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Product Analytics

Applied Data Science Techniques for Actionable Consumer Insights

Paperback Engels 2021 9780135258521
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Samenvatting

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) Build better theories and metrics, and drive more of the behaviors you want Model, understand, and alter customer behavior to increase revenue and retention Construct better frameworks for examining why your customers do what they do Develop core metrics for user analytics, and conduct more effective A/B tests Master key techniques that most books ignore, including statistical matching and uplift modeling  Use R and this book’s many R examples to implement these techniques yourself Use data science and social science to generate real changes in customer behavior Build better theories and metrics, and drive more of the behaviors you want Model, understand, and alter customer behavior to increase revenue and retention Construct better frameworks for examining why your customers do what they do Develop core metrics for user analytics, and conduct more effective A/B tests Master key techniques that most books ignore, including statistical matching and uplift modeling  Use R and this book’s many R examples to implement these techniques yourself

Specificaties

ISBN13:9780135258521
Taal:Engels
Bindwijze:Paperback
Hoofdrubriek:

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Inhoudsopgave

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

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