Joanne Rodrigues

Product Analytics (eBook, PDF)

Applied Data Science Techniques for Actionable Consumer Insights

• Format: PDF

Produktbeschreibung

Use Product Analytics to Understand and Change Consumer Behavior at Scale

Product Analytics is a complete, hands-on guide to generating actionable business insights from customer data. Experienced data scientist and enterprise manager Joanne Rodrigues introduces practical statistical techniques for determining why things happen and how to change, at scale, what people do. She complements these with powerful social science techniques for creating better theories, designing better metrics, and driving more rapid and sustained behavior change.

This book fills the gaps that many other data science book leave behind: how to start a new data science project; how to conceptualize complex ideas; building metrics from the statistic and demographic fundamentals; projecting consumer populations and material needs for a business; and causal inference beyond simple A/B testing techniques, such as difference-in-difference, regression discontinuity, propensity score matching, and uplift modelling.

Writing for entrepreneurs, product managers, marketers, and other business analytics professionals, Rodrigues teaches through intuitive examples from both web and offline environments. Avoiding math-heavy explanations, she guides you step by step through choosing the right techniques and algorithms for each application, running analyses in the R programming language, and getting answers you can trust.

Whatever your product or service, this guide can help you create precision-targeted marketing campaigns, improve consumer satisfaction and engagement, and grow revenue and profits.

• Develop core metrics and effective KPIs for user analytics in any web product
• Truly understand statistical inference, and the differences between correlation and causation
• Build intuitive predictive models to capture user behavior in products
• Tease out causal effects from observational data using modern, quasi-experimental designs and statistical matching
• Improve response through uplift modeling and other sophisticated targeting methods
• Project business costs and product population changes via advanced demographic techniques

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Produktdetails

• Verlag: Pearson ITP
• Seitenzahl: 448
• Altersempfehlung: ab 18 Jahre
• Erscheinungstermin: 27. August 2020
• Englisch
• ISBN-13: 9780135258620
• Artikelnr.: 59599958

Autorenporträt

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 behavior by using economic and sociological theory in the context of complex mathematical models.

Inhaltsangabe

* 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 Hill’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