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Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights
Contributor(s): Rodrigues, Joanne (Author)

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 ISBN: 0135258529     ISBN-13: 9780135258521
Publisher: Addison-Wesley Professional
OUR PRICE: $47.49  

Binding Type: Paperback
Published: September 2020
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Additional Information
BISAC Categories:
- Computers | Databases - Data Mining
- Computers | Databases - Data Warehousing
- Computers | Programming Languages - Sql
Series: Addison-Wesley Data & Analytics
Physical Information: 1.1" H x 6.9" W x 9.1" L (1.55 lbs) 448 pages
 
Descriptions, Reviews, Etc.
Publisher Description:
This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don't.
Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You'll learn how to:
  • Develop complex, testable theories for understanding individual and social behavior in web products
  • Think like a social scientist and contextualize individual behavior in today's social environments
  • Build more effective metrics and KPIs for any web product or system
  • Conduct more informative and actionable A/B tests
  • Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
  • Alter user behavior in a complex web product
  • Understand how relevant human behaviors develop, and the prerequisites for changing them
  • Choose the right statistical techniques for common tasks such as multistate and uplift modeling
  • Use advanced statistical techniques to model multidimensional systems
  • Do all of this in R (with sample code available in a separate code manual)
 
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