN and BP-neural network optimized by hyperparameters. The final data analysis results show that the accuracy of customer mining is as high as 99%, which fully proves the advantages of the hybrid model established in this paper in heterogeneous data and dynamic scene adaptability. This model can provide commercial banks with end-to-end customer classification solutions, helping them allocate resources rationally and achieve precision marketing. The limitation is that it relies on simulation data verification, and it is necessary to introduce real business data to further test the robustness of the model in the future.