Modeling Techniques in Predictive Analytics Business Problems and Solutions with R

by Miller, Thomas W.

• ISBN13: 9780133412932
• ISBN10: 0133412938
• Edition: 1st
• Format: Hardcover
• Copyright: 2013-08-29
• Publisher: Pearson FT Press

Summary

Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you’ve identified it, and then how to successfully model that data. You’ll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today’s #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it’s easy to find for those who want it (and easy to skip for those who don’t).

Author Biography

THOMAS W. MILLER (Evanston, IL), faculty director of Northwestern University’s Predictive Analytics program, has designed and taught courses in predictive analytics, predictive modeling, marketing analytics, and advanced modeling. Also owner of Research Publishers LLC, he has worked with predictive models for 30+ years, and consults on retail site selection, product positioning, segmentation, and pricing. He holds a Ph.D. in psychology (psychometrics); and M.S. degrees in statistics, business, and economics. His books include Data and Text Mining: A Business Applications Approach; Research and Information Services: An Integrated Approach for Business, and Without a Tout: How to Pick a Winning Team. He previously directed the A.C. Nielsen Center for Marketing Research in the School of Business, U. of Wisconsin-Madison.

Table of Contents

Introduction

1. Brand Equity Analysis
2. Competitive Analysis
3. Customer Satisfaction, Loyalty, and Churn
4. Financial Market Analysis
5. Investment Analysis
6. Market Segmentation
7. New Product Research
8. Pricing Research
9. Product Design
10. Product Positioning
11. Recommender Systems
12. Risk Analytics
13. Sales Forecasting
14. Sales Promotion
15. Sentiment Analysis
16. Site Selection
17. Social Network Analysis
18. Target Marketing
19. Transportation Planning
20. Work Force Scheduling 

The first complete guide to predictive analysis for both managers and analysts: takes you from business challenges and cases all the way through data exploration, modeling, and R programs!