Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python PDF
Preview Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python
About This eBook ePUB is an open, industry-standard format for eBooks. However, support of ePUB and its many features varies across reading devices and applications. Use your device or app settings to customize the presentation to your liking. Settings that you can customize often include font, font size, single or double column, landscape or portrait mode, and figures that you can click or tap to enlarge. For additional information about the settings and features on your reading device or app, visit the device manufacturer’s Web site. Many titles include programming code or configuration examples. To optimize the presentation of these elements, view the eBook in single-column, landscape mode and adjust the font size to the smallest setting. In addition to presenting code and configurations in the reflowable text format, we have included images of the code that mimic the presentation found in the print book; therefore, where the reflowable format may compromise the presentation of the code listing, you will see a “Click here to view code image” link. Click the link to view the print-fidelity code image. To return to the previous page viewed, click the Back button on your device or app. 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 For information about buying this title in bulk quantities, or for special sales opportunities (which may include electronic versions; custom cover designs; and content particular to your business, training goals, marketing focus, or branding interests), please contact our corporate sales department at [email protected] or (800) 382-3419. For government sales inquiries, please contact [email protected]. For questions about sales outside the U.S., please contact [email protected]. Company and product names mentioned herein are the trademarks or registered trademarks of their respective owners. All rights reserved. No part of this book may be reproduced, in any form or by any means, without permission in writing from 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
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