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Home > Business and Economics > Business and Management > Sales and marketing > Market research > Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights(Pearson Business Analytics Series)
Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights(Pearson Business Analytics Series)

Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights(Pearson Business Analytics Series)


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About the Book

This guide shows how to combine data science with social science to gain unprecedented insight into customer behaviour, so you can change it. Joanne Rodrigues 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 behaviours you want and less you don't. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behaviour, and earning business value.

Table of Contents:

  • Part I: Qualitative Methodology
  • Chapter 1: Data in Action: A Model of a Dinner Party
  • Chapter 2: Building a Theory of the Universe–The Social Universe
  • Chapter 3: The Coveted Goal Post: How to Change User Behavior
  • Part II: Basic Statistical Methods
  • Chapter 4: Distributions in User Analytics
  • Chapter 5: Retained? Metric Creation and Interpretation
  • Chapter 6: Why Are My Users Leaving? The Ins and Outs of A/B Testing
  • Part III: Predictive Methods
  • Chapter 7: Modeling the User Space: k-Means and PCA
  • Chapter 8: Predicting User Behavior: Regression, Decision Trees, and Support Vector Machines
  • Chapter 9: Forecasting Population Changes in Product: Demographic Projections
  • Part IV: Causal Inference Methods
  • Chapter 10: In Pursuit of the Experiment: Natural Experiments and the Difference-in-Difference Design
  • Chapter 11: In Pursuit of the Experiment Continued: Regression Discontinuity, Time Series Modelling, and Interrupted Time Series Approaches
  • Chapter 12: Developing Heuristics in Practice: Statistical Matching and Hil's Causality Conditions
  • Chapter 13: Uplift Modeling
  • Part V: Basic, Predictive, and Causal Inference Methods in R
  • Chapter 14: Metrics in R
  • Chapter 15: A/B Testing, Predictive Modeling, and Population Projection in R
  • Chapter 16: Regression Discontinuity, Matching, and Uplift in R
  • Conclusion


About the Author :
Joanne Rodrigues is an experienced data scientist with master's degrees in mathematics, political science, and demography. She has six years of experience in statistical computing and R programming, as well as experience with Python for data science applications. Her management experience at enterprise companies leverages her ability to understand human behaviour by using economic and sociological theory in the context of complex mathematical models.


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Product Details
  • ISBN-13: 9780135258521
  • Publisher: Pearson Education (US)
  • Publisher Imprint: Addison Wesley
  • Height: 229 mm
  • No of Pages: 448
  • Series Title: Pearson Business Analytics Series
  • Sub Title: Applied Data Science Techniques for Actionable Consumer Insights
  • Width: 175 mm
  • ISBN-10: 0135258529
  • Publisher Date: 07 Jan 2021
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Spine Width: 25 mm
  • Weight: 752 gr


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