Advanced Analytics for Customer Intelligence Using SAS

Duration: 3.0 days

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

• apply a series of powerful, recently developed, cutting-edge data mining techniques
• ensure the practical application of these techniques to optimize strategic business decisions
• explore a futuristic vision of how new emerging data mining techniques might change your key business processes
• deploy, monitor, and optimally back-test data mining systems.

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

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 Addressed

This course addresses the following software product(s): SAS Enterprise Miner, SAS/STAT, SAS/INSIGHT.

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