Free PDF Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
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Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
Free PDF Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
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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
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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.
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Product details
Series: FT Press Analytics
Hardcover: 480 pages
Publisher: Pearson FT Press; 1 edition (May 22, 2015)
Language: English
ISBN-10: 0133886557
ISBN-13: 978-0133886559
Product Dimensions:
7.4 x 1.3 x 9.4 inches
Shipping Weight: 2 pounds (View shipping rates and policies)
Average Customer Review:
3.9 out of 5 stars
24 customer reviews
Amazon Best Sellers Rank:
#407,247 in Books (See Top 100 in Books)
The organization of the book is confusing. You can't use the table of contents to decide what to read. After reading the first 4 chapters, I'm extremely disappointed.
Very good book, well written, and the best pas, as with all of Miller's books that I have purchased, is that it comes with real code examples in both Python and R. Great way to get up and running.
good book, but....no data sets to work with. Seems critical for a source code heavy book (ie almost every chapter has pages of code). We would prefer not to scan, then try to run the code ourselves. Read the appendix first at that seems to be where the theory is then go back to the chapters for practical work. Borrowed this book from the library....its really expensive otherwiseupdate: OK --kept reading, and paying overdue fines at the library, so I bought the book. Really worth the read if you're serious about focusing on marketing data science. Great starting read for technical marketers who want to do something in this field.Glad the code samples are now available. Will have to test and see. One thing I noticed about the content. Every time something got interesting Prof. Miller would quote a reference for further reading (ie. details). That's sort of OK, but leaves me wanting and having to go dig elsewhere. Suggestion: one more paragraph for such situations would put my curiosity at rest. A lot of content around product development, positioning, recommending, but a little light on broader examples - It might be helpful to describe a broader range of techniques (ie. list them), then drill down on one or two. It just seems too narrow, like drinking from a straw when really a funnel is needed with the huge alternatives. Enjoyed the book (looks like a text book but reads like a novel - that's a good thing)
If you want to have just one book on Marketing Data Science, this is the one.
Updated my rating from 2 to 5 stars as the code has become available on FTPress. I received an email from the publisher last week. Not sure why it took over 6 months for them to post this.
Good book, very well explained examples (the R and the Python codes are very well written) but if you have read other books from Prof. Miller, you would be able to remember some exacts paragraphs across some books.
Now that the data is available I will go through this book and do a proper review. But in general I do not like this book as much as this one R for Marketing Research and Analytics (Use R!)
This is a difficult book to review, and I struggled with it a bit. On one hand, it is well written with good use of hypothetical and relevant examples (.e.g Amazon, AT&T). On the other hand, it reads like a programming class - lectures and all, which can be dense and difficult to glean information from - not exactly the rapid fire approach many data scientists I work with/am use (caveat: I'm in life sciences).Pros:-Wealth of information - book is dense-Covers topics based on marketing not programming approaches (e.g. Recommending Products with approaches rather than Building Network Diagrams with marketing examples of how to use this technique)-Uses my two favorite languages - R & Python - very common and can be applied to modeling, charting and analytics more readily than other languages - they work well together - Python for building interfaces and specific R packages for doing the deep statistical/data crunching & visualization/presentation (at least that is how I use them)-Plenty of example code that can be readily used - sample data described in text available for download-I like that there is a list of Tables in the front of the book - makes it easy to rapidly find the right examplesCons:-This book is difficult to go through - you need to be comfortable with both R and Python-Book also assumes familiarity with common statistical/analytical approachesBottom line: as this book does not cover fundamentals of any of the core subjects (marketing, Python, R, Predictive Analytics) my gut is that to approach this topic you would be better served learning first predictive analytics and marketing concepts prior to this book being of full utility. That said it is incredibly informative and I found it a fascinating.
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