A common assumption in any analytics and/or data mining application is that customer behavior is independent and identically distributed, often referred to as the "iid" assumption. However, in many real-life settings, this assumption is simply not valid.
Social network effects between customers, both implicit and explicit, create collective correlational behavior that needs to be appropriately analyzed and modeled. The methodology presented in this session easily generalizes to other areas where social networks also play a crucial role, including customer acquisition, risk management and fraud detection.
This webinar will:
- Outline the architecture of a social network learning environment, consisting of a local model(e.g., a logistic regression model), a relational learner (e.g., a relational neighbor classifier) and a collective inferencing procedure (e.g., Gibbs sampling).
- Illustrate ideas and concepts using two real-life case studies about churn detection in the telecom sector with social networks using call detail record data from two major European telco providers.
- Show how social network effects can be efficiently modeled using these huge data sets, generating both additional lift and profit compared to a flat logistic regression model.
Associate Professor, K.U. Leuven (Belgium)
Lecturer, University of Southampton (United Kingdom)
Bart Baesens has done extensive research on predictive analytics, data mining, customer relationship management, Web analytics, fraud detection and credit risk management. His findings have been published in well-known international journals, including Machine Learning, Management Science, IEEE Transactions on Neural Networks, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Evolutionary Computation, and Journal of Machine Learning Research. Baesens also presents frequently at top international conferences and is co-author of the book Credit Risk Management: Basic Concepts, published in 2008. He regularly tutors, advises and provides consulting support to international firms with respect to their data mining, predictive analytics and credit risk management policies.