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Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.
Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.
Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes:
- The role of analytics in delivering effective messages on the web
- Understanding the web by understanding its hidden structures
- Being recognized on the web – and watching your own competitors
- Visualizing networks and understanding communities within them
- Measuring sentiment and making recommendations
- Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics
Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.
Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.
- Sales Rank: #642991 in Books
- Published on: 2015-05-22
- Original language: English
- Number of items: 1
- Dimensions: 9.40" h x 1.40" w x 7.20" l, .0 pounds
- Binding: Hardcover
- 480 pages
From the Back Cover
To solve real marketing problems with predictive analytics, you need to master concepts, theory, skills, and tools.
Now, one authoritative guide covers them all.
Marketing Data Science brings together the knowledge you need to model consumer and buyer preferences and predict marketplace behavior, so you can make informed business decisions. Using hands-on examples built with R, Python, and publicly available data sets, Thomas W. Miller shows how to solve a wide array of marketing problems with predictive analytics.
Building on the pioneering data science program at Northwestern University, Miller covers analytics for segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.
Miller brings together essential concepts, principles, and skills that were formerly scattered across multiple texts. You’ll gain realistic experience extending predictive analytics with powerful techniques from web analytics, network science, programming, and marketing research. As you practice, you’ll master data management and modeling skills you can apply in all markets, business-to-consumer and business-to-business alike.
All data sets, extensive R and Python code, and additional examples are available for download at www.ftpress.com/miller/.
In a world transformed by information and communication technology, marketing, sales, and research have merged--and data rule them all. Today, marketers must master a new data science and use it to uncover meaningful answers rapidly and inexpensively.
This book teaches marketing data science through real-world examples that integrate essential knowledge from the disciplines that have shaped it. Building on his pioneering courses at Northwestern University, Thomas W. Miller walks you through the entire process of modeling and answering marketing questions with R and Python, today’s leading open source tools for data science.
Using real data sets, Miller covers a full spectrum of marketing applications, from targeting new customers to improving retention, setting prices to quantifying brand equity.
Marketing professionals can use Marketing Data Science as a ready resource and reference for any project. For programmers, it offers an extensive foundation of working code for solving real problems--with step-by-step comments and expert guidance for taking your analysis even further.
ADDRESS IMPORTANT MARKETING PROBLEMS:
- Reveal hidden drivers of consumer choice
- Target likely purchasers
- Strengthen retention
- Position products to exploit marketplace gaps
- Evaluate promotions
- Build recommender systems
- Assess response to brand and price
- Model the diffusion of innovation
- Analyze consumer sentiment
- Build competitive intelligence
- Choose new retail locations
- Develop an efficient and rigorous marketing research program, drawing on a wide range of data sources, internal and external
About the Author
Thomas W. Miller is faculty director of the Predictive Analytics program at Northwestern University. He has designed courses for the program, including Marketing Analytics, Advanced Modeling Techniques, Data Visualization, Web and Network Data Science, and the capstone course. He has taught extensively in the program and works with more than forty other faculty members in delivering training in predictive analytics and data science.
Miller is owner of Research Publishers LLC and its ToutBay Division, a publisher and distributor of data science applications. He has consulted widely in the areas of retail site selection, product positioning, segmentation, and pricing in competitive markets and has worked with predictive models for more than 30 years.
Miller’s books include Web and Network Data Science, Modeling Techniques in Predictive Analytics, Data and Text Mining: A Business Applications Approach, Research and Information Services: An Integrated Approach for Business, and a book about predictive modeling in sports, Without a Tout: How to Pick a Winning Team.
Before entering academia, Miller spent nearly 15 years in business IT in the computer and transportation industries. He also directed the A. C. Nielsen Center for Marketing Research and taught market research and business strategy at the University of Wisconsin-Madison. He holds a Ph.D. in psychology (psychometrics) and a master’s degree in statistics from the University of Minnesota and an MBA and master’s degree in economics from the University of Oregon.
Most helpful customer reviews
3 of 4 people found the following review helpful.
Well Presented Text of Marketing Analytics and Business Data Science With Programming Examples in R and Python
By Ira Laefsky
An excellent overview and software implementation of marketing analysis techniques by a team of individuals with wide experience in both the academic and consulting/corporate environments. Also the first Data Science book of several in my possession to reflect the main tools used in
real data science applications and contrasting them in many cases side by side.. The code is excellent and well-commented although I would
have appreciated a greater discussion of the tools and methods in Python and R as they are used in the examples and in conditioning as well as
analyzing data for commercial and academic applications. I also would have appreciated more of a discussion of how practitioners should choose between R and Python for various categories of practical applications. The bibliography is extensive and up-to-date.
I highly recommend this book for the business or data science student, marketing practioner or consumer of data science particularly in the marketing and business arena/
5 of 6 people found the following review helpful.
Good information, but poorly organized and presented
By Mainiac
There is a lot of good information in this book, but I find it poorly organized and presented.
It's hard to figure out the best audience for this book, and I think when you imagine it as a textbook in a specific degree program, where the instructor would know exactly the prerequisite classes taken by the students coming into the class, then it starts to make sense. Ideas toward the front of the book are not properly introduced and code is thrown at the reader without much in the way of explanation. In the context of a class, the instructor could smooth out these rough edges and likely deliver a coherent educational experience. But as a standalone learning experience, this is rough going.
(I'm sure it's relevant what my own background is, which is completely comfortable with predictive analytics and programming, but ignorant of the specific way that "marketing" folks look at the world.)
I find the book very strangely organized in that Appendix A, "Data Science Methods", is, well, relegated to an appendix, rather than being a central part of the flow of the book. It's a really good chapter!
There is some tremendously useful code presented in this book, but again, the way that it's presented will make it of limited utility to those who do not have a guide in the form of a professor teaching a class or who do not have sufficient background to ramp up to the ideas presented.
I am also not fond of the production qualities of the book, e.g:
1. The paper is overly thin, so you can see the text on the other side of the page, which just makes reading harder to my mind.
2. The code is presented with this horrible gray background. Black text on a gray background (I dunno, maybe 30% gray) is not good contrast. So the code ends up being unnecessarily difficult to read.
3. That this was created using LaTeX is obvious to me. Not because the typesetting is problematic, but because of the front matter, with the separate listings of Figures, Tables, and Exhibits. LaTeX makes producing these listings easy, but just because you can produce them doesn't mean you should. They feel anachronistic and strange.
The fact that all the code and underlying data is available on the web is amazing, so for some folks, the difficult presentation will be worth slogging through to see the examples. But I fear that many would-be readers will just give up because the path is so rough.
2 of 3 people found the following review helpful.
Dig deep and add value, a real gold-mine here
By John H.
Valuable for both marketing professionals and programmers. The goal of informed business decisions is a priceless target in my life and work, and data science is here to help. My favorites, and the most impressive chapters for me, are Predicting Consumer Choice, Finding New Customers, and Retaining Customers, because these add value that is hard to come by. For those with a talent or interest in math and programming as well, this should give you loads of inspiration and I could foresee some really great ventures, ideas, and entrepreneurship coming from the stimulus of this book. The real-world examples, citations, charts, and graphs are excellent. This is obviously not an "average person" or popular consumer-level "pop salesman" book, but much deeper than that. The detailed programming examples are superb and thorough, such as "Analysis for a Field Test of Laundry Soaps" -- to give you an idea of this level of expertise. The Appendices are fantastic, with lots of case studies, code, and data sources. Highly recommended.
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