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Advanced Analytics for Customer Intelligence Using SAS

Business Knowledge Series course

Duration: 3.0 days
Course fee:
EPTO units: .
CEUs: 1.8 		Available for on-site training or can be scheduled at any SAS training facility if demand warrants.

Presented by Bart Baesens, Ph.D. or Christophe Mues, Ph.D., Assistant Professors at the School of Management of the University of Southampton (UK)

This advanced, highly interactive course will clarify how you can adopt state-of-the-art data mining techniques for complex customer intelligence applications. You will receive a sound mix of both theoretical and technical insights as well as practical implementation details, illustrated by several real-life cases.

Learn how to

Who should attend

Those involved in estimating, monitoring, or maintaining predictive models for various types of customer intelligence; those involved with using data mining techniques for various types of customer intelligence

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Prerequisites
Before attending this course, you should know how to
  • preprocess data (such as missing values, outliers, categorization, sampling, etc.)
  • develop predictive models using logistic regression
  • develop predictive models using decision trees
  • develop descriptive models using basic segmentation techniques
  • quantify the performance of predictive models (lift curves, ROC curves, etc.).
You can gain this experience by completing Data Mining Techniques: Theory and Practice and Decision Tree Modeling
Course Contents
Predictive Modeling for Customer Intelligence: The KDD Process Model
A Refresher on Data Preprocessing and Data Mining
Advanced Sampling Schemes
  • cross-validation (stratified, leave-one-out)
  • bootstrapping
Neural networks
  • multilayer perceptrons (MLPs)
  • MLP types (RBF, recurrent, etc.)
  • weight learning (backpropagation, conjugate gradient, etc.)
  • overfitting, early stopping, and weight regularization
  • architecture selection (grid search, SNC, etc.)
  • input selection (Hinton graphs, likelihood statistics, brute force, etc.)
  • self organizing maps (SOMs) for unsupervised learning
  • case study: SOMs for country corruption analysis
Support Vector Machines (SVMs)
  • linear programming
  • the kernel trick and Mercer theorem
  • SVMs for classification and regression
  • multiclass SVMs (one versus one, one versus all coding)
  • hyperparameter tuning using cross-validation methods
  • case study: benchmarking SVM classifiers
Opening up the Neural Network and SVM Black Box
  • rule extraction methods (pedagogical versus decompositional approaches such as neurorule, neurolinear, trepan, etc.
  • two-stage models
A Recap of Decision Trees (C4.5, CART, CHAID)
Regression Trees
  • splitting/stopping/assignment criteria
Ensemble Methods
  • bagging
  • boosting
  • stacking
  • random forests
Alternative Rule Representation Formats
  • rule types (oblique, M-of-N, fuzzy, etc.)
  • decision tables (lexicographical ordering, contraction methods, etc.)
  • decision diagrams
  • case study: decision tables and diagrams for customer scoring
Bayesian Network Classifiers
  • naive Bayes
  • tree augmented naive Bayes (TAN)
  • unrestricted Bayesian network classifiers
  • Bayesian inference
  • case study: Bayesian networks for churn prediction
Survival Analysis
  • censoring
  • Kaplan-Meier analysis
  • parametric survival analysis
  • proportional hazards regression
  • neural networks for survival analysis
  • case study: neural network survival analysis for customer scoring
Learning Using Networked Data
  • Markov random fields
  • homophily (guilt by association)
  • local classifiers
  • relational classifiers (relational neighbor, probabilistic relational neighbor, relational logistic regression, etc.)
  • collective inference (Gibbs sampling, iterative classification, etc.)
Monitoring and Backtesting Analytical Models
  • quantitative versus qualitative model monitoring
  • model backtesting (model stability, binomial/Hosmer-Lemeshow test, traffic light indicator approach, impact of macro-economic effects)
  • model benchmarking (internal versus external benchmarking, benchmarking statistics)
  • qualitative validation of analytical models (data quality, model design, documentation, involvement of management)
  • case study: backtesting a customer scoring model
Other Predictive Modeling Techniques (Short)
  • semi-supervised learning
  • genetic algorithms
  • fuzzy techniques
  • ant colony optimization
  • case study: Antminer+
Software
This course addresses SAS Enterprise Miner, SAS/STAT, SAS/INSIGHT.
Course Materials
Students receive a hardcopy of the course notes and, in some courses, can choose to take home a copy of the course data.

Share Your Thoughts

Are there additional topics you'd like for this course to address? Would you like for this course to be offered at another training facility? Let us know by adding to our Interest List.

Course fee and EPTO units will differ for on-site training.

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