Statistical modeling and analysis for database marketing

Traditional statistical methods are limited in their ability to meet the modern challenge of mining large amounts of data. Data miners, analysts, and statisticians are searching for innovative new data mining techniques with greater predictive power, an attribute critical for reliable models and analyses.

Statistical Modeling and Analysis for Database Marketing: Effective Techniques for Mining Big Data delivers a collection of successful database marketing methodologies for big data. This compendium solves common database marketing problems by applying new hybrid modeling techniques that combine traditional statistical and new machine learning methods. The book delivers a thorough analysis of these cutting-edge techniques, which include non-statistical machine learning and genetic intelligent hybrid models.

By following the step-by-step procedures detailed in the text, database marketing professionals can learn how to apply the proper statistical techniques to any database marketing challenge. The practical case studies and examples provided involve real problems and real data, and are taken from a variety of industries, including banking, insurance, finance, retail, and telecommunications.

• Explores Gen IQ, a non-statistical machine learning model that is effective in finding the best possible subset variables
• Demonstrates profiling techniques for identifying best customers; expands discussion of the benefits of predictive profiling to include look-alike profiling
• Applies unconventional usage of CHAID algorithm toward market segment classification and other problems
• Examines three concepts in model assessment-traditional decile analysis, precision, and separability
• Exposes weaknesses of decile analysis, and offers new bootstrap approach for measuring database model efficiency

Contents

• Introduction
• Two simple data mining methods for variable assessment
• Logistic regression : the workhorse of database response modeling
• Ordinary regression : the workhorse of database profit modeling
• CHAID for interpreting a logistic regression model
• The importance of the regression coefficient
• The predictive contribution coefficient : a measure of predictive importance
• CHAID for specifying a model with interaction variables
• Market segment classification modeling with logistic regression
• CHAID as a method for filling in missing values
• Identifying your best customers : descriptive, predictive and look-alike profiling
• Assessment of database marketing models
• Bootstrapping in database marketing : a new approach for validating models
• Visualization of database models
• Genetic modelling in database marketing : the GenIQ model
• Finding the best variables for database marketing models
• Interpreting of coefficient-free models

• Index