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Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
PDF Download Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)
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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:
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.
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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