Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python

999 Pages·2015·26.91 MB·English

by  Thomas W. Miller [Thomas W. Miller]

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Marketing Data Science
Modeling Techniques in Predictive
Analytics with R and Python
THOMAS W. MILLER
Publisher: Paul Boger
Editor-in-Chief: Amy Neidlinger
Executive Editor: Jeanne Glasser Levine
Operations Specialist: Jodi Kemper
Cover Designer: Alan Clements
Managing Editor: Kristy Hart
Manufacturing Buyer: Dan Uhrig
©2015 by Thomas W. Miller
Published by Pearson Education, Inc.
Old Tappan New Jersey 07675
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Company and product names mentioned herein are the
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All rights reserved. No part of this book may be reproduced, in
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the publisher.
Printed in the United States of America
First Printing May 2015
ISBN-10: 0-13-388655-7
ISBN-13: 978-0-13-388655-9
Pearson Education LTD.
Pearson Education Australia PTY, Limited.
Pearson Education Singapore, Pte. Ltd.
Pearson Education Asia, Ltd.
Pearson Education Canada, Ltd.
Pearson Educación de Mexico, S.A. de C.V.
Pearson Education—Japan
Pearson Education Malaysia, Pte. Ltd.
Library of Congress Control Number: 2015937911
Contents
Preface
Figures
Tables
Exhibits
1 Understanding Markets
2 Predicting Consumer Choice
3 Targeting Current Customers
4 Finding New Customers
5 Retaining Customers
6 Positioning Products
7 Developing New Products
8 Promoting Products
9 Recommending Products
10 Assessing Brands and Prices
11 Utilizing Social Networks
12 Watching Competitors
13 Predicting Sales
14 Redefining Marketing Research
A Data Science Methods
A.1 Database Systems and Data Preparation
A.2 Classical and Bayesian Statistics
A.3 Regression and Classification
A.4 Data Mining and Machine Learning
A.5 Data Visualization
A.6 Text and Sentiment Analysis
A.7 Time Series and Market Response Models
B Marketing Data Sources
B.1 Measurement Theory
B.2 Levels of Measurement
B.3 Sampling
B.4 Marketing Databases
B.5 World Wide Web
B.6 Social Media
B.7 Surveys
B.8 Experiments
B.9 Interviews
B.10 Focus Groups
B.11 Field Research
C Case Studies
C.1 AT&T Choice Study
C.2 Anonymous Microsoft Web Data
C.3 Bank Marketing Study
C.4 Boston Housing Study
C.5 Computer Choice Study
C.6 DriveTime Sedans
C.7 Lydia E. Pinkham Medicine Company
C.8 Procter & Gamble Laundry Soaps
C.9 Return of the Bobbleheads
C.10 Studenmund’s Restaurants
C.11 Sydney Transportation Study
C.12 ToutBay Begins Again
C.13 Two Month’s Salary
C.14 Wisconsin Dells
C.15 Wisconsin Lottery Sales
C.16 Wikipedia Votes
D Code and Utilities
Bibliography
Index
Preface
“Everybody loses the thing that made them. It’s even how it’s
supposed to be in nature. The brave men stay and watch it
happen, they don’t run.”
—QUVENZHANÉ WALLIS AS HUSHPUPPY IN Beasts of the
Southern Wild (2012)
Writers of marketing textbooks of the past would promote “the
marketing concept,” saying that marketing is not sales or
selling. Rather, marketing is a matter of understanding and
meeting consumer needs. They would distinguish between
“marketing research,” a business discipline, and “market
research,” as in economics. And marketing research would
sometimes be described as “marketing science” or “marketing
engineering.”
Ignore the academic pride and posturing of the past. Forget the
linguistic arguments. Marketing and sales, marketing and
markets, research and science—they are one. In a world
transformed by information technology and instant
communication, data rule the day.
Data science is the new statistics, a blending of modeling
techniques, information technology, and business savvy. Data
science is also the new look of marketing research.
In introducing marketing data science, we choose to present
research about consumers, markets, and marketing as it
currently exists. Research today means gathering and
analyzing data from web surfing, crawling, scraping, online