In a business-to-business (B2B) customer relationship management (CRM) use case, each client is a potential business organization/company with a solid business strategy and focused and rational decisions. This paper introduces a graph-based analytics approach to improve CRM within a B2B environment. In our approach, in the first instance, we have designed a graph database using the Neo4j platform. Secondly, the graph database has been investigated by using data mining and exploratory analysis coupled with cypher graph query language. Specifically, we have applied the graph convolution network (GCN) to enable CRM analytics to forecast sales. This is the first step towards a GCN-based binary classification based on graph databases in the domain of B2B CRM. We evaluate the performance of the proposed GCN model on graph databases and compare it with Random Forest (RF), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN). The proposed GCN approach is further augmented with the shortest path and eigenvector centrality attribute to significantly improve the accuracy of sales prediction. Experimental results reveal that the proposed graph-based deep learning approach outperforms the Random Forests (RsF) and two deep learning models, i.e., CNN and ANN under different combinations of graph features.
翻译:在企业对企业(B2B)客户关系管理(CRM)使用的案件中,每个客户都是潜在的商业组织/公司,具有扎实的商业战略,有重点和理性的决定;本文件采用基于图表的分析方法,在B2B环境中改进客户关系管理;我们首先利用Neo4j平台设计了一个图表数据库;第二,通过利用数据挖掘和探索分析以及密码图形查询语言,对图表数据库进行了调查;具体地说,我们应用了图形组合网络,使客户关系分析能够预测销售情况;这是根据B2B CRM领域图表数据库进行基于GCN的二进制分类的第一步。我们评估了拟议的GCN图形数据库模型的性能,并将其与随机森林(RF)、革命神经网络(CNNN)和人工神经网络(ANN)进行比较。拟议的GCN方法以最短路径和精度核心特征为基础,进一步扩大了GR对销售预测的准确性。这是在B2BBCR CR域域图下,实验性结果显示拟议的GCN模型和FR图式两种不同的图表方法。