  One of the goals of every business enterprise is to increase customer
loyalty. The degree of customer loyalty is called customer quality which its
forecasting will affect strategic marketing practices. The purpose of this
study is to predict the quality of customers of large e-commerce social
networks by big data algorithms and unsupervised learning. For this purpose, a
graph-based social network analysis framework was used for community detection
in the Stanford Network Analysis Platform (SNAP). Then in the found
communities, the quality of customers was predicted. The results showed that
various visits with an impact of 37.13% can have the greatest impact on
customer quality and the order of impact of other parameters were from highest
to lowest: number of frequent customer visits (28.56%), role in social networks
(28.37%), Indirect transactions (26.74%), activity days (25.62%) and customer
social network size (25.06%).
