为制造类企业B2B客户设计动态多准则分群框架，更精准识别客户稳定性与变化趋势。
An Analytical Multiple Criteria Framework for Temporal and Dynamic Business-to-Business Customer Segmentation in Manufacturing
- 扩展RFM模型，加入稳定性和增长性维度
- 基于时间序列聚类与图共识模型，识别客户跨时段迁移规律
- 适用于需长期跟踪客户变化的制造业战略决策者
在销售与营销中，客户细分是制定客户策略和供应链管理的重要工具。现有方法多依赖有限标准（如最近购买、频率、金额），难以捕捉复杂商业互动。本文提出一种面向制造行业B2B场景的动态多准则决策方法：1）将RFM扩展至稳定性与增长性维度；2）引入自适应解析层次过程以匹配业务目标；3）评估多种多变量时间序列聚类模型。通过追踪客户稳定性、段间转移及波动性，结合图式共识模型增强分析能力。基于真实制造企业数据集对3000+个B2B客户进行测试，验证了该方法对时间变化的强鲁棒性。系统支持领域专家进行偏好分析，为B2B客户细分提供有效决策支持。
原文摘要 · Abstract (English)
In sales and marketing, customer segmentation is an important tool for formulating strategies for customer treatment and supply chain management. Most segmentation implementations rely on limited criteria, such as recency, frequency, and monetary (RFM) modeling, which often fail to capture complex business interactions. In this work, we design and evaluate a dynamic multi-criteria decision-making (MCDM) method in a business-to-business (B2B) manufacturing context by 1) extending RFM to dimensions of stability and growth, 2) integrating an adaptive and analytical hierarchical process to match business objectives, and 3) evaluating multivariate time-series clustering models. We then measure customer stability, tracking between-segment transitions, and volatility over time, and apply a graph-based consensus model to further strengthen the analysis. We test the efficacy of the proposed method using a real-world manufacturing company dataset to segment more than 3,000 B2B customers, showing strong robustness to temporal shifts. The implementation enables domain experts with preferential analytics to devise their strategies, providing effective decision support for B2B customer segmentation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。