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Marketing Data Science - Modeling Techniques in Predictive Analytics with R and Python (Hardcover)
Loot Price: R1,771
Discovery Miles 17 710
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Marketing Data Science - Modeling Techniques in Predictive Analytics with R and Python (Hardcover)
Series: FT Press Analytics
Expected to ship within 9 - 15 working days
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Donate to Gift Of The Givers
Total price: R1,791
Discovery Miles: 17 910
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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.
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