Marketing data science thomas w miller

Marketing data science thomas w miller Marketing data science thomas w miller Marketing data science thomas w miller Marketing data science thomas w miller Marketing data science thomas w miller Marketing data science thomas w miller Khoa học dữ liệu Marketing

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 corpsales@pearsoned.com or (800) 382-3419 For government sales inquiries, please contact governmentsales@pearsoned.com For questions about sales outside the U.S., please contact international@pearsoned.com 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 Understanding Markets Predicting Consumer Choice Targeting Current Customers Finding New Customers Retaining Customers Positioning Products Developing New Products Promoting Products 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 surveys, focus groups, blogs and social media Research today means finding answers as quickly and cheaply as possible Finding answers efficiently does not mean we must abandon notions of scientific research, sampling, or probabilistic inference We take care while designing marketing measures, fitting models, describing research findings, and recommending actions to management There are times, of course, when we must engage in primary research We construct survey instruments and interview guides We collect data from consumer samples and focus groups This is traditional marketing research—custom research, tailored to the needs of each individual client or research question The best way to learn about marketing data science is to work through examples This book provides a ready resource and reference guide for modeling techniques We show programmers how to build on a foundation of code that works to solve real business problems The truth about what we is in the programs we write The code is there for everyone to see and for some to debug To promote student learning, programs include step-by-step comments and suggestions for taking analyses further Data sets and computer programs are available from the website for the Modeling Techniques series at http://www.ftpress.com/miller/ When working on problems in marketing data science, some things are more easily accomplished with Python, others with R And there are times when it is good to offer solutions in both languages, checking one against the other Together, Python and R make a strong combination for doing data science Most of the data in this book come from public domain sources Supporting data for many cases come from the University of California–Irvine Machine Learning Repository and the Stanford Large Network Dataset Collection I am most thankful to those who provide access to rich data sets for research I have learned from my consulting work with Research Publishers LLC and its ToutBay division, which promotes what can be called “data science as a service.” Academic research and models can take us only so far Eventually, to make a difference, we need to implement our ideas and models, sharing them with one another Many have influenced my intellectual development over the years There were those good thinkers and good people, teachers and mentors for whom I will be forever grateful Sadly, no longer with us are Gerald Hahn Hinkle in philosophy and Allan Lake Rice in languages at Ursinus College, and Herbert Feigl in philosophy at the University of Minnesota I am also most thankful to David J Weiss in psychometrics at the University of Minnesota and Kelly Eakin in economics, formerly at the University of Oregon Thanks to Michael L Rothschild, Neal M Ford, Peter R Dickson, and Janet Christopher who provided invaluable support during our years together at the University of Wisconsin–Madison While serving as director of the A C Nielsen Center for Marketing Research, I met the captains of the marketing research industry, including Arthur C Nielsen, Jr himself I met and interviewed Jack Honomichl, the industry’s historian, and I met with Gil Churchill, first author of what has long been regarded as a key textbook in marketing research I learned about traditional marketing research at the A C Nielsen Center for Marketing Research, and I am most grateful for the experience of working with its students and executive advisory board members Thanks go as well to Jeff Walkowski and Neli Esipova who worked with me in exploring online surveys and focus groups when those methods were just starting to be used in marketing research After my tenure with the University of Wisconsin–Madison, I built a consulting practice My company, Research Publishers LLC, was co-located with the former Chamberlain Research Consultants Sharon Chamberlain gave me a home base and place to practice the craft of marketing research It was there that initial concepts for this book emerged: What could be more important to a business than understanding its customers, competitors, and markets? Managers need a coherent view of things With consumer research, product management, competitive intelligence, customer support, and management information systems housed within separate departments, managers struggle to find the information they need Integration of research and information functions makes more sense (Miller 2008) My current home is the Northwestern University School of Professional Studies I support courses in three graduate programs: Master of Science in Predictive Analytics, Advanced Certificate in Data Science, and Master of Arts in Sports Administration Courses in marketing analytics, database systems and data preparation, web and network data science, and data visualization provide inspiration for this book I expect Northwestern’s graduate programs to prosper as they forge into new areas, including analytics entrepreneurship and sports analytics Thanks to colleagues and staff who administer these exceptional graduate programs, and thanks to the many students and fellow faculty from whom I have learned Amy Hendrickson of TEXnology Inc applied her craft, making words, tables, and figures look beautiful in print—another victory for open source Lorena Martin reviewed the book and provided much needed feedback Roy Sanford provided advice on statistical explanations Candice Bradley served dual roles as a reviewer and copyeditor for all books in the Modeling Techniques series I am grateful for their guidance and encouragement Thanks go to my editor, Jeanne Glasser Levine, and publisher, Pearson/FT Press, for making this and other books in the Modeling Techniques series possible Any writing issues, errors, or items of unfinished business, of course, are my responsibility alone My good friend Brittney and her daughter Janiya keep me company when time permits And my son Daniel is there for me in good times and bad, a friend for life My greatest debt is to them because they believe in me Thomas W Miller Glendale, California April 2015 ... further Data sets and computer programs are available from the website for the Modeling Techniques series at http://www.ftpress.com /miller/ When working on problems in marketing data science, ... Certificate in Data Science, and Master of Arts in Sports Administration Courses in marketing analytics, database systems and data preparation, web and network data science, and data visualization... list(main_effects_model_fit.params[begin:end]) new_part_worth.append((-1) * sum(new_part_worth)) part_worth_range.append(max(new_part_worth) - min(new_part_worth)) part_worth.append(new_part_worth) # end set to begin next iteration