Enterprise Analytics using Graph Database and Graph-based Deep Learning

TL;DR

This paper presents a novel graph-based deep learning approach using GCNs on Neo4j graph databases to enhance B2B CRM sales forecasting, outperforming traditional models.

Contribution

It introduces the first application of GCNs for binary classification in B2B CRM using graph databases and demonstrates improved prediction accuracy.

Findings

GCN outperforms RF, CNN, and ANN in sales prediction

Augmenting GCN with shortest path and eigenvector centrality improves accuracy

Graph-based deep learning enhances CRM analytics effectiveness

Abstract

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.