Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Machine Learning
A Data Mining Approach for Retailing Bank Customer Attrition Analysis
Applied Intelligence
Cluster ranking with an application to mining mailbox networks
Knowledge and Information Systems
Identification of influencers - Measuring influence in customer networks
Decision Support Systems
Expert Systems with Applications: An International Journal
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Expert Systems with Applications: An International Journal
The effects of innovation alliance on network structure and density of cluster
Expert Systems with Applications: An International Journal
An expert system for detecting automobile insurance fraud using social network analysis
Expert Systems with Applications: An International Journal
Customer churn prediction --a case study in retail banking
Proceedings of the 2010 conference on Data Mining for Business Applications
Recognizing plankton images from the shadow image particle profiling evaluation recorder
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Improved multilevel security with latent semantic indexing
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This study investigates the advantage of social network mining in a customer retention context. A company that is able to identify likely churners in an early stage can take appropriate steps to prevent these potential churners from actually churning and subsequently increase profit. Academics and practitioners are constantly trying to optimize their predictive-analytics models by searching for better predictors. The aim of this study is to investigate if, in addition to the conventional sets of variables (socio-demographics, purchase history, etc.), kinship network based variables improve the predictive power of customer retention models. Results show that the predictive power of the churn model can indeed be improved by adding the social network (SNA-) based variables. Including network structure measures (i.e. degree, betweenness centrality and density) increase predictive accuracy, but contextual network based variables turn out to have the highest impact on discriminating churners from non-churners. For the majority of the latter type of network variables, the importance in the model is even higher than the individual level counterpart variable.